Malware RSS Feed
All the statistics used in this report were obtained using Kaspersky Security Network (KSN), a distributed antivirus network that works with various anti-malware protection components. The data was collected from KSN users who agreed to provide it. Millions of Kaspersky Lab product users from 213 countries and territories worldwide participate in this global exchange of information about malicious activity.Q3 figures
- According to KSN data, Kaspersky Lab solutions detected and repelled 171,802,109 malicious attacks from online resources located in 190 countries all over the world.
- 45,169,524 unique URLs were recognized as malicious by web antivirus components.
- Kaspersky Lab’s web antivirus detected 12,657,673 unique malicious objects: scripts, exploits, executable files, etc.
- Attempted infections by malware that aims to steal money via online access to bank accounts were registered on 1,198,264 user computers.
- Crypto ransomware attacks were blocked on 821,865 computers of unique users.
- Kaspersky Lab’s file antivirus detected a total of 116,469,744 unique malicious and potentially unwanted objects.
- Kaspersky Lab mobile security products detected:
- 1,520,931 malicious installation packages;
- 30,167 mobile banker Trojans (installation packages);
- 37,150 mobile ransomware Trojans (installation packages).
One of the most significant events of the third quarter was the release of Pokémon GO. Of course, cybercriminals could not ignore such a popular new product and tried to exploit the game for their own purposes. This was primarily done by adding malicious code to the original app and spreading malicious versions via third-party stores. This method was used, for example, to spread Trojan-Banker.AndroidOS.Tordow, which exploits vulnerabilities in the system to obtain root access to a device. With root access, this Trojan protects itself from being deleted, and it can also steal saved passwords from browsers.
But perhaps the most notable case of Pokémon GO’s popularity being used to infect mobile devices involved fraudsters publishing a guide for the game in the official Google Play store. The app turned out to be an advertising Trojan capable of gaining root access to a device by exploiting vulnerabilities in the system.
We later came across two more modifications of this Trojan, which were added to Google Play under the guise of different apps. According to Google Play data, one of them, imitating an equalizer, was installed between 100,000 and 500,000 times.
Trojan.AndroidOS.Ztorg.ad in the official Google Play store
Interestingly, one of the methods used by the cybercriminals to promote the Trojan was a company that pays users for the installation of advertising apps.
Screenshot of the app that prompts the user to install the Trojan for 5 cents
According to this company’s rules, it doesn’t work with users whose devices have root access. The users may be looking to earn some money, but they end up with an infected device and don’t actually receive any money, because after infection the device gains root access.Ad with a Trojan
The most popular mobile Trojan in the third quarter of 2016 was Trojan-Banker.AndroidOS.Svpeng.q. During the quarter, the number of users attacked by it grew almost eightfold.
Over 97% of users attacked by Svpeng were located in Russia. The attackers managed to make the Trojan so popular by advertising it via Google AdSense – one of the most popular advertising networks on the Russian Internet. Many popular sites use it to display targeted advertising. Anyone can pay to register their ad on the network, and that was exactly what the attackers did.
Along with the advert, however, they added the AdSense Trojan. When a user visited the page with the advert, Svpeng was downloaded to their device.Bypassing protection mechanisms in Android 6
In our report for the second quarter of 2016 we mentioned the Trojan-Banker.AndroidOS.Asacub family that can bypass several system controls. Of special note this quarter is the Trojan-Banker.AndroidOS.Gugi family that has learned to bypass the security mechanisms introduced in Android 6 by tricking the user. The Trojan first requests rights to overlay other applications, and then uses those rights to trick the user into giving it privileges to work with text messages and to make calls.Trojan ransomware in the Google Play store
In the third quarter, we registered the propagation of Trojan-Ransom.AndroidOS.Pletor.d, a mobile ransomware program, via Google Play. The Trojan imitated an app for servicing devices, including deleting unnecessary data, speeding up device performance and even antivirus protection.
Trojan-Ransom.AndroidOS.Pletor.d in Google Play
The Trojan checks which country the device is located in, and if it is not Russia or Ukraine, it requests administrator rights and calls the command server. Earlier versions of this Trojan encrypted user data, but this modification doesn’t possess such functionality. Instead, the Trojan blocks operation of the device by opening a window that covers all other open windows and demanding a ransom to unblock it.Mobile threat statistics
In Q3 2016, Kaspersky Lab detected 1,520,931 malicious installation packages, which is 2.3 times fewer than in the previous quarter.
Number of detected malicious installation packages (Q4 2015 – Q1 2016)Distribution of mobile malware by type
Distribution of new mobile malware by type (Q2 2016 and Q3 2016)
In Q3 2016, RiskTool software, or legitimate applications that are potentially dangerous to users, topped the rating of malicious objects detected for mobile devices. Their share continued to grow from 45.1% in Q2 to 55.8% this quarter.
Due to the large number of RiskTool programs and the considerable increase in their overall share of the total flow of detected objects, the proportion of almost all other types of malicious programs decreased, even where the actual number of detected programs increased compared to the previous quarter.
The most affected was Trojan-Ransom – its share decreased from 5.72% to 2.37%. This was caused by a decline in activity by the Trojan-Ransom.AndroidOS.Fusob family (covered in more detail below).
At the same time, we registered a slight growth in the share of Trojan-Bankers – from 1.88% to 1.98%.TOP 20 mobile malware programs
Please note that this rating of malicious programs does not include potentially dangerous or unwanted programs such as RiskTool or adware.Name % of attacked users* 1 DangerousObject.Multi.Generic 78,46 2 Trojan-Banker.AndroidOS.Svpeng.q 11,45 3 Trojan.AndroidOS.Ztorg.t 8,03 4 Backdoor.AndroidOS.Ztorg.c 7,24 5 Backdoor.AndroidOS.Ztorg.a 6,55 6 Trojan-Dropper.AndroidOS.Agent.dm 4,91 7 Trojan.AndroidOS.Hiddad.v 4,55 8 Trojan.AndroidOS.Agent.gm 4,25 9 Trojan-Dropper.AndroidOS.Agent.cv 3,67 10 Trojan.AndroidOS.Ztorg.aa 3,61 11 Trojan-Banker.AndroidOS.Svpeng.r 3,44 12 Trojan.AndroidOS.Ztorg.pac 3,31 13 Trojan.AndroidOS.Iop.c 3,27 14 Trojan.AndroidOS.Muetan.b 3,17 15 Trojan.AndroidOS.Vdloader.a 3,14 16 Trojan-Dropper.AndroidOS.Triada.s 2,80 17 Trojan.AndroidOS.Muetan.a 2,77 18 Trojan.AndroidOS.Triada.pac 2,75 19 Trojan-Dropper.AndroidOS.Triada.d 2,73 20 Trojan.AndroidOS.Agent.eb 2,63
* Percentage of unique users attacked by the malware in question, relative to all users of Kaspersky Lab’s mobile security product that were attacked.
First place is occupied by DangerousObject.Multi.Generic (78.46%), the verdict used for malicious programs detected using cloud technologies. Cloud technologies work when the antivirus database contains neither the signatures nor heuristics to detect a malicious program, but the cloud of the antivirus company already contains information about the object. This is basically how the very latest malware is detected.
In Q3 2016, 17 Trojans that use advertising as their main means of monetization (highlighted in blue in the table) made it into the TOP 20. Their goal is to deliver as many adverts as possible to the user, employing various methods, including the installation of new adware. These Trojans may use superuser privileges to conceal themselves in the system application folder, from which it will be very difficult to delete them.
In Q3 2016, attempted infections by financial #malware were registered at 1.2m users’ computers #KLreport #bankingTweet
With root access on the device, Trojans can do many different things without the user being aware, such as installing apps from Google Play, including paid apps.
It’s worth noting that the Trojans from the Ztorg family, which occupied four places in the TOP 20, are often distributed via the official Google Play store. Since the end of 2015, we have registered more than 10 such cases (including a fake guide for Pokemon GO). Several times the Trojan notched up over 100,000 installations, and on one occasion it was installed more than 500,000 times.
Trojan.AndroidOS.Ztorg.ad masquerading as a guide for Pokemon GO in Google Play
The ranking also included two representatives of the Trojan-Banker.AndroidOS.Svpeng mobile banker family. As we mentioned above, Svpeng.q became the most popular malware in the third quarter of 2016. This was down to the Trojan being distributed via the AdSense advertising network, which is used by a large number of sites on the Russian segment of the Internet.The geography of mobile threats
The geography of attempted mobile malware infections in Q3 2016 (percentage of all users attacked)
TOP 10 countries attacked by mobile malware (ranked by percentage of users attacked)Country* % of users attacked ** 1 Bangladesh 35,57 2 Nepal 31.54 3 Iran 31.38 4 China 26.95 5 Pakistan 26.83 6 Indonesia 26.33 7 India 24,35 8 Nigeria 22.88 9 Algeria 21,82 10 The Philippines 21.67
* We eliminated countries from this rating where the number of users of Kaspersky Lab’s mobile security product is relatively low (under 10,000).
** Percentage of unique users attacked in each country relative to all users of Kaspersky Lab’s mobile security product in the country.
Bangladesh topped the rating, with almost 36% of users there encountering a mobile threat at least once during the quarter. China, which came first in this rating two quarters in a row, dropped to fourth place.
The most popular mobile malware in all the countries of this rating (except China) was the same – advertising Trojans that mostly belonged to the Ztorg, Iop, Hiddad and Triada families. A significant proportion of attacks in China also involved advertising Trojans, but the majority of users there encountered Trojans from the Backdoor.AndroidOS.GinMaster and Backdoor.AndroidOS.Fakengry families.
Russia (12.1%) came 24th in this rating, France (6.7%) 52nd, the US (5.3%) 63rd, Italy (5.1%) 65th, Germany (4.9%) 68th, and the United Kingdom (4.7%) 71st.
The situation in Germany and Italy has improved significantly: in the previous quarter, 8.5% and 6.2% of users in those countries respectively were attacked. This was due to a decline in activity by the Fusob family of mobile ransomware.
The safest countries were Austria (3.3%), Croatia (3.1%) and Japan (1.7%).Mobile banking Trojans
Over the reporting period, we detected 30,167 installation packages for mobile banking Trojans, which is 1.1 times as many as in Q2.
Number of installation packages for mobile banking Trojans detected by Kaspersky Lab solutions
(Q4 2015 – Q3 2016)
Trojan-Banker.AndroidOS.Svpeng became the most popular mobile banking Trojan in Q3 due to its active distribution via the advertising network AdSense. More than half the users that encountered mobile banking Trojans in the third quarter faced Trojan-Banker.AndroidOS.Svpeng.q. It was constantly increasing the rate at which it spread – in September the number of users attacked by the Trojan was almost eight times greater than in June.
The number of unique users attacked by the Trojan-Banker.AndroidOS.Svpeng banking Trojan family
Over 97% of attacked users were in Russia. This family of mobile banking Trojans uses phishing windows to steal credit card data and logins and passwords from online banking accounts. In addition, fraudsters steal money via SMS services, including mobile banking.
Geography of mobile banking threats in Q3 2016 (percentage of all users attacked)
TOP 10 countries attacked by mobile banker Trojans (ranked by percentage of users attacked)Country* % of users attacked** 1 Russia 3.12 2 Australia 1.42 3 Ukraine 0.95 4 Uzbekistan 0.60 5 Tajikistan 0.56 6 Kazakhstan 0.51 7 China 0.49 8 Latvia 0.47 9 Russia 0.41 10 Belarus 0.37
* We eliminated countries from this rating where the number of users of Kaspersky Lab’s mobile security product is relatively low (under 10,000).
** Percentage of unique users in each country attacked by mobile banker Trojans, relative to all users of Kaspersky Lab’s mobile security product in the country.
In Q3 2016, first place was occupied by Russia (3.12%) where the proportion of users that encountered mobile banker Trojans almost doubled from the previous quarter.
In second place again was Australia (1.42%), where the Trojan-Banker.AndroidOS.Acecard and Trojan-Banker.AndroidOS.Marcher families were the most popular threats.
The most widely distributed mobile banking Trojans in Q3 were representatives of the Svpeng, Faketoken, Regon, Asacub, Gugi and Grapereh families. In particular, the third quarter saw the Trojan-Banker.AndroidOS.Gugi family learn how to bypass protection mechanisms in Android by tricking users.Mobile Ransomware
In Q3 2016, we detected 37,150 mobile Trojan-Ransomware installation packages.
