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- According to KSN data, Kaspersky Lab solutions detected and repelled 228,420,754 malicious attacks from online resources located in 195 countries all over the world.
- 74,001,808 unique URLs were recognized as malicious by web antivirus components.
- Kaspersky Lab’s web antivirus detected 18,610,281 unique malicious objects: scripts, exploits, executable files, etc.
- There were 459,970 registered notifications about attempted malware infections that aim to steal money via online access to bank accounts.
- Crypto ransomware attacks were blocked on 372,602 computers of unique users.
- Kaspersky Lab’s file antivirus detected a total of 174,547,611 unique malicious and potentially unwanted objects.
- Kaspersky Lab mobile security products detected:
- 2,045,323 malicious installation packages;
- 4,146 mobile banker Trojans;
- 2,896 mobile ransomware Trojans.
2016 has only just got underway, but the first three months have already seen the same amount of cybersecurity events that just a few years ago would have seemed normal for a whole year. The main underlying trends remained the same, while there was significant growth in trends related to traditional cybercrime, especially mobile threats and global ransomware epidemics.
Ransomware became the main theme of the quarter after knocking targeted attacks from the top of the most popular threat rating. Unfortunately, this is a situation that will continue to evolve, and those behind the extortion could well end up being named “problem of the year”.Targeted attacks BlackEnergy2/3
The BlackEnergy cyberattack on the Ukrainian energy sector was the most high-profile incident. Although it occurred at the end of last year, a fuller picture of what happened only appeared in the course of the subsequent analysis. Moreover, attempts by cybercriminals to arrange new attacks continued in 2016.
The attack was unique because of the damage it caused – the hackers managed to disable the power distribution system in Western Ukraine, launch the Wiper program on the targeted systems and carry out a telephone DDoS on the technical support services of the affected companies.
There were numerous publications about the attack, and Kaspersky Lab’s experts revealed several aspects of the activities of the group responsible. In particular, they published an analysis of the tool used to penetrate the systems – a malicious DOC file.
For those who want to learn more about the attack, we recommend the report prepared by the American SANS Institute and ICS-CERT.Poseidon
In February, the experts at Kaspersky Lab revealed details about the activities of Poseidon – the first Portuguese-speaking targeted attack group which had set up a custom-tailored malware boutique.
Although the report was only released in 2016, the group has been operational for a long time. Malware campaigns that were most probably supported by Poseidon were detected as far back as 2005, while the first sample dates back to 2001. Poseidon’s arsenal is focused primarily on the Microsoft Windows operating system family: from Windows 95, which the group targeted in its early days, to Windows 8.1 and Windows Server 2012, which were targeted by the most recently detected malware samples.
The attack scenario is carefully tailored to the victim. Although the initial infection occurs according to the same scenario, the following stages of the campaign specifically customize the infection method for each new victim. That is why the specialists from the Global Research & Analysis Team (GReAT) decided to call Poseidon a “custom-tailored malware boutique”.
Having gained access to the corporate network, the criminals move across the network and collect as much data as possible in order to escalate their privileges, create a network map and to identify the computer they need. The main target of the attack is usually the local Windows domain controller. Once they have control over it, the attackers can steal intellectual property, data, trade secrets, and other valuable information.
The information collected by Poseidon for its owners was in most cases used to blackmail victim companies into contracting the Poseidon Group as a security firm. Regardless of whether a contract was signed, Poseidon remained on the network.Hacking Team
Yet another infamous “boutique” creating cyber-espionage tools, the Italian company Hacking Team, fell victim to a cyberattack last year in which a huge database of its employee email correspondence was stolen, as well as project source codes.
The incident revealed a lot of problems in the work of the company and many thought it would be very difficult for the business to develop further. However, at the beginning of 2016 new Hacking Team implants for OSX were found. This indicates that the group has no intention of halting its work and is continuing to develop in the sphere of secondary operating systems. This means their “creations” will continue to be a problem for users who have become an object of interest for HT customers.
Yet another story related to Hacking Team was the hunt for a Microsoft Silverlight 0-day. Information about the possible presence of this vulnerability was found in the Italian company’s documents. Based on very little initial data and armed with the Yara and VirusTotal tools, our experts set a trap and waited. And sure enough, they detected a 0-day exploit.Operation BLOCKBASTER
Kaspersky Lab was among the participants in operation Blockbaster, a joint investigation conducted by several major IT security companies. The subject of the investigation was activity by the Lazarus Group, a cybercriminal gang of supposedly North Korean origin that was involved in the attack on Sony Pictures in 2014.
The Lazarus Group has been around since 2009, but their activities moved up a gear from 2011. The group is responsible for such well-known attacks as Troy, Dark Seoul (Wiper), WildPositron. During the investigation over 40 different types of malicious program, which they had created over the years, were detected. In particular, the group used their malware to attack companies, financial institutions, radio and television. Use of exploits for 0-day vulnerabilities was also recorded.Hospitals under attack
This section on targeted attacks should also include Sergei Lozhkin’s research on how hackers can penetrate the internal network of hospitals and gain full access to patient data using publicly available tools and services.