Number of mobile Trojan-Ransomware installation packages detected by Kaspersky Lab
(Q4 2015 – Q3 2016)
The sharp rise in the number of mobile Trojan-Ransomware installation packages in Q1 and Q2 of 2016 was caused by the active proliferation of the Trojan-Ransom.AndroidOS.Fusob family of Trojans. In the first quarter of 2016, this family accounted for 96% of users attacked by mobile ransomware; in Q2 it accounted for 85%. Its share in Q3 was 73%.
Number of users attacked by the Trojan-Ransom.AndroidOS.Fusob family, January-September 2016
The highest number of users attacked by the mobile Trojan-Ransomware family was registered in March 2016. Since then the amount of attacked users has been decreasing, especially in Germany.
Despite this, Trojan-Ransom.AndroidOS.Fusob.h remained the most popular mobile Trojan-Ransomware in the third quarter, accounting for nearly 53% of users attacked by mobile ransomware. Once run, the Trojan requests administrator privileges, collects information about the device, including GPS coordinates and call history, and downloads the data to a malicious server. After that, it may receive a command to block the device.
Geography of mobile Trojan-Ransomware in Q3 2016 (percentage of all users attacked)
TOP 10 countries attacked by mobile Trojan-Ransomware (ranked by percentage of users attacked)Country* % of users attacked ** 1 Canada 0.95 2 USA 0.94 3 Kazakhstan 0.71 4 Germany 0.63 5 UK 0.61 6 Mexico 0.58 7 Australia 0.57 8 Spain 0,54 9 Italy 0.53 10 Switzerland 0.51
* We eliminated countries from this ranking where the number of users of Kaspersky Lab’s mobile security product is relatively low (under 10,000).
** Percentage of unique users in each country attacked by mobile Trojan-Ransomware, relative to all users of Kaspersky Lab’s mobile security product in the country.
In all the TOP 10 countries apart from Kazakhstan, the most popular Trojan-Ransom family was Fusob. In the US, the Trojan-Ransom.AndroidOS.Svpeng family was also popular. This Trojan family emerged in 2014 as a modification of the Trojan-Banker.AndroidOS.Svpeng family. These Trojans demand a ransom of $100-$500 from victims to unblock their devices.
In Q3 2016, #crypto #ransomware attacks were blocked on 821,865 unique computers #KLreportTweet
In Kazakhstan, the main threat to users originated from representatives of the Small mobile Trojan-Ransom family. This is a fairly simple ransomware program that blocks the operation of a device by overlaying all the windows with its own and demanding $10 to remove it.Vulnerable apps exploited by cybercriminals
In Q3 2016, the Neutrino exploit kit departed the cybercriminal market, following in the wake of Angler and Nuclear which also left the market in the previous quarter.
RIG and Magnitude remain active. RIG was especially prominent – it has quickly filled the vacant niche on the exploit kit market.
This is the overall picture for the use of exploits this quarter:
Distribution of exploits used in attacks by the type of application attacked, Q3 2016
Exploits for different browsers and their components (45%) once again topped the rating, although their share decreased by 3 percentage points. They are followed by exploits for Android OS vulnerabilities (19%), whose share fell 5 p.p. in the third quarter. Exploits kits for Microsoft Office rounded off the top three. Their contribution actually saw an increase from 14% to 16% in Q3.
Exploits for Adobe Flash Player remained popular. In fact, their share more than doubled from 6% to 13%. This was caused by the aforementioned RIG exploit kit: its use in several campaigns saw the share of SWF exploits increase dramatically.Online threats (Web-based attacks)
The statistics in this section were derived from web antivirus components that protect users from attempts to download malicious objects from a malicious/infected website. Malicious websites are created deliberately by malicious users; infected sites include those with user-contributed content (such as forums), as well as compromised legitimate resources.
In the third quarter of 2016, Kaspersky Lab’s web antivirus detected 12,657,673 unique malicious objects (scripts, exploits, executable files, etc.) and 45,169,524 unique URLs were recognized as malicious by web antivirus components. Kaspersky Lab solutions detected and repelled 171,802,109 malicious attacks from online resources located in 190 countries all over the world.Online threats in the banking sector
These statistics are based on detection verdicts of Kaspersky Lab products, received from users of Kaspersky Lab products who have consented to provide their statistical data.
Kaspersky Lab solutions blocked attempts to launch malware capable of stealing money via online banking on 1,198,264 computers in Q3 2016. The number of users attacked by financial malware increased by 5.8% from the previous quarter (1,132,031).
The third quarter is traditionally holiday season for many users of online banking services in Europe, which means the number of online payments made by these users increases during this period. This inevitably sees an increase in financial risks.
Number of users attacked by financial malware, Q3 2016
In Q3, the activity of financial threats grew month on month.Geography of attacks
To evaluate and compare the risk of being infected by banking Trojans worldwide, we calculate the percentage of Kaspersky Lab product users in the country who encountered this type of threat during the reporting period, relative to all users of our products in that country.
Geography of banking malware attacks in Q3 2016 (percentage of attacked users)
TOP 10 countries by percentage of attacked usersCountry* % of attacked users** 1 Russia 4.20 2 Sri Lanka 3.48 3 Brazil 2.86 4 Turkey 2.77 5 Cambodia 2.59 6 Ukraine 1.90 7 Venezuela 1.90 8 Vietnam 1.86 9 Argentina 1.86 10 Uzbekistan 1.77
These statistics are based on detection verdicts returned by the antivirus module, received from users of Kaspersky Lab products who have consented to provide their statistical data.
* We excluded those countries in which the number of Kaspersky Lab product users is relatively small (under 10,000).
** Unique users whose computers have been targeted by banking Trojan attacks as a percentage of all unique users of Kaspersky Lab products in the country.
In the third quarter of 2016, Russia had the highest proportion of users attacked by banking Trojans. Representatives of the Trojan-Banker ZeuS (Zbot) family, which leads the way in terms of the number of attacked users worldwide, were especially active in Russia. This is unsurprising since Russian cybercriminals are allegedly behind the development of this malware. They know the specifics of Russia’s online banking systems as well as the mentality of Russian users and take them into consideration when developing their malware. In Russia, the Gozi banking Trojan continues to proliferate. It displayed a burst of activity in the previous quarter after its developers joined forces with the creators of the Nymaim Trojan. Russia also topped the TOP 10 countries with the highest proportion of users attacked by mobile bankers.
Sri Lanka, a favorite destination with tourists, was a newcomer to the rating, going straight in at second. Financial threats were encountered by 3.48% of users in the country. Among them are likely to be foreigners who arrived in the country on holiday and used online banking services to make payments. The most active representatives of banking malware in the region were those from the Fsysna banker family. This family has previously been noted for attacks targeting customers of Latin American banks.
In Q3 2016, @kaspersky #mobile security products detected 1.5m malicious installation packages #KLreportTweet
Brazil rounds off the top three for the second quarter in a row. In Q2, we forecast a surge of financial threat activity in Latin America and specifically in Brazil because of this summer’s Olympic Games. However, the increase in the proportion of users attacked in Brazil was negligible: in the third quarter, 2.86% of users in Brazil encountered financial threats compared to 2.63% in Q2. At the same time, users in Argentina were subjected to a surge in malicious attacks, and as a result, the country ranked ninth.
The holiday season affected almost all countries in the TOP 10. In Russia, Ukraine and Uzbekistan, people traditionally have vacations at this time of the year, while other countries (Sri Lanka, Brazil, Turkey, Cambodia, etc.) are considered popular tourist destinations. Tourists tend to be active users of online banking systems, which in turn attracts cybercriminals and their banking malware.
The share of banking Trojan victims in Italy was 0.60%, in Spain it was 0.61%, while in Germany and the UAE the figures were 1.21% and 1.14% respectively.The TOP 10 banking malware families
The table below shows the TOP 10 malware families used in Q3 2016 to attack online banking users (as a percentage of users attacked):Name* % of attacked users** 1 Trojan-Spy.Win32.Zbot 34.58 2 Trojan.Win32.Qhost/Trojan.BAT.Qhost 9.48 3 Trojan.Win32.Fsysna 9.467 4 Trojan-Banker.Win32.Gozi 8.98 5 Trojan.Win32.Nymaim 8.32 6 Trojan-Banker.Win32.Shiotob 5.29 7 Trojan-Banker.Win32.ChePro 3.77 8 Trojan-Banker.Win32.BestaFera 3.31 9 Trojan-Banker.Win32.Banbra 2.79 10 Trojan.Win32.Neurevt 1.79
* The detection verdicts of Kaspersky Lab products, received from users of Kaspersky Lab products who have consented to provide their statistical data.
** Unique users whose computers have been targeted by the malware in question as a percentage of all users attacked by financial malware.
The undisputed leader of the rating is Trojan-Spy.Win32.Zbot. Its source codes have been publicly available since a leak and are now widely exploited as an easy-to-use tool for stealing user payment data. Unsurprisingly, this malware consistently tops this rating – cybercriminals regularly enhance the family with new modifications compiled on the basis of the source code and containing minor differences from the original.
The family of Qhost Trojans (verdicts Trojan.Win32.Qhost and Trojan.BAT.Qhost) came second. The functionality of this family’s malicious programs is relatively simple: the Trojan modifies the content of the Host file (a special text file that contains a database of domain names that are used when transmitting to the network addresses of nodes) and as soon as specific resources are visited, the Trojan’s malicious components are loaded to an infected workstation and used to steal payment information. The Trojan adds a number of records to the Host file preventing the user’s browser from connecting to web-based apps and resources of popular antivirus vendors.
The Q3 rating also includes a new malware representative that has already demonstrated its capabilities in Sri Lanka – the Trojan.Win32.Fsysna family of banking Trojans. Members of this family, in addition to stealing payment data from infected workstations, are also used by cybercriminals to distribute spam. The Trojan uses an infected machine to redirect spam messages from the command center to a mail server. Some representatives of this family also possess Trojan cryptor functionality. Fsysna is kind of a ‘Swiss army knife’ used by cybercriminals to steal money.
Q3 2016 saw a decline in the activity of the notorious financial threat Trojan-Spy.Win32.Lurk: the number of users attacked by this malware fell by 7.1%. Lurk was not included in the TOP 10 banking malware families, but it still poses a threat to users of online banking systems. The cybercriminal group behind this financial threat has been arrested (something we wrote about in a separate article), so we expect to see a further decrease in activity by this banking Trojan next quarter.Ransomware Trojans
Cryptors are currently one of the biggest threats to users and companies. These malicious programs are becoming more and more popular in the cybercriminal world because they are capable of generating large profits for their owners.
A total of 21 new cryptor families and 32,091 new modifications were detected in Q3. We also added several existing cryptor families to our virus collection.
The number of new cryptor families added to our virus collection is slightly less than in the second quarter (25), but the number of newly created modifications increased 3.5 times compared to the previous quarter.
The number of newly created cryptor modifications, Q1 – Q3 2016
Malware writers are constantly trying to improve their creations. New ways to infect computers are always being sought, especially for attacks on companies, which cybercriminals see as far more profitable than attacks on standard users.Remote launching of cryptors by cybercriminals
We are increasingly seeing incidents where cybercriminals crack passwords to gain remote access to a victim’s system (usually an organization) and infect a compromised machine with Trojan ransomware. Examples of this in Q3 were Dcryptor and Xpan.Dcryptor/Mamba
Trojan-Ransom.Win32.Dcryptor is known on the Internet under the pseudonym ‘Mamba’. Infection is carried out manually. The fraudsters brute-force the passwords for remote access to the victim machine and run the Trojan, passing on the password for encryption as a command line argument.
During infection, the Trojan uses the legitimate DiskCryptor utility. As a result, it’s not just individual files on network drives that are infected but entire hard drive sectors on the local machine. System boot is blocked: once the computer is started, a message appears on the screen demanding a ransom and displaying an email address for communicating with the attackers.
This Trojan reminds us of the notorious Petya/Mischa Trojan and continues the growing trend of cybercriminals looking for new ways to block access to data.Xpan/TeamXRat ransomware
Trojan-Ransom.Win32.Xpan is yet another example of ransomware that is launched after attackers remotely penetrate a system. This Trojan is distributed by Brazilian cybercriminals. They brute-force the RDP password (the standard protocol for remote access to Windows computers) and infect the compromised system using the Xpan Trojan that encrypts files and displays a ransom demand.Ransomware in scripting languages
Another trend that has attracted our attention is the growing number of cryptors written in scripting languages. In the third quarter of 2016, we came across several new families written in Python:
- HolyCrypt (Trojan-Ransom.Python.Holy)
- CryPy (Trojan-Ransom.Python.Kpyna)
Another example that emerged in June was Stampado (Trojan-Ransom.Win32.Stampa) written in AutoIt, the automation language.The number of users attacked by ransomware
In Q3 2016, 821,865 unique KSN users were attacked by cryptors – that is 2.6 times more than the previous quarter.
Number of unique users attacked by Trojan-Ransom cryptor malware (Q3 2016)
Geography of Trojan-Ransomattacks in Q3 2016 (percentage of attacked users)
Top 10 countries attacked by cryptorsCountry* % of users attacked by cryptors** 1 Japan 4.83 2 Croatia 3.71 3 Korea 3.36 4 Tunisia 3.22 5 Bulgaria 3.20 6 Hong Kong 3.14 7 Taiwan 3.03 8 Argentina 2.65 9 Maldives 2.63 10 Australia 2.56
* We excluded those countries where the number of Kaspersky Lab product users is relatively small (under 10,000).
** Unique users whose computers have been targeted by ransomware as a percentage of all unique users of Kaspersky Lab products in the country.
As in the previous quarter, Japan topped this rating.
Newcomers to this Top 10 were Tunisia, Hong Kong, Argentina, and Australia, with Italy, Djibouti, Luxembourg, and the Netherlands all making way.Top 10 most widespread cryptor families Name Verdict* % of attacked users** 1 CTB-Locker Trojan-Ransom.Win32.Onion/ Trojan-Ransom.NSIS.Onion 28.34 2 Locky Trojan-Ransom.Win32.Locky 9.60 3 CryptXXX Trojan-Ransom.Win32.CryptXXX 8.95 4 TeslaCrypt Trojan-Ransom.Win32.Bitman 1.44 5 Shade Trojan-Ransom.Win32.Shade 1.10 6 Cryakl Trojan-Ransom.Win32.Cryakl 0.82 7 Cryrar/ACCDFISA Trojan-Ransom.Win32.Cryrar 0.73 8 Cerber Trojan-Ransom.Win32.Zerber 0.59 9 CryptoWall Trojan-Ransom.Win32.Cryptodef 0.58 10 Crysis Trojan-Ransom.Win32.Crusis 0.51
* These statistics are based on detection verdicts received from users of Kaspersky Lab products who have consented to provide their statistical data.
** Unique users whose computers have been targeted by a specific Trojan-Ransom family as a percentage of all users of Kaspersky Lab products attacked by Trojan-Ransom malware.
CTB-Locker once again occupied first place in the Q3. The top three also included the now infamous Locky and CryptXXX. Despite the fact that the owners of TeslaCrypt disabled their servers and posted a master key to decrypt files back in May 2016, it continues to make it into our rating (although its contribution dropped by 5.8 times in Q3)Crysis
Crysis (verdict Trojan-Ransom.Win32.Crusis) was a newcomer to the TOP 10 in Q3. This Trojan was first detected in February 2016 and since then has undergone several code modifications.
Interestingly, the list of email addresses used for ransom demands by the distributors of Crysis partly matches the list associated with the Cryakl and Aura Trojans. Analysis of the executable files from these families, however, shows that they do not share the same code. It appears that these malicious programs are spread via a partner scheme, and because some distributors are distributing several different Trojans simultaneously they are using the same email address to communicate their ransom demands to the victims.Polyglot/MarsJoke
This Trojan appeared in August 2016 (we recently published a detailed analysis of Polyglot/ MarsJoke). It is not included in the TOP 10, but it does have one interesting feature: the authors have tried to imitate the well-known CTB-Locker, which tops the rating for the second quarter in a row. Both the external and internal design of this piece of malware is very similar to the “original”, but the cybercriminals made a mistake that allows files to be decrypted without paying a ransom.Top 10 countries where online resources are seeded with malware
The following statistics are based on the physical location of the online resources used in attacks and blocked by our antivirus components (web pages containing redirects to exploits, sites containing exploits and other malware, botnet command centers, etc.). Any unique host could be the source of one or more web attacks.
In order to determine the geographical source of web-based attacks, domain names are matched against their actual domain IP addresses, and then the geographical location of a specific IP address (GEOIP) is established.
In Q3 2016, Kaspersky Lab solutions blocked 171,802,109 attacks launched from web resources located in 190 countries around the world. 45,169,524 unique URLs were recognized as malicious by web antivirus components.
83% of notifications about blocked web attacks were triggered by attacks coming from web resources located in 10 countries.
Distribution of web attack sources by country, Q3 2016
The US (33.51%) remained top of this rating in Q3. Russia (9%) dropped from second to fourth, while Germany came second with a share of 10.5%. Canada left the Top 10, with Cyprus a newcomer in ninth place (1.24%).Countries where users faced the greatest risk of online infection
In order to assess the risk of online infection faced by users in different countries, we calculated the percentage of Kaspersky Lab users in each country who encountered detection verdicts on their machines during the quarter. The resulting data provides an indication of the aggressiveness of the environment in which computers work in different countries.
In Q3 2016, 30,167 #mobile #banking Trojans were detected by @kaspersky mobile security products #KLreportTweet
Please note that starting this quarter, this rating only includes attacks by malicious programs that fall under the Malware class. The rating does not include web antivirus module detections of potentially dangerous or unwanted programs such as RiskTool or adware.Country* % of users attacked ** 1 Slovenia 30.02 2 Bulgaria 29.49 3 Armenia 29.30 4 Italy 29.21 5 Ukraine 28.18 6 Spain 28.15 7 Brazil 27.83 8 Belarus 27.06 9 Algeria 26.95 10 Qatar 26.42 11 Greece 26.10 12 Portugal 26.08 13 Russia 25.87 14 France 25.44 15 Kazakhstan 25.26 16 Azerbaijan 25.05 17 United Arab Emirates 24.97 18 Vietnam 24.73 19 China 24.19 20 Albania 23.23
These statistics are based on detection verdicts returned by the web antivirus module, received from users of Kaspersky Lab products who have consented to provide their statistical data.
* These calculations excluded countries where the number of Kaspersky Lab users is relatively small (under 10,000 users).
** Unique users whose computers have been targeted by Malware-class attacks as a percentage of all unique users of Kaspersky Lab products in the country.
On average, 20.2% of computers connected to the Internet globally were subjected to at least one Malware-class web attack during the quarter.
Geography of malicious web attacks in Q3 2016 (ranked by percentage of users attacked)
The countries with the safest online surfing environments included Croatia (14.21%), the UK (14.19%), Singapore (13.78%), the US (13.45%), Norway (13.07%), Czech Republic (12.80%), South Africa (11.98%), Sweden (10.96%), Korea (10.61%), the Netherlands (9.95%), Japan (9.78%).Local threats
Local infection statistics for user computers are a very important indicator: they reflect threats that have penetrated computer systems by infecting files or removable media, or initially got on the computer in an encrypted format (for example, programs integrated in complex installers, encrypted files, etc.).
Data in this section is based on analyzing statistics produced by antivirus scans of files on the hard drive at the moment they were created or accessed, and the results of scanning removable storage media.
In Q3 2016, Kaspersky Lab’s file antivirus detected 116,469,744 unique malicious and potentially unwanted objects.Countries where users faced the highest risk of local infection
For each country, we calculated the percentage of Kaspersky Lab product users on whose computers the file antivirus was triggered during the quarter. These statistics reflect the level of personal computer infection in different countries.
In Q3 2016, @kaspersky #mobile security products detected 37,150 mobile #ransomware Trojans #KLreportTweet
Please note that starting this quarter, the rating of malicious programs only includes Malware-class attacks. The rating does not include web antivirus module detections of potentially dangerous or unwanted programs such as RiskTool or adware.Country* % of users attacked** 1 Vietnam 52.07 2 Afghanistan 52.00 3 Yemen 51.32 4 Somalia 50.78 5 Ethiopia 50.50 6 Uzbekistan 50.15 7 Rwanda 50,14 8 Laos 49.27 9 Venezuela 49.27 10 Philippines 47.69 11 Nepal 47.01 12 Djibouti 46.49 13 Burundi 46,17 14 Syria 45.97 15 Bangladesh 45.48 16 Cambodia 44.51 17 Indonesia 43.31 18 Tajikistan 43,01 19 Mozambique 42.98 20 Myanmar 42.85
These statistics are based on detection verdicts returned by on-access and on-demand antivirus modules, received from users of Kaspersky Lab products who have consented to provide their statistical data. The data include detections of malicious programs located on users’ computers or on removable media connected to the computers, such as flash drives, camera and phone memory cards, or external hard drives.
* These calculations exclude countries where the number of Kaspersky Lab users is relatively small (under 10,000 users).
** The percentage of unique users in the country with computers that blocked Malware-class local threats as a percentage of all unique users of Kaspersky Lab products.
An average of 22.9% of computers globally faced at least one Malware-class local threat during the third quarter.
The safest countries in terms of local infection risks were: Spain (14.68%), Singapore (13.86%), Italy (13.30%), Finland (10.94%), Norway (10.86%), France (10.81%), Australia ( 10.77%), Czech Republic (9.89%), Croatia (9.70%), Ireland (9.62%), Germany (9.16%), the UK (9.09%), Canada (8.92%), Sweden (8.32%), the USA (8.08%), Denmark (6.53%), and Japan (6.53%).
Targeted attack campaigns don’t need to be technically advanced in order to be successful. In July 2016 we reported on a group called Dropping Elephant (also known as ‘Chinastrats’ and ‘Patchwork’). Using a combination of social engineering, old exploit code and some PowerShell-based malware this group was able to steal sensitive data from its victims.
This group, which has been active since November 2015, targets high profile diplomatic and economic organizations linked to China’s foreign relations – an interest that is evident from the themes the attackers use to trap their victims.
The attackers use a combination of spear-phishing e-mails and watering-hole attacks. The first involves sending a document with remote content. When the victim opens the document, a ping request is sent to the attackers’ Command-and-Control (C2) server. The victim then receives a second spear-phishing e-mail, containing either a Word document or a PowerPoint file (these exploit old vulnerabilities – CVE-2012-0158 and CVE-2014-6352 respectively). Once the payload has been executed, a UPX-packed AutoIT executable is dropped on to the system: once executed, this downloads further components from the C2 server and the theft of data from the victim’s computer begins.
In Q3 2016, @kaspersky repelled 172m malicious attacks via online resources located in 191 countries #KLreport #InfosecTweet
The attackers also created a watering-hole website that downloads genuine news articles from legitimate websites. If a visitor wants to view the whole article, they are prompted to download a PowerPoint file: this reveals the rest of the document, but also asks the victim to download a malicious object. The attackers sometimes e-mail links to their watering-hole website. In addition, they maintain Google+, Facebook and Twitter accounts, to develop relevant search engine optimization (SEO) and to reach out to wider targets.
The success of the Dropping Elephant group is striking given that no zero-day exploits or advanced techniques were used to target high-profile victims – it’s clear that by applying security updates and improving the security awareness of staff, the success of attacks like this can be prevented. At the start of the year we predicted that APT groups would invest less effort in developing sophisticated tools and make greater use of off-the-shelf malware. Dropping Elephant provides a further example of how low investment and use of ready-made toolsets can be very effective when combined with high quality social engineering.ProjectSauron
In September, our Anti-Targeted Attack Platform flagged an anomaly in the network of a customer’s organization. Further investigation led us to uncover ProjectSauron, a group that has been stealing confidential data from organizations in Russia, Iran and Rwanda – and probably other countries – since June 2011. We have identified more than 30 victims: the target organizations all play a key role in providing state services and come from government, military, scientific research, telecommunications and financial sectors.
ProjectSauron is particularly focused on obtaining access to encrypted communications, hunting for them using an advanced, modular cyber-espionage platform that incorporates a set of unique tools and techniques. The cost, complexity, persistence and the ultimate goal of the operation (i.e. stealing secret data from state-related organizations) suggest that ProjectSauron is a state-sponsored campaign. ProjectSauron gives the impression of an experienced threat group that has made a considerable effort to learn from other highly advanced attacks, including Duqu, Flame, Equation and Regin – adopting some of their most innovative techniques and improving on their tactics in order to remain undiscovered.
One of the most noteworthy features of ProjectSauron is the deliberate avoidance of patterns: the implants used by the group are customized for each victim and are never re-used. This makes the use of traditional Indicators of Compromise (IoC) almost useless. This approach, along with the use of multiple routes for the exfiltration of stolen data (such as legitimate e-mail channels and DNS) enables ProjectSauron to conduct well-hidden, long-term spying campaigns in targeted networks.