Unfortunately, medical institutions are being targeted more and more by such attacks. In the first quarter of 2016, there were several incidents of hospital networks being infected with various types of Trojan ransomware that encrypts data and demands a ransom to decrypt it.
The latest incident was an attack on the MedStar network that affected 10 hospitals. According to the network’s official report, the data was saved without paying a ransom to the blackmailers, while another hospital in California ended up paying $17,000 for a ransomware crypto key.Cybercrime Adwind
At the Security Analyst Summit 2016 (SAS 2016) our GReAT experts presented the results of their investigation into the Trojan known as Adwind RAT (Remote Access Tool). Having studied the activity of the malware, the researchers came to the conclusion that even the story behind the Trojan’s creation was out of the ordinary.
The Trojan was developed continuously over several years, with the first samples appearing in 2012. It has had different names at different times: in 2012, the creators were selling it as Frutas; in 2013 it was called Adwind; in 2014 the Trojan was known as Unrecom and AlienSpy; and in 2015 it was named JSocket.
The GReAT experts believe that Adwind and all its incarnations have been developed by one hard-working hacker who has been releasing new features and modules for four years.
The Adwind platform was initially only available in Spanish, but an English-language interface was added later, allowing cybercriminals worldwide to evaluate it. The main users of this Trojan are those conducting advanced cyber fraud, unscrupulous competitors, as well as so-called Internet mercenaries who are paid for spying on people and organizations online. Adwind can also be used by anyone wishing to spy on their friends.
Geographically, the biggest concentration of victims has also changed over the last four years. In 2013, the targets were mostly in Spanish- and Arabic-speaking countries. The following year, cybercriminals focused on Turkey and India, as well as the United Arab Emirates, the United States and Vietnam. In 2015, Russia topped the rating with the United Arab Emirates, Turkey, the United States and Germany close behind.
Fortunately, our investigation was not in vain – a few days after its publication, the JSocket website stopped working and the Adwind author ceased their activity. Since then, no new versions of the Trojan have appeared. Perhaps we can expect another reincarnation of the Trojan, or maybe this is the end of the story.Banking threats
At the Security Analyst Summit (SAS in 2016), Kaspersky Lab announced the discovery of two new gangs engaged in APT-style bank robberies – Metel and GCMAN – and the reemergence of the Carbanak group with new targets in its sights.
In 2015, Kaspersky Lab researchers conducted incident response investigations for 29 organizations located in Russia that were infected by these three groups.
There are other cybercriminal groups currently attacking banks in Russia, but these three are the most active and are involved in the most high-profile thefts from both customer bank accounts and the banks themselves.
The activity of Carbanak 2.0 is of particular interest. In December 2015, Kaspersky Lab confirmed that the group was still active after discovering signs of Carbanak in a telecommunications company and a financial organization. An interesting feature of the Carbanak 2.0 group is that they have a different type of victim. The group has moved beyond banks and is now targeting the budgeting and accounting departments of any organization that interests them, using the same APT-style tools and techniques.
In one remarkable case, the Carbanak 2.0 gang used its access to a financial institution that stored information about shareholders to change the ownership details of a major company. The information was modified to name a money mule as a shareholder of the company, displaying their IDs.FakeCERT
Yet another criminal gang known as Buhtrap came to the fore in the first quarter. It is responsible not only for the theft of hundreds of millions of rubles from Russian banks but also for organizing a targeted attack on banks using the names and attributes of FinCERT, a special department of the Central Bank of Russia created to detect cyberattacks and notify member banks. It was the first time that attackers had used the FinCert “brand” and the attack was carefully prepared; a corresponding domain name was created and the identifiers used by FinCERT were studied closely.
The malicious mass mailing affected hundreds of banks in Russia. The attackers have a database of their employee email addresses, including names and surnames. A legitimate remote administration tool was used as the remote access module installed in the system.Bangladesh
On the global arena, the most prominent attack on banks was that involving the Central Bank of Bangladesh. It was not just the object of the attack – the Central Bank – that was remarkable but also the amount of money the attackers managed to steal, plus the amount they tried to steal but failed.
The investigation is still ongoing, but according to the information that has been made public, it is possible to put together a picture of what happened. Back in early February, hackers managed to access the workstations of several employees at the national bank. Using their identities, the fraudsters began to send out transfer orders for money held in different banks including the New York Federal Reserve Bank. With full access and posing as employees, they were able to steal approximately $80 million. The money was transferred to accounts in the Philippines and then passed through a money-laundering scheme involving local casinos and forex brokers.
Another $20 million would have been transferred to Sri Lanka, but the hackers made an error in the name of a recipient organization; this aroused the suspicion of Deutsche Bank, which was the correspondent bank of the Central Bank of Bangladesh. An investigation found that the payment order had been initiated by hackers, and approximately $900 million was still waiting to be transferred.
It’s worth noting that Bangladesh’s Minister of Finance only learned about the incident a month later from the mass media. The head of the Central Bank was forced to resign, the investigators are currently trying to trace those responsible, and the bank is taking measures to return at least some of the stolen funds.Ransomware Trojans
As we mentioned above, ransomware Trojans were the main theme of the quarter and could well become the main problem of the year.