Key features of ProjectSauron:
- core implants that are unique for each victim;
- use of legitimate software update scripts;
- use of backdoors that download new modules or run commands in memory only;
- focus on information relating to custom network encryption software;
- use of low-level tools orchestrated by high-level LUA scripts (the use of LUA is very rare – previously seen only in Flame and Animal Farm attacks;
- use of specially prepared USB drives to jump across air-gapped networks, with hidden compartments for storing stolen data;
- use of multiple exfiltration mechanisms to conceal transfer of data in day-to-day traffic.
The method used to initially infect victims remains unknown.
The single use of unique methods, such as control server, encryption keys and more, in addition to the adoption of cutting-edge techniques from other major threats groups, is new. The only effective way to withstand such threats is to deploy multiple layers of security, with sensors to monitor for even the slightest anomaly in organizational workflow, combined with threat intelligence and forensic analysis. You can find further discussion of the methods available to deal with such threats here.ShadowBrokers
In August, a person or group going under the name ‘ShadowBrokers’ claimed to possess files belonging to the Equation group. They provided links to two PGP encrypted archives. They provided the password to the first for free, but ‘auctioned’ the second, setting the price at 1 million BTC (1/15th of the bitcoins in circulation).
Having uncovered the Equation group in February 2015, we were interested in examining the first archive. It contains almost 300MB of firewall exploits, tools and scripts, under cryptonyms such as BANANAUSURPER, BLATSTING and BUZZDIRECTION. Most of the files are at least three years old, with change entries pointing to August 2013 and the newest time-stamp dating to October 2013.
The Equation group makes extensive use of RC5 and RC6 encryption algorithms (these algorithms were designed by Ronald Rivest in 1994 and 1998 respectively). The free trove provided by ShadowBrokers includes 347 different instances of RC5 and RC6 implementations. The implementation is functionally identical with that found in the Equation malware – and has not been seen elsewhere.
The code similarity makes us believe with a high degree of confidence that the tools from the ShadowBrokers leak are related to the malware from the Equation group.Operation Ghoul
In June, we noticed a wave of spear-phishing e-mails with malicious attachments. The messages, sent mainly to top and middle level managers of numerous companies, appeared to be coming from a bank in the UAE. The messages claimed to offer payment advice from the bank and included an attached SWIFT document. But the archive really contained malware. Further investigation revealed that the June attacks were the most recent operation of a group that researchers had been tracking for more than a year, named Operation Ghoul by Kaspersky Lab.
The group successfully attacked more than 130 organizations from 30 countries, including Spain, Pakistan, UAE, India, Egypt, the United Kingdom, Germany and Saudi Arabia. Based on information obtained from the sink-hole of some C2 servers, the majority of the target organizations work in the industrial and engineering sectors. Others include shipping, pharmaceutical, manufacturing, trading and educational organizations.
The malware used by the Operation Ghoul group is based on the commercial spyware kit Hawkeye, sold openly on the Dark Web. Once installed, the malware collects interesting data from the victim’s computer, including keystrokes, clipboard data, FTP server credentials, account data from browsers, messaging clients, e-mail clients and information about installed applications. This data is sent to the group’s C2 servers.
The aim of the campaign seems to be financial profit – all the targeted organizations hold valuable data that can be sold on the black market.
The continued success of social engineering as a way of gaining a foothold in target organizations highlights the need for businesses to make staff awareness and education a central component of their security strategy.Malware stories Lurk
In June 2016 we reported on the Lurk banking Trojan, used to systematically siphon money from the accounts of commercial organizations in Russia – among them, a number of banks. The police estimate the losses caused by this Trojan at around $45 million.
During our research into this Trojan, it became apparent that victims of Lurk had also installed the remote administration software, Ammyy Admin. While we didn’t give it much thought at first, it became apparent that the official Ammyy Admin website had been compromised and was being used by the Lurk gang as part of a watering-hole attack: the Trojan was downloaded to victim’s computers along with the legitimate software.
The dropper on the Ammyy Admin site started distributing a different Trojan on 1 June 2016, ‘Trojan-PSW.Win32.Fareit’: this was the day that the alleged creators of the Lurk Trojan were arrested. It seems that those responsible for the Ammyy Admin website breach were happy to sell their Trojan dropper to anyone who wanted to distribute malware from the compromised site.
The banking Trojan wasn’t the only cybercriminal activity the Lurk group was involved in. The group also developed the Angler exploit kit, a set of malicious programs designed to exploit vulnerabilities in widespread software to install malware. This exploit kit was originally developed to provide a reliable and effective delivery channel for the group’s malware. However, in 2013 the group started to rent out the kit to anyone who was willing to pay for it – probably to help pay for the group’s huge network infrastructure and large number of ‘staff’. The Angler exploit kit became one of the most powerful tools available on the criminal underground. Unlike the Lurk banking Trojan, which focused on victims in Russia, Angler has been used by attackers across the world – including the groups behind the CryptXXX and TeslaCrypt ransomware and the Neverquest banking Trojan (the latter was used against almost 100 banks). The operations of Angler were disrupted after the arrest of the alleged members of the Lurk group.
In Q3 2016, 45.2M unique malicious URLs were recognized by @kaspersky web antivirus components #KLreport #ITTweet
The group was involved in other side activities too. For more than five years, the group moved from developing very powerful malware for automated money theft with Remote Banking Services software, to sophisticated theft involving SIM-card swap fraud, to becoming hacking specialists familiar with the internal infrastructure of banks.
Kaspersky Lab provided assistance to the Russian police in the investigation into the group behind the Lurk Trojan. The arrests marked the culmination of a six-year investigation by our Computer Incidents Investigation Team. You can read about the investigation here.Ransomware
Hardly a month goes by without reports of ransomware attacks in the media: for example, a recent report suggested that 28 NHS trusts in the UK have fallen victim to ransomware in the last 12 months. Most ransomware attacks are directed at consumers, but a significant proportion target businesses (around 13 per cent in 2015-16). The Kaspersky Lab IT Security Risks Survey 2016 indicated that around 42 per cent of small and medium businesses became victims of ransomware in the 12 months up to August 2016.
One recent ransomware campaign demanded a massive two bitcoins (around $1,300) as a ransom. The ransomware program, named Ded Cryptor, changes the wallpaper on the victim’s computer to a picture of an evil-looking Santa Claus.
The modus operandi of this program (i.e. encrypted files, scary image, and ransom demand) is unremarkable, but the pre-history of this attack is interesting. It is based on the EDA2 open-source ransomware code, developed by Utku Sen as part of a failed experiment. Utku Sen, a security expert from Turkey, created a ransomware program and published the code online. He realized that cybercriminals would use the code to create their own cryptors, but hoped that this would help security researchers to understand how cybercriminals think and code, thereby making their own efforts to block ransomware more effective.
Ded Cryptor was just one of many ransomware programs spawned by EDA2. Another such program that we saw recently was Fantom. This was interesting not just because of its connection to EDA2, but because it simulates a genuine-looking Windows update screen
This is displayed while Fantom is encrypting the victim’s files in the background. The fake update program runs in full-screen mode, visually blocking access to other programs and distracting the victim from what’s really happening. Once the encryption has been completed, Fantom displays a more typical message.
There’s no doubt that public awareness of the problem is growing, but it’s clear that consumers and organizations alike are not doing enough to combat the threat; and cybercriminals are capitalising on this – this is clearly reflected in the growing number of ransomware attacks.
It’s important to reduce your exposure to ransomware (and we’ve outlined important steps you can take here and here). However, there’s no such thing as 100 per cent security, so it’s also important to mitigate the risk. In particular, it’s vital to ensure that you have a backup, to avoid facing a situation where the only choices are to pay the cybercriminals or lose your data. It’s never advisable to pay the ransom.
In Q3 2016, @kaspersky web #antivirus detected 12,657,673 unique malicious objects #KLreport #netsecTweet
If you do find yourself in a situation where your files are encrypted and you don’t have a backup, ask your anti-malware vendor if they can help and check the No More Ransom website, to see if it holds the keys to decrypt your data. This is a joint initiative by the National High Tech Crime Unit of the Netherlands’ police, Europol’s European Cybercrime Centre, Kaspersky Lab and Intel Security – designed to help victims of ransomware retrieve their encrypted data without paying cybercriminals.
In a recent ‘ask the expert‘ session, Jornt van der Wiel, an expert from Kaspersky Lab’s Global Research and Analysis Team, provided useful insights into ransomware.Data breaches
Personal information is a valuable commodity, so it’s no surprise that cybercriminals target online providers, looking for ways to bulk-steal data in a single attack. We’ve become accustomed to the steady stream of security breaches reported in the media. This quarter has been no exception, with data leaks from the official forum of DotA 2, Yahoo and others.
Some of these attacks resulted in the theft of huge amounts of data, highlighting the fact that many companies are failing to take adequate steps to defend themselves. Any organization that holds personal data has a duty of care to secure it effectively. This includes hashing and salting customer passwords and encrypting other sensitive data.
Consumers can limit the damage of a security breach at an online provider by ensuring that they choose passwords that are unique and complex: an ideal password is at least 15 characters long and consists of a mixture of letters, numbers and symbols from the entire keyboard. As an alternative, people can use a password manager application to handle all this for them automatically.
It’s also a good idea to use two-factor authentication, where an online provider offers this feature – requiring customers to enter a code generated by a hardware token, or one sent to a mobile device, in order to access a site, or at least in order to make changes to account settings.
Given the potential impact of a security breach, it’s hardly surprising to see regulatory authorities paying closer attention to the issue. The UK Information Commissioner’s Office (ICO) recently issued a record fine of £400,000 to Talk Talk for the company’s ‘failure to implement the most basic cyber security measures’, related to the attack on the company in October 2015. In the view of the ICO, the record fine ‘acts as a warning to others that cyber security is not an IT issue, it is a boardroom issue’.
The EU General Data Protection Regulation (GDPR), which comes into force in May 2018, will require companies to notify the regulator of data breaches, with significant fines for failure to secure personal data. You can find an overview of the regulation here.
We took a look back at the impact of the Ashley Madison breach, one year after the attack that led to the leak of customer data, offering some good tips to anyone who might be considering looking online for love (and good advice for managing any online account).
In the last few months the scale of the global ‘Cybercrime as a Service’ infrastructure has been revealed – fully commercialized, with DDoS as one of the most popular services capable of launching attacks the likes of which have never seen before in terms of volume and technological complexity.
Against this background, Europol published the 2016 Internet Organized Crime Threat Assessment (IOCTA) on 28 September, which is based on the experiences of law enforcement institutions within the EU member states. The report clearly ranks DDoS in first place as a key threat and that any “Internet facing entity, regardless of its purpose or business, must consider itself and its resources to be a target for cybercriminals”.
Most likely, this stems from early September when Brian Krebs, an industry security expert, published an investigation outlining the business operations of a major global DDoS botnet service called vDOS and its principal owners, two young men in Israel. The culprits have been arrested and investigations are ongoing, but the sheer scale of their business is stunning.
Based on a subscription scheme, starting from $19.99 per month, tens of thousands of customers paid more than $600,000 over the past two years to vDOS. In just four months between April and July, vDOS launched more than 277 million seconds of attack time, or approximately 8.81 years’ worth of attack traffic.
It was no wonder, that shortly afterwards a DDoS attack brought down Brian Krebs’s website with a traffic volume close to 620 Gbps, making it one of the biggest attacks the Internet has ever witnessed, only to be topped several days later by another attack close to 1 Tbps that hit France’s OVH. The attack vector, as Octava Klaba, CTO at OVH reported, looks like the same Internet of Things (IoT) botnet totaling 152,464 devices – mainly webcams, routers and thermostats – that brought down Brian Krebs’s website.
To make the situation even worse, hackers just released the Mirai source code, which, according to security experts, was responsible for the aforementioned DDoS attacks. The code includes a built-in scanner to look for vulnerable IoT devices and enrolls them into a botnet. With this, we expect to see a new wave of commercial services like vDOS and DDoS attacks in the coming months.
The Internet of Things is increasingly becoming a powerful tool for attackers, facilitated by the neglect for information security both on the part of vendors and users.