Making the situation worse is the fact that a number of ransomware Trojans have become accessible to anyone with a little bit of cyber know-how in the form of source code. As a consequence, even the average script-kiddy can deploy their own version of the Trojan which, together with the active use of Bitcoin for paying ransoms, makes it much easier to organize attacks with impunity.
Moreover, the term Ransomware-as-a-Service (RaaS) has already come into use. This involves the attackers offering to pay for Trojan distribution, promising a cut of any ransom money received. The clients of these services are usually webmasters of porn sites. There are services that work the other way round, offering a complete set of tools to the encryptor who takes responsibility for distributing the Trojan and takes 10% of the ransom as commission.
According to reports from several companies, the first quarter of 2016 saw incidents where ransomware was used by a number of well-known APT-groups, mainly Chinese. We also identified similar cases, and not only involving Chinese groups. If these incidents become a trend, the threat will move to a new level because the damage caused by ransomware is not much different from that caused by Wiper-type Trojans. In both cases, user data becomes inaccessible.
In addition, ransomware Trojans are expanding their sphere of activity; in Q1 2016, CTB-Locker targeted web servers.
The earlier version of CTB-Locker known as crypto-ransomware Onion differed from other ransomware in that it used the anonymous network Tor to protect its command servers from being disabled because, as a rule, it is only possible to disable static servers. The use of Tor also helped the malware avoid detection and blocking. There was one more thing that protected CTB-Locker operators: payment was only accepted in Bitcoins, a decentralized anonymous cryptocurrency.
The new version of this malicious program encrypts web servers, and demands less than half a Bitcoin (~ $150) as ransom. If the money is not paid on time, the ransom is doubled to about $300. Once the ransom is paid, a key is generated to decrypt the web server files.
However, the biggest crypto epidemic of Q1 2016 was caused by the ransomware Trojan Locky (detected by Kaspersky Lab products as Trojan-Ransom.Win32.Locky).
This Trojan is continuing to spread; Kaspersky Lab products have recorded attempts to infect users in 114 countries around the world.
In order to spread the Trojan, the cybercriminals use mass mailings in which malicious loaders are attached to spam messages. Initially, the malicious spam messages contained a DOC file attachment with a macro that downloaded the Locky Trojan from a remote server and executed it.
The most significant technical innovation in ransomware was full disk encryption (more specifically, encryption of the file system table) rather than file encryption. This trick was used by the Petya Trojan (the fact that it has a Russian name does not necessarily mean that it was created by Russian-language malware writers).
After encrypting the main file table, Petya shows its true face – a skull and crossbones composed of ASCII characters. Then the typical encryptor routine begins: the Trojan demands a ransom from the victim, 0.9 Bitcoin (about $380) in this case.
At this stage, the only thing that distinguishes Petya from other ransomware is the fact that it operates without an Internet connection. This is hardly surprising though, because Petya basically “eats” the operating system, including its ability to connect to the Internet. This means the user has to go to another computer to pay the ransom and recover their data.
In March, yet another encryptor for Mac OS X was discovered – Trojan-Ransom.OSX.KeRanger. The attackers used it to infect two BitTorrent client installers from the open source Transmission project, which were available for download on their official website. Most likely, the project site was hacked, and the files for download were substituted for malicious recompiled versions. The KeRanger Apple encryptor was signed with a valid Apple certificate, and could therefore bypass the Gatekeeper security feature.Statistics on Trojan encryptors
Encryptors belong to the Trojan-Ransom class of malware, i.e. to ransomware. Today, in addition to encryptors this class of malicious programs also includes so-called browser ransomware. In the general flow of Trojan-Ransom detections the share of browser ransomware accounts for 25%, and that is mainly in Russia and the CIS. In this section, we will not dwell on browser ransomware, but will look at malicious encryptors in more detail.The number of new Trojan-Ransom encryptors
The following graph represents the rise in the number of newly created encryptor modifications over the last two quarters.
Number of Trojan-Ransom encryptor modifications in Kaspersky Lab’s Virus Collection (Q4 2015 vs Q1 2016)
The overall number of encryptor modifications in our Virus Collection to date is at least 15,000. Nine new encryptor families and 2,900 new modifications were detected in Q1.The number of users attacked by encryptors
Number of users attacked by Trojan-Ransom encryptor malware (Q1 2016)
In Q1 2016, 372,602 unique users were attacked by encryptors, which is 30% more than in the previous quarter. Approximately 17% of those attacked were in the corporate sector.
It is important to keep in mind that the real number of incidents is several times higher: the statistics reflect only the results of signature-based and heuristic detections, while in most cases Kaspersky Lab products detect encryption Trojans based on behavior recognition models and issue the Generic verdict, which does not distinguish the types of malicious software.Top 10 countries attacked by encryptors Country* % of users attacked by encryptors** 1 Italy 3.06 2 Netherlands 1.81 3 Belgium 1.58 4 Luxembourg 1.36 5 Bulgaria 1.31 6 Croatia 1.16 7 Rwanda 1.15 8 Lebanon 1.13 9 Japan 1.11 10 Maldives 1.11
* We excluded those countries in which the number of Kaspersky Lab product users is relatively small (less than 10,000).
** Unique users whose computers have been targeted by Trojan-Ransom encryptor malware as a percentage of all unique users of Kaspersky Lab products in the country.
In Q1, the first six places in the Top 10 were occupied by European countries. Italy (3.06%) topped the rating; the most widespread encryptor family in this country was Teslacrypt (Trojan-Ransom.Win32.Bitman). Italy was followed by the Netherlands (1.81%) and Belgium (1.58%).Top 10 most widespread encryptor families Name Verdict* Percentage of users** 1 Teslacrypt Trojan-Ransom.Win32.Bitman/Trojan-Ransom.JS.Cryptoload 58.43% 2 CTB-Locker Trojan-Ransom.Win32.Onion/Trojan-Ransom.NSIS.Onion 23.49% 3 Cryptowall / Cryptodef Trojan-Ransom.Win32.Cryptodef 3.41% 4 Cryakl Trojan-Ransom.Win32.Cryakl 3.22% 5 Scatter Trojan-Ransom.BAT.Scatter/Trojan-Downloader.JS.Scatter/Trojan-Dropper.JS.Scatter/Trojan-Ransom.Win32.Scatter 2.47% 6 Rakhni Trojan-Ransom.Win32.Rakhni/Trojan-Downloader.Win32.Rakhni 1.86% 7 Locky Trojan-Ransom.Win32.Locky 1.30% 8 Shade Trojan-Ransom.Win32.Shade 1.21% 9 iTorLock / Troli Trojan-Ransom.MSIL.Lortok 0.84% 10 Mor / Gulcrypt Trojan-Ransom.Win32.Mor 0.78%
* 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.
First place in Q1 was occupied by the Teslacrypt family represented by two verdicts: Trojan-Ransom.Win32.Bitman and Trojan-Ransom.JS.Cryptoload. The second verdict is typical for scripts that are sent out in ZIP archives as part of spam mailings. In the past, these scripts downloaded malware such as Fareit and Cryptowall, but recently the attackers have switched to TeslaCrypt. Noticeably, in Q1 new versions of this encryptor with an improved encryption algorithm were spread this way: the authors used the “reliable” RSA-4096 instead of AES.
Second came the CTB-Locker (Trojan-Ransom.Win32 / NSIS.Onion) family. The members of this family are usually distributed via an affiliate program, and are supported in many languages. As mentioned above, in the first quarter of 2016, a new variant of the CTB-Locker that targets web servers only was discovered. It has already successfully encrypted web-root files in more than 70 servers located in 10 countries.
The Trojan-Ransom.Win32.Cryptodef family also known as Cryptowall came third. Its representatives, as in the case of Teslacrypt, are spread via spam mass mailings.
In fifth place is the Scatter family. Earlier this year, a new wave of proliferation involving this encryptor via spam mailings was registered. The emails contained a link to a JS script that was masked in order to make the user download and launch it locally. Interestingly, when the script runs, in addition to Scatter, it saves two other malicious programs to the disk: Nitol (DDoS-bot) and Pony (a Trojan designed to steal information, mostly passwords).
The Locky family, which occupied seventh place in the Q1 rating, was notable for its wide geographic spread, mainly across Europe. Located on the Tor network, the site containing the criminals’ demands supports more than two dozen languages, which doesn’t include Russian or other CIS languages. This may mean that cybercriminals are not interested in attacking victims in these countries, something that is confirmed by the KSN statistics.Statistics
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.Mobile threats
Cybercriminals continue to improve new techniques for deceiving users. This quarter, we identified two mobile Trojans that counter standard security mechanisms used by operating systems. One version of Trojan-Banker.AndroidOS.Asacub overlays the regular system window requesting device administrator privileges with its own window containing buttons. The Trojan thereby conceals the fact that it is gaining elevated privileges in the system from the user, and tricks the user into approving these privileges. Another Trojan using a similar method is Trojan-SMS.AndroidOS.Tiny.aw. In recent versions of Android the system asks for the user’s approval when an SMS is sent to a premium number. The Tiny SMS Trojan overlays this dialog with its own screen without covering the buttons in the original window.
Request screen of Trojan-SMS.AndroidOS.Tiny.aw overlaying a notification about the sending of an SMS to a premium-rate number (The message states: Would you like to send a request to receive a gaming database?)
The Trojan’s request is presented in such a way that the user will most probably agree to send the SMS to a premium-rate number without having the vaguest idea of what happened next.
In the Q3 2015 report we mentioned the banking Trojan Trojan-Banker.AndroidOS.Marcher. This quarter, we were able to detect new versions of Marcher which attacked nearly 40 banking apps, mostly belonging to European banks. Unlike most other mobile Trojans, Marcher uses phishing web pages rather than its own windows to overlay banking app screens.