So, check that your devices connected to the Internet have a strong security setup.‘Political’ DDoS attacks
DDoS is widely used in politics. In July of this year, an international tribunal stated China’s territorial claims to the Spratly archipelago in the South China Sea were groundless, and almost immediately at least 68 sites belonging to various Philippine government institutions were subjected to powerful DDoS attacks. The international press called these incidents part of a long-term cyberespionage campaign launched by China in its struggle for sovereignty of the Spratly archipelago.Attack on a broker
Cybercriminals have identified the most vulnerable targets for DDoS extortion purposes – broker companies. They are high-turnover businesses that are also extremely dependent on web services. The Taiwanese company First Securities recently received a demand for 50 bitcoins (about $32,000) from unknown persons. After refusing to pay, the company’s website was targeted by a DDoS attack, which made bidding for the company’s clients impossible. Meanwhile, the president of First Securities released a statement to the press saying they had experienced a “trade slowdown” that only affected some of their investors.Assessing the damage caused by DDoS attacks
B2B International, at the request of Kaspersky Lab, conducted the study called IT Security Risks 2016. According to the results, corporations are suffering increasing damage from DDoS attacks: a single attack can cost a company more than $1.6 billion in losses. At the same time, 8 out of 10 companies are subjected to several attacks per year.Trend of the quarter: SSL-based DDoS attacks
According to Kaspersky DDoS Protection, the number of “smart” HTTPS-based DDoS attacks on applications increased in the third quarter of 2016. These attacks boast a number of important advantages that make a successful attack more likely.
Establishing a secure connection requires considerable resources, despite operating speeds for cryptographic algorithms constantly increasing (e.g., the elliptic curve algorithm has made it possible to enhance the performance of encryption while maintaining the persistence level). For the sake of comparison, a properly configured web server is capable of processing tens of thousands of new HTTP connections per second, but when processing encrypted connections this capacity falls to just hundreds of connections per second.
The use of hardware crypto accelerators makes it possible to significantly increase this value. However, this doesn’t help much considering the current reality of cheap and readily available rented servers, high-capacity communication channels, as well as known vulnerabilities that allow cybercriminals to build larger botnets. They can carry out a successful DDoS attack by creating a load that exceeds the performance of expensive hardware solutions.
A typical example of a “smart” attack is a relatively small number of queries being sent to the “load-heavy” parts of websites (as a rule, search forms are chosen) inside a small number of encrypted connections. Those requests are almost invisible in the overall traffic flow, and at a low intensity they are often very effective. At the same time, decryption and analysis of traffic is only possible on the web-server side.
Encryption also complicates the operation of specialized systems designed to protect against DDoS attacks (especially solutions used by communications providers). Decrypting traffic on-the-fly in order to analyze the content of network packets is often not possible during such attacks due to technical or security reasons (it’s not permitted to pass a server’s private key to third-party organizations, mathematical limitations prevent access to the information in encrypted packets in transit traffic). This significantly reduces the effectiveness of protection against such attacks.
The growing proportion of “smart” DDoS attacks is caused in no small part by the fact that amplification-type attacks, the most popular attack type in recent times, are becoming increasingly difficult to implement. On the Internet, the number of vulnerable servers that can be used to organize such attacks is steadily falling. In addition, most of these attacks have similar features, making it easy to block them completely, and ensuring their effectiveness is eroded over time.
The desire of website owners to protect data and improve privacy levels, combined with cheaper computing capacities have resulted in a growing trend: classic HTTP is being replaced by HTTPS, leading to an increase in the proportion of resources using encryption. The development of web-based technology encourages active implementation of the new HTTP/2 protocol, in which operations without encryption are not supported by the latest browsers.
We believe that the number of encryption-based attacks will grow. For developers of information security solutions this requires an immediate reappraisal of their approach to combating distributed attacks, because today’s tried and tested solutions may soon become ineffective.Statistics for botnet-assisted DDoS attacks Methodology
Kaspersky Lab has extensive experience in combating cyber threats, including DDoS attacks of various types and levels of complexity. The company’s experts monitor botnet activity with the help of the DDoS Intelligence system.
DDoS Intelligence (part of Kaspersky DDoS Protection) is designed to intercept and analyze commands sent to bots from command and control (C&C) servers, and does not have to wait until user devices are infected or cybercriminal commands are executed in order to gather data.
This report contains the DDoS Intelligence statistics for the third quarter of 2016.
In the context of this report, a single (separate) DDoS attack is defined as an incident during which any break in botnet activity lasts less than 24 hours. If the same web resource was attacked by the same botnet after a break of more than 24 hours, this is regarded as a separate DDoS attack. Attacks on the same web resource from two different botnets are also regarded as separate attacks.
The geographic distribution of DDoS victims and C&C servers is determined according to their IP addresses. In this report, the number of DDoS targets is calculated based on the number of unique IP addresses reported in the quarterly statistics.
It is important to note that DDoS Intelligence statistics are limited to those botnets detected and analyzed by Kaspersky Lab. It should also be noted that botnets are just one of the tools used to carry out DDoS attacks; therefore, the data presented in this report does not cover every DDoS attack that has occurred within the specified time period.Q3 Summary
- Resources in 67 countries (vs. 70 in Q2) were targeted by DDoS attacks in Q3 2016.
- 62.6% of targeted resources were located in China.
- China, the US and South Korea remained leaders in terms of both the number of DDoS attacks and number of targets. For the first time both rankings included Italy.
- The longest DDoS attack in Q3 2016 lasted for 184 hours (or 7.6 days) – significantly shorter than the previous quarter’s maximum (291 hours or 12.1 days).
- A popular Chinese search engine was subjected to the largest number of attacks (19) over the reporting period.
- SYN DDoS, TCP DDoS and HTTP DDoS remain the most common DDoS attack scenarios. The proportion of attacks using the SYN DDoS method continued to grow, increasing by 5 p.p., while the shares of TCP DDoS and HTTP DDoS continued to decline.
- In Q3 2016, the percentage of attacks launched from Linux botnets continued to increase and reached 78.9% of all detected attacks.
In Q3 2016, the geography of DDoS attacks narrowed to 67 countries, with China accounting for 72.6% (4.8 p.p. less than the previous quarter). In fact, 97.4% of the targeted resources were located in just 10 countries. The other two countries in the top three switched places – the US (12.8%) overtook South Korea (6.3%) to become the second most targeted country.
Distribution of DDoS attacks by country, Q2 2016 vs. Q3 2016
One entry of note to the rating of most targeted countries was Italy, appearing for the first time ever and accounting for 0.6% of all attacks. In all, the TOP 10 included three Western European countries (Italy, France and Germany).
This quarter’s statistics show that targets within the leading 10 countries accounted for 96.9% of all attacks.
Distribution of unique DDoS attack targets by country, Q2 2016 vs. Q3 2016
In Q3 2016, 62.6% of attacks (8.7 p.p. less than the previous quarter) targeted resources located in China. However, targets in the US became more attractive for cybercriminals – the country’s share accounted for 18.7% compared with 8.9% in the previous quarter. South Korea rounded off the top three – its contribution decreased by 2.4 p.p. and amounted to 8.7%.
The shares of the other countries in the TOP 10 increased, with the exception of France (0.4%), which saw a fall of 0.1 p.p. Japan (1.6%) and Italy (1.1%) each saw a 1 p.p. increase, and as a result, Italy entered the TOP 10 for the first time and went straight in at 6th place (Ukraine left the TOP 10). The proportion of attacks targeting Russia also grew significantly – from 0.8% to 1.1%.
This rating also included three Western European countries – Italy, France and the Netherlands.Changes in DDoS attack numbers
DDoS activity was relatively uneven in Q3 2016. The period between 21 July and 7 August was marked by the highest DDoS activity, with peaks in the number of attacks registered on 23 July and 3 August. From 8 August, DDoS activity plummeted and resulted in a lull which lasted from 14 August till 6 September. The smallest number of attacks was recorded on 3 September (22 attacks). The largest number of attacks was observed on 3 August – 1,746 attacks. Note that this is the highest figure for the first three quarters of 2016. Most of these attacks took place on the servers of just one service provider located in the United States.
Number of DDoS attacks over time* in Q3 2016
*DDoS attacks may last for several days. In this timeline, the same attack can be counted several times, i.e. one time for each day of its duration.
In Q3, Friday was the most active day of the week for DDoS attacks (17.3% of attacks), followed by Thursday (15.2%). Monday, which was second in Q2 with 15%, became the quietest day of the week in terms of DDoS attacks (12.6%).
Distribution of DDoS attack numbers by day of the week, Q2 and Q3 2016Types and duration of DDoS attacks
The rating of the most popular attack methods saw no considerable changes from the previous quarter. The SYN DDoS method has further strengthened its position as leader: its share increased from 76% to 81%. The proportion of the other attack types decreased slightly. ICMP DDoS was most affected: its share decreased by 2.6 p.p.
Distribution of DDoS attacks by type, Q2 and Q3 2016
Attacks that last no more than four hours remained the most popular: in Q3 their share increased by 9.2 p.p., accounting for 69%. Attacks that lasted 5-9 hours remained in second. Meanwhile, the percentage of longer attacks decreased considerably – the share of attacks lasting 100-149 hours fell from 1.7% in Q2 to 0.1% in the third quarter. There were very few cases of attacks lasting longer than that.
Distribution of DDoS attacks by duration (hours), Q2 and Q3 2016
The longest DDoS attack in Q3 2016 only lasted for 184 hours (targeting a Chinese provider), which is significantly lower than the Q2 maximum of 291 hours. A Chinese search engine had the unenviable distinction of being attacked most – it was targeted 19 times during the quarter.C&C servers and botnet types
In Q3, the highest number of C&C servers (45.8%) was detected in South Korea, although this country’s contribution is considerably smaller compared to the previous quarter (69.6%).
The top three countries hosting the most C&C servers remained unchanged – South Korea, China and the US – although their total share was 67.7% vs. 84.8% in Q2.
The number of active C&C servers in Western Europe is growing – the TOP 10 included the Netherlands (4.8%), the UK (4.4%), and France (2%). To recap, three Western European countries entered both the TOP 10 countries subjected to the highest number of attacks and the TOP 10 countries with the highest number of targets.
Among the newcomers to the C&C rating were Hong Kong and Ukraine, each with a share of 2%.
Distribution of botnet C&C servers by country in Q3 2016
In Q3, Linux-based DDoS bots remained the clear leader and the share of attacks launched from Linux botnets continued to grow, accounting for 78.9% vs. 70.8% in Q2. This correlates with the growing popularity of SYN DDoS for which Linux bots are the most appropriate tool. In addition, this can be explained by the growing popularity of Linux-based IoT devices used for DDoS attacks, and will most probably be boosted further after the leakage of Mirai.
Correlation between attacks launched from Windows and Linux botnets, Q2 and Q3 2016
Q3 continues the trend of Linux dominance from the previous quarter. Prior to Q2 2016, the difference between the share of Windows- and Linux-based botnets did not exceed 10 p.p. for several quarters in a row.
The majority of attacks – 99.8% – were carried out by bots belonging to a single family. Cybercriminals launched attacks using bots from two different families in just 0.2% of cases.Conclusion
‘Classic’ botnet attacks based on widespread malware tools such as Pandora, Drive, etc. have been well researched by analysts who have developed effective and simple methods of neutralizing attacks that utilize these tools. This is increasingly forcing cybercriminals to use more sophisticated attack methods, including data encryption and new approaches to the development of tools used for organizing attacks and building botnets.
Another interesting trend this quarter was the increased activity of DDoS botnets in Western Europe. For the first time in a year the TOP 10 most attacked countries included three Western European countries – Italy, France and Germany. This correlates with the increased number of active C&C servers in Western Europe, particularly in France, the UK and the Netherlands. Overall, Western European countries accounted for about 13% of active DDoS botnet C&C servers.
The Gootkit bot is one of those types of malicious program that rarely attracts much attention from researchers. The reason is its limited propagation and a lack of distinguishing features.
There are some early instances, including on Securelist (here and here), where Gootkit is mentioned in online malware research as a component in bots and Trojans. However, the first detailed analysis was published by researchers around two years ago. That was the first attempt to describe the bot as a standalone malicious program, where it was described as a “new multi-functional backdoor”. The authors of that piece of research put forward the assertion that the bot’s features were borrowed from other Trojans, and also provided a description of some of Gootkit’s key features.
In September 2016, we discovered a new version of Gootkit with a characteristic and instantly recognizable feature: an extra check of the environment variable ‘crackme’ in the downloader’s body. This feature was not present in the early versions. Just as interesting was the fact that we were able to gain access to the bot’s C&C server, including its complete hierarchal tree of folders and files and their contents.Infection
As was the case earlier, the bot Gootkit is written in NodeJS, and is downloaded to a victim computer via a chain of downloaders. The main purpose of the bot also remained the same – to steal banking data. The new Gootkit version, detected in September, primarily targets clients of European banks, including those in Germany, France, Italy, the Netherlands, Poland, etc.
The Trojan’s main propagation methods are spam messages with malicious attachments and websites containing exploits on infected pages (Rig Exploit Kit). The attachment in the spam messages contained Trojan-Banker.Win32.Tuhkit, the small initial downloader that launched and downloaded the main downloader from the C&C server, which in turn downloaded Gootkit.