In Q1, we saw an increase in activity by the mobile ransomware Trojan-Ransom.AndroidOS.Fusob.pac, which blocks the user’s device and demands a ransom for decryption. In the first three months of 2016, Fusob became the most popular mobile Trojan of this type – it accounted for over 64% of users attacked by mobile ransomware. The total number of users attacked by mobile ransomware Trojans increased more than 1.8 times compared to the previous quarter.The number of new mobile threats
In Q1 2016, Kaspersky Lab detected 2,045,323 malicious installation packages – this is 11 times greater than in Q4 2015, and 1.2 times more than in Q3 2015.
Number of detected malicious installation packages (Q2 2015 – Q1 2016)Distribution of mobile malware by type
Distribution of new mobile malware by type, Q1 2016 vs. Q4 2015
In Q1 2016, adware programs continued to top the rating of detected malicious objects for mobile devices. The share of adware programs grew 13 p.p. compared to Q4 2015, and reached 42.7%. Notably, this is lower than in Q3 2015 (52.5%).
Second place is occupied by an SMS Trojan, and it is the second quarter in a row that we have seen a growth in the share of detections of this type of object. In Q4 2015, the share of SMS Trojans rose dramatically from 6.2% to 19.8%, and grew by another 0.7 p.p. in Q1 2016, and amounted to 20.5%.
Trojan spyware programs, with a 10% share, were right behind the SMS Trojans. These programs steal the user’s personal data, including incoming messages (mTANs) from banks.
RiskTool software, or legal applications that are potentially dangerous to users, had occupied the first or second position in this rating for nearly two years. However, starting in Q4 2015 they fell to the fifth place. In Q4 2014, there share was 5.6%, and in Q1 2016 7.4%.
The share of banking Trojans has continued to grow, and amounted to 1.2% in Q1 2016.TOP 20 mobile malware programs
Please note that this ranking of malicious programs does not include potentially dangerous or unwanted programs such as RiskTool or adware.Name % of attacked users* 1 DangerousObject.Multi.Generic 73.7 2 Backdoor.AndroidOS.Ztorg.c 11.3 3 Trojan.AndroidOS.Iop.c 8.9 4 Trojan.AndroidOS.Ztorg.a 8.7 5 Trojan-Ransom.AndroidOS.Fusob.pac 6.2 6 Trojan-Dropper.AndroidOS.Agent.ar 4.6 7 Trojan-Clicker.AndroidOS.Gopl.a 4.5 8 Backdoor.AndroidOS.Ztorg.b 4.3 9 Trojan.AndroidOS.Iop.m 3.7 10 Trojan.AndroidOS.Agent.ej 3.7 11 Trojan.AndroidOS.Iop.q 3.5 12 Trojan.AndroidOS.Ztorg.i 3.3 13 Trojan.AndroidOS.Muetan.b 3.1 14 Trojan.AndroidOS.Agent.gm 3.1 15 Trojan-SMS.AndroidOS.Podec.a 3.1 16 Trojan-Downloader.AndroidOS.Leech.a 3.0 17 Trojan-Dropper.AndroidOS.Guerrilla.b 2.8 18 Exploit.AndroidOS.Lotoor.be 2.8 19 Backdoor.AndroidOS.Ztorg.a 2.8 20 Backdoor.AndroidOS.Triada.d 2.4
* Percentage of users attacked by the malware in question, relative to all users attacked
First place is occupied by DangerousObject.Multi.Generic (44.2%), used for malicious programs detected by 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.
An increasing number of entries in the TOP 20 are occupied by Trojans that use advertising as their main means of monetization. Their goal is to deliver as much advertisements 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 Q1, 16 such programs made it into the TOP 20: three programs from the family Backdoor.AndroidOS.Ztorg, three from the family Trojan.AndroidOS.Iop, two from the family Trojan.AndroidOS.Ztorg, plus Trojan-Dropper.AndroidOS.Agent.ar, Trojan-Clicker.AndroidOS.Gopl.a, Trojan.AndroidOS.Agent.ej, Trojan.AndroidOS.Muetan.b, Trojan.AndroidOS.Agent.gm, Trojan-Downloader.AndroidOS.Leech.a, Trojan-Dropper.AndroidOS.Guerrilla.b, and Backdoor.AndroidOS.Triada.d.
Backdoor.AndroidOS.Triada is a new entry in the TOP 20 of mobile malware. The main function of this Trojan is to redirect financial SMS transactions when the user makes online payments to buy additional content in legitimate apps. The money goes to the attackers rather than to the software developer. Triada is the most complex mobile malware program that we know of. Its distinctive feature is the use of the Zygote process to implement its code in the context of all the applications on the device. Triada penetrates virtually all applications running on the infected device, and continues to exist in the RAM memory only. In addition, all the Trojan’s separately launched processes are concealed from the user and other applications.
The ransomware Trojan Trojan-Ransom.AndroidOS.Fusob.pac is in fifth place (6.2%). This Trojan demands a $200 ransom from victims to unblock their devices. A substantial number of the victims are located in North America (the US and Canada) and Europe (mostly in Germany, Italy, the UK, Spain and Switzerland).
Trojan-SMS.AndroidOS.Podec.a (3%) has spent over a year now in the mobile malware TOP 20, although now it is beginning to lose ground. Earlier it was consistently among the top 5 mobile threats, but in Q1 2016 it only made it into the bottom half of the rating. The number of users attacked by this Trojan fell 1.7 times compared to Q4 2015. Its functionality has remained practically unchanged; the main means of monetization is still achieved by subscribing the user to paid services.
Also making it into the rating is Exploit.AndroidOS.Lotoor.be, an exploit used to gain local super-user rights.The geography of mobile threats
The geography of mobile malware infection attempts in Q1 2016 (percentage of all users attacked)
Top 10 counties attacked by mobile malware (ranked by percentage of users attacked)Country* % of users attacked** 1 China 38.2 2 Bangladesh 27.6 3 Uzbekistan 21.3 4 Algeria 17.6 5 Nigeria 17,4 6 India 17.0 7 Philippines 15.7 8 Indonesia 15,6 9 Ukraine 15.0 10 Malaysia 14.0
* We eliminated countries from this ranking where the number of users of Kaspersky Lab’s mobile security product is lower than 10,000.
** Percentage of unique users attacked in each country relative to all users of Kaspersky Lab’s mobile security product in the country.
China topped the ranking, with 40% of users encountering a mobile threat at least once during the year. To recap, in 2015 China also came first in the ranting.
In all the countries of the Top 10 except for China the most popular mobile malware was the same – advertising Trojans that appeared in the TOP 20 mobile malware, and AdWare. In China, a significant proportion of attacks also involved advertising Trojans, but the majority of users encountered the Backdoor.AndroidOS.GinMaster and Backdoor.AndroidOS.Fakengry families. Representatives of the RiskTool.AndroidOS.SMSreg family were also popular. If used carelessly, these programs could result in money being withdrawn from a mobile account.
The safest countries are Taiwan (2.9%), Australia (2.7%) and Japan (0.9%).Mobile banking Trojans
Over the reporting period, we detected 4,146 mobile Trojans, which is 1.7 times more than in the previous quarter.
Number of mobile banking Trojans detected by Kaspersky Lab solutions (Q2 2015 – Q1 2016)
Geography of mobile banking threats in Q1 2016 (number of users attacked)
The number of attacked users depends on the overall number of users within each individual country. To assess the risk of a mobile banker Trojan infection in each country, and to compare it across countries, we created a country ranking according to the percentage of users attacked by mobile banker Trojans.
Top 10 counties attacked by mobile banker Trojans (ranked by percentage of users attacked)Country* % users attacked** 1 China 0.45 2 Australia 0.30 3 Russia 0.24 4 Uzbekistan 0.20 5 Ukraine 0.08 6 France 0.06 7 Byelorussia 0.05 8 Turkey 0.05 9 Japan 0.03 10 Kazakhstan 0.03
* We eliminated countries from this ranking where the number of users of Kaspersky Lab’s mobile security product is lower than 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 Q1 2016, first place was occupied by China where the majority of affected users encountered the Backdoor.AndroidOS.GinMaster and Backdoor.AndroidOS.Fakengry families of mobile banker Trojans. In second place was Australia where the Trojan-Banker.AndroidOS.Acecard family was replaced by the Trojan-Banker.AndroidOS.Marcher family as the most popular threat.
TOP 10 countries by the percentage of users attacked by mobile banking Trojans relative to all attacked users
An indication of how popular mobile banker Trojans are with cybercriminals in each country can be provided by the percentage of users who were attacked at least once by mobile banker Trojans during the quarter, relative to all users in the same country whose mobile security product was activated at least once in the reporting period. This ranking differs from the one above:Country* % users attacked** 1 Australia 13.4 2 Russia 5.1 3 United Kingdom 1.6 4 Turkey 1.4 5 Austria 1.3 6 France 1.3 7 Poland 1.2 8 China 1.1 9 Hong Kong 1 10 Switzerland 0.9
* We eliminated countries from this ranking where the number of users of Kaspersky Lab’s mobile security product is lower than 10,000.
** Percentage of unique users in each country attacked by mobile banker Trojans, relative to all unique users attacked by mobile malware in the country.
To recap, Australia was among the Top 3 countries with the lowest percentage of users attacked by mobile malware. However, in this ranking Australia ended in first place: more than 13% of all users attacked by mobile malicious programs were attacked by mobile bankers. Meanwhile China, which came first in the previous ranking, ended the quarter in tenth place. In other words, in China the cybercriminals’ mobile banking Trojans are less popular than other types of mobile malware.Mobile Trojan-Ransom
In Q1 2016, we detected 2,896 mobile ransomware samples, which is 1.4 times more than in the previous quarter.
>Number of mobile Trojan-Ransomware programs detected by Kaspersky Lab (Q2 2015 – Q1 2016)
TOP 10 countries attacked by Trojan-Ransomware as a percentage of attacked users:Country* % of users attacked ** 1 Kazakhstan 0.92 2 Germany 0.83 3 Uzbekistan 0.80 4 Canada 0.71 5 Italy 0.67 6 Netherlands 0.66 7 United Kingdom 0.59 8 Switzerland 0.58 9 USA 0.55 10 Spain 0.36
* We eliminated countries from this ranking where the number of users of Kaspersky Lab’s mobile security product is lower than 10,000.