Examples of infected pages used to spread the Trojan
While carrying out our research we detected a huge number of the initial downloader versions that were used to distribute the Trojan – most of them are detected as Trojan.Win32.Yakes. Some of the loaders were extremely odd, like the one shown below. It clearly stated in its code that is was a loader for Gootkit.
Section of code from one of the initial downloaders
Some versions of Gootkit are also able to launch the main body with administrator privileges bypassing UAC. To do so, the main loader created an SDB file and registered it in the system with the help of the sdbinst.exe utility, after which it launched the bot with elevated privileges without notifying the user.‘Crackme’ check
The new version of Gootkit is distinct in that it checks the environment variable ‘crackme’ located in the downloader body. It works as follows: the value of the variable is compared to a fixed value. If the two values differ, the bot starts to check if it has been launched in a virtual environment.
Checking the global variable in the downloader’s body
To do so, the bot checks the variable ‘trustedcomp’, just like it did in earlier versions.
Checking the bot’s body for launch in a virtual environment
The Trojan’s main body
The Trojan’s main file includes a NodeJS interpreter and scripts. After unpacking, the scripts look like this:
NodeJS scripts that make up the Trojan’s main body
The scripts shown in the screenshot constitute the main body of the Trojan. Gootkit has about a hundred various scripts, but they are mostly for practical purposes (intermediate data handlers, network communication DLLs, wrapper classes implementations, encoders etc.) and not of much interest.
The Trojan itself is distributed in an encrypted and packed form. Gootkit is encrypted with a simple XOR with a round key; unpacking is performed using standard Windows API tools. The screen below shows the first 255 bytes of the transferred data.
The Trojan’s packed body
The first three DWORDs denote the sizes of the received, unpacked and packed data respectively. One can easily check this by subtracting the third DWORD from the first DWORD, which leaves 12 bytes – i.e., the size of these variables.Stealing money
Interception of user data is done the standard way, via web injections into HTTPS traffic (examples of these web injects are shown below). After the data is sent to the C&C server, it is processed by parsers, each of which is associated with the website of a specific bank.
Fragment of parser code
Communication with the C&C
In the version of Gootkit under review, the C&C address is the same as the address from which the Trojan’s main body is downloaded; in earlier versions, these two addresses sometimes differed. While generating a request, the Trojan uses its unique User Agent – any request that does not specify a User Agent will be denied.
The unique GootKit User Agent
Communication with the C&C comes down to the exchange of a pre-defined set of commands, the main ones being:
- Request a list of files available to the Trojan (P_FS:FS_READDIR);
- Receive those files (P_FS:FS_GETFILE/FS_GET_MULTIPLEFILES);
- Receive update for the bot (P_FS: FS_GETFILE);
- Obtain screenshot (P_SPYWARE:SP_SCREENSHOT);
- Upload list of processes (P_SPYWARE:SP_PROCESSLIST);
- Terminate process (P_SPYWARE:SP_PROCESSKILL);
- Download modules (P_FS: FS_GETFILE);
- Receive web injects (P_ SPYWARE:SP_SPYWARE_CONFIG).
The bot’s main commands and sub-commands
The C&C addresses (two or three in number) are hardwired in the loader’s body and can also be saved in the registry. The body of the data packet may vary depending on the request type, but always includes the following variables:
- Size of data packet, plus eight;
- Check value XORed with a constant;
- Command type;
- Command sub-type.
In the screen below, the C&C requests registration information from the bot during its first launch.
Request from C&C, example of variables
The response in this case will contain detailed information about the infected computer, including:
- Network adapter parameters;
- CPU details, amount of RAM;
- User name, computer name.
Regardless of the request type, data is communicated between the C&C and the bot in the format protobuf.
When the main body is downloaded, the address that the loader contacts typically ends in one of the following strings:
Mystery solved…rather easily
We found a configuration error that often appears on botnet C&C servers and took advantage of it to capture a complete tree of folders and files, as well as their contents, from one of the GootKit C&C servers.
Contents of GootKit C&C server
The C&C server contains a number of parsers for different banking sites. These parsers are used (provided the user data is available) to steal money from user accounts and to send notifications via Jabber. The stolen data is used in the form of text files, with the infected computer’s IP address used as the file name.
Stolen data and logs on the bot’s C&C server
Example of stolen data in one of the text files
Other data (bank transfers and logs) is also stored in text file format.
An analysis of the bot’s web injects and parser logs has shown that the attackers primarily target the clients of German and French banks.
Distribution of web injects across domain zones
Excerpts from parser logs
Analysis of the server content and the parsers made it clear that the botnet’s creator was a Russian speaker. Note the comments in the screen below.
A fragment of script including the author’s comments in Russian
Moreover, Gootkit most probably has just one owner – it’s not for sale anywhere and, regardless of the downloaders’ modifications or type of admin panel, the code in NodeJS (the Trojan’s main body) is always the same.
Examples of Gootkit web injectsConclusions
Gootkit belongs to a class of Trojans that are extremely tenacious, albeit not very widespread. Because it’s not very common, new versions of the Trojan may remain under the researchers’ radar for long periods.
It should also be noted that the users of NodeJS as a development platform set themselves certain limitations, but simultaneously get a substantial degree of flexibility and simplicity when creating new versions of the Trojan.
Kaspersky Lab’s security products detect the Trojan GootKit and all its associated components under the following verdicts:
- Trojan-Banker.Win32.Tuhkit (the initial downloader distributed via emails);
- Trojan.Win32.Yakes (some modifications of the main downloader);
- HEUR:Trojan.Win32.Generic (the bot’s main body, some modifications of the downloader).
It’s unusual for a day to go by without finding some new variant of a known ransomware, or, what is even more interesting, a completely new one. Unlike the previously reported and now decrypted Xpan ransomware, this same-but-different threat from Brazil has recently been spotted in the wild. This time the infection vector is not a targeted remote desktop intrusion, but a more massively propagated malicious campaign relying on traditional spam email.
Since the infection is not done manually by the bad guys, their malware has a higher chance of being detected and we believe that is one of the reasons for them to have added one more level of protection to the code, resorting to a binary dropper to launch the malicious payload.
Given that this particular ransomware is fairly well known by now, instead of opting for the usual branding and marketing efforts in which most ransomware authors invest time, this group has decided to choose an unnamed campaign, showing only an email address for technical support and a bitcoin address for making the payment. It has become a kind of urban legend that if you can’t find something on Google, then it doesn’t exist.
Not very long ago, we saw the birth of truly autochthonous Brazilian ransomware, without much technical sophistication and mainly based on an open-source project. While there’s a long road ahead for local bad guys to achieve the level of the key players on the ransomware scene, this particular family is interesting to study since there have been versions in English, Italian, and now Brazilian Portuguese. Is this ransomware being sold as a commodity in underground forums with Brazilian crews just standing on the shoulders of giants? Or is this a regional operation just starting out?
As one of the very few ransomware variants that prepend a custom ‘Lock.’ extension to the encrypted files instead of appending it, the task of recognizing this malware is not particularly difficult. However, understanding its true origins could still be considered an ongoing debate.The drop
If we trust that the first transaction corresponds to the very first victim, the campaign has probably been active since 2016-04-04 17:29:26 (April 4th, 2016). In reality, this is not exactly accurate. The timestamp of the original dropper shows that the sample was actually compiled at the beginning of October:
That would mean that the criminal behind the campaign might have had different ransomware campaigns running in the past, or is just using the same BTC wallet for more than his criminal deeds.
The dropper is protected by the popular .NET obfuscator SmartAssembly, as can be seen by the string “Powered by SmartAssembly 184.108.40.206”. Once executed, it tries to mask itself in the Alternate Data Stream of the NTFS file system in Windows:
It’s capable of disabling Windows LUA protection:
“HKLM\SOFTWARE\MICROSOFT\WINDOWS\CURRENTVERSION\POLICIES\SYSTEM”; Key: “ENABLELUA”; Value: “00000000”
(cmd.exe /c %WINDIR%\System32\reg.exe ADD HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System /v EnableLUA /t REG_DWORD /d 0 /f
Reg.exe ADD HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System /v EnableLUA /t REG_DWORD /d 0 /f)
The mechanism used to write new information to the registry is quite unusual: it uses the official windows application ‘migwiz.exe’ in order to bypass the UAC screen, not requiring any action from the user to execute with elevated privileges.
The malware is able to do that by writing a library ‘cryptbase.dll’ to the same folder as the ‘migwiz.exe’ file. Then, as soon as it’s launched, the process will load this library, which has a WinExec call that will launch the command line provided by the parameter.
The reason why they are using MigWiz is because this process is one that is in Microsoft’s auto-elevate list, meaning it can be elevated without asking for explicit permission.
As a simple mean of information gathering, the dropper will read the name of the infected computer:
HKLM\SYSTEM\CONTROLSET001\CONTROL\COMPUTERNAME\ACTIVECOMPUTERNAME”; Key: “COMPUTERNAME
Moreover, it includes data stealer techniques, such as retrieving information from the clipboard, or while it’s being typed on the keyboard. Additionally it has the capability to reboot the user’s machine.
@4333be: push ebp
@4333bf: mov ebp, esp
@4333c1: sub esp, 14h
@4333c4: push ebx
@4333c5: mov ebx, dword ptr [ebp+08h]@4333c8: lea eax, dword ptr [ebp-04h]@4333cb: push eax
@4333cc: push 00000028h
@4333ce: call dword ptr [00482310h] ;GetCurrentProcess@KERNEL32.DLL
@4333d4: push eax
@4333d5: call dword ptr [0048202Ch] ;OpenProcessToken@ADVAPI32.DLL
@4333db: test eax, eax
@4333dd: je 0043341Eh
@4333df: lea ecx, dword ptr [ebp-10h]@4333e2: push ecx
@4333e3: push 00487D68h ;SeShutdownPrivilege
Finally, it drops and executes the file tmp.exe (corresponding hash B4FDC93E0C089F6B885FFC13024E4B9).Hello sir, hello madam, your fines have been locked
After the infection has been completed, as is usual in all ransomware families, the ransom note is shown. This time, it is written in Brazilian Portuguese and demanding 2000 BRL, which equates to around 627 USD or 1 BTC at the time of writing.
The bitcoin address provided (1LaHiL3vTGdbXnzyQ9omsYt8nFkUafXzK4) for payment shows total deposits for 1.89 BTC although many transactions have been made since the creation of this wallet. This is leading us to believe that either the criminal has been using the wallet for other purposes or they have bargaining with the victims and offering them a lower price, as depicted by the amount in each transaction.
The ransom note is very succinct, without giving any special payment URL or any other type of information. The victim will have to learn about bitcoin payments the hard way, and should they need support they can reach the criminals through a single email point of contact.
TODOS os seus arquivos foram BLOQUEADOS e esse bloqueio somente serão DESBLOQUEADOS
caso pague um valor em R$ 2000,00 (dois Mil reais) em Bitcoins
Após o pagamento desse valor, basta me enviar um print para o email
que estarei lhe enviando o programa com a senha para descriptografar/desbloquear o seus arquivos.
Caso o pagamento não seja efetuado, todos os seus dados serão bloqueados
permanentemente e o seu computador sera totalmente formatado
(Perdendo assim, todas as informações contidas nele, incluindo senhas de email, bancárias…)
O pagamento deverá ser efetuado nesse endereço de Bitcoin:
Para converter seu saldo em bitcoins acesse o site:
The growth of ransomware in Brazil has been nothing short of impressive, taking into consideration that during October 2016 alone the popular ransomware family Trojan-Ransom.NSIS.MyxaHaTpyne.gen family grew by 287.96%, and another of the usual suspects Trojan-Ransom.Win32.CryptXXX.gen grew by 56.96%, (when compared to the previous month in each case.)
In 2016, the 3 most important families of ransomware have been Trojan-Ransom.Win32.Blocker, accounting for 49.63% of the total infections,
Trojan-Ransom.NSIS.Onion, 29.09%, and Trojan-Ransom.Win32.Locky, 3.99%.
Currently, Brazil is the eighth most affected country worldwide as far as ransomware infections go for this year, and ranked first in Latin America.Indicators of compromise
Compiled: Saturday, October 8 2016, 11:22:30 – 32 Bit .NET
Compiled: Sunday, January 29 2012, 21:32:28 – 32 Bit
A few days ago, Microsoft published the “critical” MS16-120 security bulletin with fixes for vulnerabilities in Microsoft Windows, Microsoft Office, Skype for Business, Silverlight and Microsoft Lync.
One of the vulnerabilities – CVE-2016-3393 – was reported to Microsoft by Kaspersky Lab in September 2016.