** Percentage of unique users in each country attacked by mobile banker Trojans, relative to all users attacked by mobile malware in the country.
In all the countries of the TOP 10, except for Kazakhstan and Uzbekistan, the most popular Trojan-Ransom family was Fusob, especially its Trojan-Ransom.AndroidOS.Fusob.pac modification (note, this malicious program was fifth in the ranking of mobile threats).
In Kazakhstan and Uzbekistan, which came first and third respectively, the main threat to users originated from representatives of the Small family of mobile Trojan-Ransom. This is a fairly simple ransomware program that blocks operation of a device by overlaying all the windows on the device with its own window and demands $10 to unblock it.Vulnerable applications used by cybercriminals
In Q1 2016, exploits for Adobe Flash Player remained popular. During the reporting period two new vulnerabilities in this software were detected:
The first exploit pack to add support for these vulnerabilities was Angler.
One notable event in the first quarter was the use of an exploit for Silverlight – CVE-2016-0034. At the time of publication, this vulnerability is used by the Angler and RIG exploit packs.
As is now traditional, some popular packs included an exploit for the Internet Explorer (CVE-2015-2419) vulnerability.
The overall picture of the use of exploits in the first quarter looks as follows:
Distribution of exploits used in attacks by the type of application attacked, Q1 2016
As expected, we have seen a decline in the share of exploits for Java (-3 percentage points) and an increase in the use of Flash exploits (+1 p.p.). There was also a significant increase in the percentage of exploits for Microsoft Office (+10 p.p.): this group mainly includes exploits for vulnerabilities in Microsoft Word. This significant growth was caused by spam mailings containing these exploits.
Overall, the first quarter of 2016 continued the trend of the past few years – cybercriminals are focused on exploits for Adobe Flash Player and Internet Explorer. In our chart, the latter is included in the “Browsers” category together with detections of landing pages that “distribute” exploits.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 first quarter of 2016, Kaspersky Lab’s web antivirus detected 18,610,281 unique malicious objects: scripts, exploits, executable files, etc. 74,001,808 unique URLs were recognized as malicious by web antivirus components.Online threats in the banking sector
In the first three months of 2016, Kaspersky Lab solutions blocked attempts to launch malware capable of stealing money via online banking on 459,970 computers. We are witnessing a decline in financial malware activity: the figure for Q1 is 23.3% lower than in the previous quarter (597,415). A year ago, in Q1 2015 this figure was 699,652, which translates into a 34.26% fall in the number of victims over the past year.
Number of attacks by financial users, Q1 2016Geography of attacks
To evaluate and compare the degree of risk of being infected by banking Trojans worldwide, we calculate the percentage of Kaspersky Lab product users who encountered this type of threat during the reporting period in the country, relative to all users of our products in the county.
Geography of banking malware attacks in Q1 2016 (percentage of attacked users)
Top 10 countries by the percentage of attacked usersCountry* % attacked users** 1 Brazil 3.86 2 Austria 2.09 3 Tunisia 1.86 4 Singapore 1.83 5 Russia 1.58 6 Venezuela 1.58 7 Morocco 1.43 8 Bulgaria 1.39 9 Hong Kong 1.37 10 United Arab emirates 1.30
These statistics are based on the 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 (less than 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 Q1 2016, Brazil had the highest percentage of Kaspersky Lab users who were attacked by banking Trojans. One of the reasons for the growth of financial threats in this country was the emergence of cross-platform Trojan bankers. Noticeably, most countries in the TOP 10 have a high level of technological development and/or well-developed banking system which attracts cybercriminals.
In Russia, 1.58% of users encountered a banking Trojan at least once in Q1 (an increase of 1 p.p. compared to the previous quarter). In the US, the figure was 0.26%; Spain – 0.84%; Italy – 0.79%; Germany – 0.52%; the UK – 0.48%; France – 0.36%.The Top 10 banking malware families
The table below shows the Top 10 malware families most commonly used in Q1 2016 to attack online banking users:Name Number of users attacked 1 Trojan-Spy.Win32.Zbot 419940 2 Trojan-Downloader.Win32.Upatre 177665 3 Trojan-Banker.Java.Agent 68467 4 Trojan-Banker.Win32.Gozi 53978 5 Trojan-Banker.Win32.BestaFera 25923 6 Trojan.Win32.Tinba 24964 7 Trojan-Banker.Win32.Banbra 22942 8 Trojan-Banker.AndroidOS.Agent 19782 9 Trojan-Banker.AndroidOS.Abacus 13446 10 Trojan-Banker.Win32.ChePro 9209
Trojan-Spy.Win32.Zbot topped the ranking. It has become a permanent resident in this ranking, and it is no coincidence that it consistently occupies a leading position. The Trojans of the Zbot family were among the first to use web injections to compromise the payment details of online banking users and to modify the contents of banking web pages. They encrypt their configuration files at several levels; the decrypted configuration file is never stored in the memory in its entirety, but is instead loaded in parts.