Here’s a bit of background on how this zero-day was discovered. A few of months ago, we deployed a new set of technologies in our products to identify and block zero-day attacks. These technologies proved their effectiveness earlier this year, when we discovered two Adobe Flash zero-day exploits – CVE-2016-1010 and CVE-2016-4171. Two Windows EoP exploits have also been found with the help of this technology. One is CVE-2016-0165. The other is CVE-2016-3393.
Like most zero-day exploits found in the wild today, CVE-2016-3393 is used by an APT group we call FruityArmor. FruityArmor is perhaps a bit unusual due to the fact that it leverages an attack platform that is built entirely around PowerShell. The group’s primary malware implant is written in PowerShell and all commands from the operators are also sent in the form of PowerShell scripts.
In this report we describe the vulnerability that was used by this group to elevate privileges on a victim’s machine. Please keep in mind that we will not be publishing all the details about this vulnerability because of the risk that other threat actors may use them in their attacks.Attack chain description
To achieve remote code execution on a victim’s machine, FruityArmor normally relies on a browser exploit. Since many modern browsers are built around sandboxes, a single exploit is generally not sufficient to allow full access to a targeted machine. Most of the recent attacks we’ve seen that rely on a browser exploit are combined with an EoP exploit, which allows for a reliable sandbox escape.
In the case of FruityArmor, the initial browser exploitation is always followed by an EoP exploit. This comes in the form of a module, which runs directly in memory. The main goal of this module is to unpack a specially crafted TTF font containing the CVE-2016-3393 exploit. After unpacking, the module directly loads the code exploit from memory with the help of AddFontMemResourceEx. After successfully leveraging CVE-2016-3393, a second stage payload is executed with higher privileges to execute PowerShell with a meterpreter-style script that connects to the C&C.EOP zero-day details
The vulnerability is located in the cjComputeGLYPHSET_MSFT_GENERAL function from the Win32k.sys system module. This function parses the cmap table and fills internal structures. The CMAP structure looks like this:
The most interesting parts of this structure are two arrays – endCount and startCount. The exploit contains the next cmap table with segments:
To compute how much memory to allocate to internal structures, the function executes this code:
After computing this number, the function allocates memory for structures in the following way:
The problem is that if we compute the entire table, we will achieve an integer overflow and the cnt variable will contain an incorrect value.
In kernel, we see the following picture:
The code allocates memory only for 0x18 InternalStruct but then there is a loop for all the segments range (this value was extracted from the file directly):
Using the cmap table, the v44 variable (index) could be controlled and, as a result, we get memory corruption. To achieve it, the attacker can do the following:
- Make an integer overflow in win32k!cjComputeGLYPHSET_MSFT_GENERAL
- Make a specific segment ranges in font file to access interesting memory.
What about Windows 10? As most of you know, the font processing in Windows 10 is performed in a special user mode process with restricted privileges. This is a very good solution but the code has the same bug in the TTF processing.
As a result, if you load/open this font exploit in Windows 10, you will see the crash of fontdrvhost.exe:
Kaspersky Lab detects this exploit as:
We would like to thank Microsoft for their swift response in closing this security hole.
* More information about the FruityArmor APT group is available to customers of Kaspersky Intelligence Services. Contact: email@example.com
In April of this year, we registered some mass attacks on Facebook users in Russia. As a result, many Russian-speaking users of the social network fell victim to fraudsters. Half a year later the fraudsters have used the same tactics to attack Facebook users in Europe.
The attackers use a compromised Facebook account to post a link to an adult video that is supposedly on the popular YouTube service. In order to attract potential victims, “likes” are added from the account holder’s list of friends. The fraudsters rely on the user or their friends being curious and those who would like to watch an “18+” video.
Clicking on the link opens a page made to look like YouTube.
However, a quick look at the address bar is enough to see that the page has nothing to do with YouTube. During the latest attack the fraudsters distributed a “video” located on the xic.graphics domain. The domain is not currently available, but we discovered more than 140 domains with the same registration data that can be used for similar purposes.
After trying to start the video, a pop-up banner appears prompting the user to install a browser extension. In this particular example, it was called ‘Profesjonalny Asystent’ (Professional assistant), but we also came across other names.
The “View details” message explains that if the extension is not installed, the video cannot be viewed.
The attackers are banking on an intrigued victim not being interested in the details and just installing the extension. As a result, the extension gains rights to read all the data in the browser, which the fraudsters can later use to get all the passwords, logins, credit card details and other confidential user information that is entered. The extension can also continue spreading links to itself on Facebook, but now in your name and among your friends.
We strongly recommend not clicking such links and not installing suspicious browser extensions. It’s also worth checking if any suspicious extensions have already been installed. If any are discovered, they should be immediately removed via the browser settings, and the passwords for sites that are visited most often, especially online banking, should be changed.
A Tweet posted recently by AVG researcher, Jakub Kroustek, suggested that a new ransomware, written entirely in Python, had been found in the wild, joining the emerging trend for Pysomwares such as the latest HolyCrypt, Fs0ciety Locker and others.
This Python executable comprises two main files. One is called boot_common.py and the other encryptor.py. The first is responsible for error-logging on Windows platforms, while the second, the encryptor, is the actual locker. Within the encryptor are a number of functions including two calls to the C&C server. The C&C is hidden behind a compromised web server located in Israel. The Israeli server was compromised using a known vulnerability in a content management system called Magento, which allowed the threat actors to upload a PHP shell script as well as additional files that assist them in streaming data from the ransomware to the C&C and back.
A notable point to mention is that the server was also used for phishing attacks, and contained Paypal phishing pages. There are strong indications that a Hebrew-speaking threat actor was behind these phishing attacks. The stolen Paypal credentials were forwarded to another remote server located in Mexico and which contains the same arbitrary file upload technique, only with a different content management.
It is a known practice for attackers to look for low-hanging fruit into which they can inject their code in order to hide their C&C server. One such example was the CTB-Locker for web servers reported last March.Ransomware Analysis
To reverse the executable one should first conduct a number of checks using a convenient debugger. The universal steps for unpacking an unknown packer start with trying to set a memory breakpoint on popular functions that packers use, such as VirtualAlloc.
If the breakpoint hits, the next step involves switching to user mode and setting a hardware breakpoint (on access). That will assist in inspecting where exactly the program initializes the memory block. In most cases, an executable magic header (MZ) should appear in the memory block. However, in this case the following screenshot shows the readable data that was allocated to that memory block:
After the data was allocated to the memory block, it appeared to be using VM code (python vm) to execute the code. For those who are not familiar with the term, VM code is the process of creating new instruction sets based on the author’s request. The CPU uses those instruction sets to understand the instructions.
py2exe simply converts the code to x86 assembly, the architecture used on the CPU for communication, and, by loading a python DLLs, loads all the modules into the memory.
We found that the executable file was generated using py2exe. The first indicator was a stack PUSH instruction to add the string – PY2EXE_VERBOSE: a module that compiles Python scripts to Microsoft Windows executables.
PY2EXE module string disclosure
A module that reverse the operation of the py2exe can be found in Github and is called unpy2exe. This module will revert the executable back to its origin Python compiled code (i.e. .pyc file). From that format, another step will be required to fully revert to the original code. We randomly chose to use EasyPythonDecompiler.
Fully decompiled Python scripts
In it’s current state, the executable fails to encrypt the file system, simply because the threat actors must have migrated from the current server to another. By doing so, they deleted the remaining traces of the PHP files they used for data collection from a victim’s machine. The following is the log file that is generated upon exception:
Error log file being generated by the boot_common.py
The scripts in Python use two files:
This file throws an “error” to show that the program failed to execute if there is a problem.
This script encrypts the victim’s files.
The ransomware disables the following features from the compromised machine:
By overwriting the registry policies it disables Registry Tools, Task Manager, CMD and Run.
list of registry manipulations
It then continues with changing bcdedit to disable recovery and ignore boot status policy.
Upon successful encryption, the ransomware will encrypt the following file extensions:
*.mid, *.wma, *.flv, *.mkv, *.mov, *.avi, *.asf, *.mpeg, *.vob, *.mpg, *.wmv, *.fla, *.swf, *.wav, *.qcow2, *.vdi, *.vmdk, *.vmx, *.gpg, *.aes, *.ARC, *.PAQ, *.tar.bz2, *.tbk, *.bak, *.tar, *.tgz, *.rar, *.zip, *.djv, *.djvu, *.svg, *.bmp, *.png, *.gif, *.raw, *.cgm, *.jpeg, *.jpg, *.tif, *.tiff, *.NEF, *.psd, *.cmd, *.class, *.jar, *.java, *.asp, *.brd, *.sch, *.dch, *.dip, *.vbs, *.asm, *.pas, *.cpp, *.php, *.ldf, *.mdf, *.ibd, *.MYI, *.MYD, *.frm, *.odb, *.dbf, *.mdb, *.sql, *.SQLITEDB, *.SQLITE3, *.asc, *.lay6, *.lay, *.ms11 (Security copy), *.sldm, *.sldx, *.ppsm, *.ppsx, *.ppam, *.docb, *.mml, *.sxm, *.otg, *.odg, *.uop, *.potx, *.potm, *.pptx, *.pptm, *.std, *.sxd, *.pot, *.pps, *.sti, *.sxi, *.otp, *.odp, *.wks, *.xltx, *.xltm, *.xlsx, *.xlsm, *.xlsb, *.slk, *.xlw, *.xlt, *.xlm, *.xlc, *.dif, *.stc, *.sxc, *.ots, *.ods, *.hwp, *.dotm, *.dotx, *.docm, *.docx, *.DOT, *.max, *.xml, *.txt, *.CSV, *.uot, *.RTF, *.pdf, *.XLS, *.PPT, *.stw, *.sxw, *.ott, *.odt, *.DOC, *.pem, *.csr, *.crt, *.key and wallet.dat to encrypt crypto currency wallets
The files are encrypted using AES with CBC mode for the following paths:
*userhome - The current user home directory
When the encryption step is done, the ransomware will remove the restore points and write the README_FOR_DECRYPT.txt file and execute it. The following screen shot is the ransom note:
CryPy Ransomware Note embedded in the Python code
The threat actor behind the attack asks the victim to contact it via email, and to send a request to the following two email addresses to receive the decryption program:
Note that the ransom note contains mistakes, implying that it has been written by a non-English speaker. First, the headline is missing a ‘T’ in “IMPORTAN INFORMATION”. Second, the sentence “Decrypting of your files…” is syntatically wrong. Native speakers will be able to find additional mistakes.
The threat actor claims that files will be deleted every 6 hours, which reflects the approach of more advanced ransomwares. However, it forgets to mention proof of decryption or a channel that can be used in cases where the payment process is not responsive. This points to the executable being at an early stage of development.
The ransomware survives a reboot by adding the following keys to the registry:
The code for adding the values to the registry are located on the functions autorun() and autorun2().
These keys cause the computer to execute the files after the computer is restarted.
Right before launching the ransom note, the script calls a delete_shadow() function that takes no arguments, and simply executes the following command line code to remove all shadow copies and prevent recovery from backup:
os.system("vssadmin Delete shadows /all /Quiet")
Lastly, the file calls autorun2() fuction that copies the ransomware from its current location to C:\\Users\\\\AppData\\Local with hardcoded name:
The ransomware hides behind an Israeli web server which was compromised using Shell script arbitrary upload written in PHP. The compromise and upload were possible because the server carried a vulnerable Magento CMS.
The executable transfers data over an unencrypted HTTP channel in clear-text. This allows for easy traffic inspection using a network listener. The following screenshot is the traffic being sent to the server:
Inspecting the Magento exploit and the compromised server, we found that the origin of the upload carries the title Pak Haxor – Auto Xploiter and the email ardiansyah09996@gmail[.]com and that the file was uploaded in August 2016, which aligns with the case in subject. The following screenshot reveals how attackers are using massive exploiters that scan for vulnerable web servers and exploit the vulnerability, which they later visit to expand their control over the server:
Part of such an exploitation technique is dropping additional PHP scripts to refine a more sophisticated attack, such as the CryPy ransomware.
One such script can be found hard-coded in the CryPy Python code, in the form of a GET request. The request is sent with two parameters to a script that was uploaded using the Auto Xploiter and carries the name victim.php. By reviewing the Python code it is easier to understand the type of data being presented in Base64 encoding format.
As seen in the screenshot above, the configurl parameter accepts a URL querystring where the victim_info input value of the info parameter is derived from the platform module.
uname() is used when one wants to return a tuple of system, node, release, version, machine and processor values. These are encoded with Base64.
The next parameter is ip which contains the socket.gethostname() which basically collects an IP address.
The querystring is then sent to urllib.urlopen(), which will send a GET request to the selected server and read the reponse content into glob_config.
The response contains a JSON format payload which is checked for the following keys:
x_ID – the victim’s unique ID to request their decryption keys after payment.
x_UDP – Not used; perhaps saved for future use.
x_PDP – Not used; perhaps saved for future use.
The second call is implemented in a function called generate_file() which is responsible for fetching a unique key for each file before encryption.
We have seen in recent lockers that, in order to demonstrate trust and integrity, the victim is able to decrypt one/two files before processing the payment. This proves decryptor validity. In order to randomly choose a file, the attacker must first generate a unique token for each one. The second PHP script found in the code is savekey.php which is described in the following screenshot and is suspected to have the C2 IP in it. It was however deleted long before we were able to reach it.
As for the first call, the second sends two parameters. The first is the file’s name and the other is the victim ID. In return, the server responds with two keys:
X – Unique key after encryption which will be appended to the file’s header.
Y – New filename which will be stored instead of the previous one.
These parameters are then sent to an encryption routine, along with the file’s original name.IOCs REG Keys
Machine learning has long permeated all areas of human activity. It not only plays a key role in the recognition of speech, gestures, handwriting and images – without machine learning it would be difficult to imagine modern medicine, banking, bioinformatics and any type of quality control. Even the weather forecast cannot be made without machines capable of learning and generalization.
I would like to warn about, or dispel, some of the misconceptions associated with the use of ML in the field of cybersecurity.Myth №1: Machine learning in information security is a novelty
For some reason, discussion of artificial intelligence in cybersecurity has become all the rage of late. If you haven’t been following this theme over the longer term, you may well think it’s something new.
A bit of background: one of the first machine learning algorithms, the artificial neural network, was invented in the 1950s. Interestingly, at that time it was thought the algorithm would quickly lead to the creation of “strong” artificial intelligence. That is, intelligence capable of thinking, understanding itself and solving other tasks in addition to those it was programmed for. Then there is so-called weak AI. It can solve some creative tasks – recognize images, predict the weather, play chess, etc. Now, 60 years later, we have a much better understanding of the fact that the creation of true AI will take years, and what today is referred to as artificial intelligence is in fact machine learning.
When it comes to cybersecurity, machine learning is nothing new either. Algorithms of this class were first implemented 10-12 years ago. At that time the amount of new malware was doubling every two years, and once it became clear that simple automation for virus analysts was not enough, a qualitative leap forward was required. That leap came in the form of processing the files in a virus collection, which made it possible to search for files similar to the one being examined. The final verdict about whether a file was malicious was issued by a human, but this function was almost immediately transferred to the robot.
In other words, there’s nothing new about machine learning in cybersecurity.Myth №2: Machine learning in cybersecurity is straightforward – everything’s already been thought of
It’s true that for some spheres where machine learning is used there are a few ready-made algorithms. These spheres include facial and emotion recognition, or distinguishing cats from dogs. In these and many other cases, someone has done a lot of thinking, identified the necessary signs, selected an appropriate mathematical tool, set aside the necessary computing resources, and then made all their findings publicly available. Now, every schoolkid can use these algorithms.
Machine learning determines the quality of cookies by the number of chocolate chips and the radius of the cookie
This creates the false impression that the algorithms already exist for malware detection too. That is not the case. We at Kaspersky Lab have spent more than 10 years developing and patenting a number of technologies. And we continue to carry out research and come up with new ideas because … well, that’s where the next myth comes in.Myth №3: Machine learning – do it once and forget about it
There is a conceptual difference between malware detection and facial recognition. Faces will remain faces – nothing is going to change in that respect. In the majority of spheres where machine learning is used, the objective is not changing with time, while in the case of malware things are changing constantly and rapidly. That’s because cybercriminals are highly motivated people (money, espionage, terrorism…). Their intelligence is not artificial; they are actively combating and intentionally modifying malicious programs to get away from the trained model.
That’s why the model has to be constantly taught, sometimes even retrained from scratch. Obviously, with rapidly modifying malware, a security solution based on a model without an antivirus database is worthless. Cybercriminals can think creatively when necessary.Myth №4: You can let security software learn on the client side
Let’s say, it processes the client’s files. Most of them will be clean, but some will be malicious. The latter are mutating, of course, but the model will learn.
However, it doesn’t work like that, because the number of malware samples passing through the computer of an average client is much smaller than the amount of malware samples collected by an antivirus lab system. And because there will be no samples for learning, there will be no generalization. Factor in the “creativity” of the virus writers (see the previous myth) and detection will fail, the model will start recognizing malware as clean files and will “learn the wrong things.”
Why use multi-level protection based on different technologies? Why not put all your eggs in one basket if that basket is so smart and advanced? One algorithm is enough to solve everything. Right?
The thing is most malware belongs to families consisting of a large number of modifications of one malicious program. For example, Trojan-Ransom.Win32.Shade is a family of 30,000 cryptors. A model can be taught with a large number of samples, and it will gain the ability to detect future threats (within certain limits, see Myth №3). In these circumstances machine learning works well.
However, it’s often the case that a family consists of just a few samples, or even one. Perhaps the author didn’t want to go into battle with security software after his “brainchild” was immediately detected due to its behavior. Instead, he decided to attack those who had no security software installed or those who had no behavioral detection (i.e., those who had put all their eggs in one basket).
These sorts of “mini-families” cannot be used to teach a model – generalization (the essence of machine learning) is impossible with just one or two examples. In these circumstances, it is much more effective to detect a threat using time-tested methods based on the hash, masks, etc.
Another example is targeted attacks. The authors behind these attacks have no intention of producing more and more new samples. They create one sample for one victim, and you can be sure this sample will not be detected by a protection solution (unless it is a solution specifically designed for this purpose, for example, Kaspersky Anti-Targeted Attack Platform). Once again, hash-based detection is more effective.
Conclusion: different tools need to be used in different situations. Multi-level protection is more effective than a single level – more effective tools should not be neglected just because they are “out of fashion.”The problem of Robocop
And one last thing. This is more of a warning than a myth. Researchers are currently paying more attention to what mistakes complex models are making: in some cases, the decisions they take, cannot be explained from the point of view of human logic.
Machine learning can be trusted. But critical systems (the autopilot in planes and automobiles, medicine, control services, etc.) usually have very strict quality standards; formal verification of software programs is used, while in machine learning, we delegate part of the thought process and responsibility to the machine. That’s why quality control of a model needs to be conducted by highly respected experts.
Earlier today we became aware of a malicious website delivering Petya through the Hunter exploit kit. While there is nothing special about yet another exploit kit page, this one caught our attention because it mimics the index page of our sinkhole systems.
A malicious webpage faking one of our research systems
With cybercriminals increasingly trying to exploit trust relationships in cyberspace, it’s easy to get fooled by such attempts. We believe the criminals attempted to mimic our sinkhole systems in order to avoid being shut down by other researchers.
Just last week we were investigating a case of a serious attack that potentially breached a company. When we collected proof of the attack, we had to contact the company to help them isolate compromised systems and remediate. This brought us to a problem we commonly see today: the problem of trust.
The first reaction you normally have when someone calls you and attempts to convince you must arouse suspicion. In our investigations we normally deal with security personnel, who are highly paranoid people and do not trust anyone by nature. So far, the reaction of the company’s security staff was spot on: get the name of the caller, the company and department name, look up the company contacts using an independent, trusted, verifiable source, contact the company and confirm the facts, asking to connect to the researcher in the office immediately to do additional voice recognition. When that is done, the conversation can be resumed. Such a reaction and verification process is what we consider standard in our business. Unfortunately, we haven’t seen the same level of cautiousness among regular users.
A typical strategy for cybercriminals is to try to hide their tools, exploit kits and other malicious files on a compromised legitimate website or inject a malicious payload into a hijacked banner network account. Attackers also will rip entire websites, or just replace links to redirect visitors to attacker controlled sites, as we observed with the StrongPity watering holes. In this case, they simply counted on the confusion caused by visual appearance.
The fake webpage looks exactly the same as the original one from our research server and there is no point in finding even minor differences. Every webpage on the web can be copied and made to look identical to the source, except for the page’s original address or validated SSL certificate. PGPHtml is an alternative possibility, with each page explicitly stating its host domain or IP and then signed and verified with a public key. The server in question has been reportedly serving the Pony Trojan, hosting the Hunter Exploit Kit and distributing Petya ransomware.
We believe that this was the act of Russian-speaking cybercriminals, who send messages to our side every time their activities are affected by the work we do. We are bringing this to your attention to make you a little bit more cautious. Having said that, our first reaction was laughter, because it brought back some memories of an excellent short video on this matter shot by our colleagues from the security industry. And, because of this history of receiving messages from malware authors in their code and on sites, we think it is unlikely that this site is a watering hole targeting security researchers.
Unfortunately, this game of shadows is a well-known method not only in the criminal world but also in the world of advanced targeted attackers. We have seen in the past that some APT groups use deceiving tactics in order to try to confuse security researchers into wrong attribution. We have seen malware samples in the past where attackers from one group implanted decoys, trying to mimic the behaviour of their rivals. This is done to harden the research process or consume extra time. The attribution process, being the hardest part of any computer investigation, can easily be driven in the wrong direction. However, we have been looking at these attempts for a long time and learned to recognize such false flags. Now we would like you to be cautious and verify everything you see.
Related to this topic, our colleagues recently presented a more in-depth analysis of these techniques at VB 2016. You can read their entire paper here: Wave your false flags!
As a new VirusBulletin is upon us, it’s once again time to deep dive into interesting topics in anti-malware research. This time around, we’ve chosen to focus on attribution in APT research, its methods and complications, and how intermediate-to-advanced attackers are already manipulating attribution indicators in an attempt to mislead researchers and squander limited incident response resources. False flags and deception tactics have always been discussed as possibilities in this space, but we wanted to put out a wealth of examples to advance the conversation. Our hope is that we can further the dialogue regarding attribution to involve more nuanced and daunting questions that have yet to be conclusively addressed.
When reading some of the examples, keep in mind that this was written back in February to submit to the VirusBulletin call for papers. At the time, deception techniques were a topic discussed in private between researchers, but never publicly substantiated. Since then, events over the summer have made this topic commonplace to the infosec community, if still a matter of contention and skepticism. The paper is extensive (but not exhaustive) and we hope that those of you interested in the subject will take some time to go through the reasoning and examples. The following are some takeaways we hope will pique your interest and get a dialogue going regarding the nuances of attribution as it’s currently being done:There’s nothing straightforward about ‘whodunnit’
From the perspective of threat intelligence producers, there exist complications regarding attribution and its practical purpose. As any honest anti-malware company should admit, no institution has complete or perfect visibility into the activities of any threat actor. Different companies see different fragments; different types of service providers compliment that visibility with other types of data. This is a research space rewarded by cooperation and data exchanges. As such, when attempting to describe the activities of a threat actor, it’s difficult to suggest that a single threat intelligence product can stand as the exhaustive final chapter on any of the threat actors we investigate. Much less, provide a definitive picture of their identity, activities, and resources.
The true value of a threat intelligence product is its actionable potential, its ability to help detect and mitigate attacks, to provide clear avenues for proactive defense and improved defensive posture against a persistent and shadowy adversary, and to provide understanding to institutions and individuals outmatched and outwitted by the topdogs of the cyber espionage space. And even then, we have to consider that when it comes to wide dissemination of this information for the benefit of the public, it’s not just victims that are reading threat intelligence products. As our paper sets out to demonstrate, attackers too are keenly consuming threat intelligence research, learning from researcher methods as well as other APT groups and incorporating that information to better their own operations.What can attribution do for you?
Threat Intelligence has come a long way in the last five years or so, and with that, more and more attribution is being done publicly by companies selling this as a product. Before that, attribution was only really done within governments and kept private or classified. These days, journalists and commentators are after the ‘sexy’ part of the story and are heavily focused on the “who” and not the “why”. While we are not arguing for or against companies performing attribution and publicly sharing their discoveries, we do pose some questions around how deep attribution really needs to go based on the role of the organization defended and its ability to take action. For governments, the most fidelity is justifiably needed, especially when the outcome of the attribution results in diplomatic sanctions, offensive operations, or demarches. But for a private company consuming threat intelligence is that level of attribution really needed to protect that organization against these attacks?
We hope the paper proves thought-provoking to threat intelligence producers and consumers alike, aligning needs and expectations, and preparing the infosec community for increasingly deceptive and manipulative interactions with our adversaries.