The Trojan-Downloader.Win32.Upatre family of malicious programs came second in Q1 2016. The malware is no larger than 3.5 KB in size, and is limited to downloading the payload to the victim computer, most typically a banker Trojan from the Dyre/Dyzap/Dyreza family. The main aim of this family of banking Trojans is to steal the user’s payment details. Dyre does this by intercepting the data from a banking session between the victim’s browser and the online banking web app, in other words, it uses the “Man-in-the-Browser” (MITB) technique.
It is worth noting that the vast majority of the TOP 10 malware uses the technique of embedding arbitrary HTML code in the web page displayed by the browser and intercepting payment data entered by the user into the original and the inserted web forms.
The TOP 3 threats in the first quarter of 2016 include cross-platform banking malware written in Java. Brazilian cybercriminals have started actively using cross-platform Java Trojans. In addition, Kaspersky Lab experts detected new malicious software also written in Java and used to steal financial information – Adwind RAT. Adwind is written entirely in Java, which is why it can attack all popular platforms: Windows, Mac OS, Linux and Android. The malicious program allows attackers to collect and extract data from the system, as well as remotely control an infected device. To date, it is able to take screenshots, memorize keystrokes, steal passwords and data stored in browsers and web forms, take photos and videos via the webcam, make audio recordings using the microphone built into the device, collect general data about the user and the system, steal VPN certificates and keys from crypto currency wallets and, finally, manage SMS.
Fourth place in the TOP 10 is occupied by Trojan-Banker.Win32.Gozi, which penetrates working processes of popular web browsers to steal payment information. Some samples of this Trojan can infect the MBR (Master Boot Record) and maintain their presence in the operating system, even if it has been reinstalled.
One of the most interesting pieces of malware designed to steal financial data that did not make it into the TOP 10 is Gootkit. It is written using the software platform NodeJS and has a modular architecture. The malicious code interpreter is contained in its body; as a result, it is big – approximately 5 MB. To steal payment data, Gootkit uses http traffic interception and embeds itself in the browser. Other standard Trojan features include execution of arbitrary commands, auto-update, and capturing screenshots. However, this banking Trojan is not particularly widespread.Top 10 countries where online resources are seeded with malware
The following statistics are based on the physical location of the online resources that were 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 up against their actual domain IP addresses, and then the geographical location of a specific IP address (GEOIP) is established.
In Q1 2016, Kaspersky Lab solutions blocked 228,420,754 attacks launched from web resources located in 195 countries around the world. 76% of notifications on blocked web attacks were triggered by attacks coming from web resources located in 10 countries.
Distribution of web attack sources by country, Q1 2016
Q1 saw the Netherlands take over first place (24.6%) from the US (21.44%). Russia (7.45%) and Germany (6%), which followed them, also swapped places. Vietnam has dropped out the Top 10, while Bulgaria is a newcomer in eighth place with 1.75%.
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.Country* % of unique users attacked ** 1 Russia 36.28 2 Kazakhstan 33.19 3 China 32.87 4 Azerbaijan 30.28 5 Ukraine 29.96 6 Belarus 29.16 7 Slovenia 26.88 8 Armenia 26.27 9 Vietnam 25.14 10 Moldova 24.68 11 Kyrgyzstan 24.46 12 Spain 24.00 13 India 23.98 14 Brazil 23.68 15 Italy 22.98 16 Algeria 22.88 17 Lithuania 22.58 18 Croatia 22.04 19 Turkey 21.46 20 France 21.46
These statistics are based on the 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 (fewer than 10,000 users).
** Unique users whose computers have been targeted by web attacks as a percentage of all unique users of Kaspersky Lab products in the country.
The leader of this ranking remained unchanged – it is still Russia with 36.3%. Since the previous quarter, Chile, Mongolia, Bulgaria and Nepal have left the Top 20. Newcomers to the ranking are Slovenia (26.9%), India (24%) and Italy (23%).
The countries with the safest online surfing environments included Germany (17.7%), Canada (16.2%), Belgium (14.5%), Switzerland (14%), the US (12.8%), the UK (12.7%), Singapore (11.9%), Norway (11.3%), Honduras (10.7%), the Netherlands (9.6%) and Cuba (4.5%).
On average, 21.42% of computers connected to the Internet globally were subjected to at least one web attack during the three months. This is a fall of 1.5 p.p. compared to Q4 2015.Local threats
Local infection statistics for users computers are a very important indicator: they reflect threats that have penetrated computer systems using means other than the Internet, email, or network ports.
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 Q1 2016, Kaspersky Lab’s file antivirus detected a total of 174,547,611 unique malicious and potentially unwanted objects.Countries where users faced the highest risk of local infection
For each of the countries, we calculated the percentage of Kaspersky Lab product users on whose computers the file antivirus had been triggered during the quarter. These statistics reflect the level of personal computer infection in different countries.
Top 20 countries with the highest levels of computer infectionCountry* % of unique users** 1 Somalia 66.88% 2 Yemen 66.82% 3 Armenia 65.17% 4 Kyrgyzstan 64.45% 5 Russia 64.18% 6 Tajikistan 64.06% 7 Bangladesh 63.00% 8 Vietnam 61.31% 9 Afghanistan 60.72% 10 Kazakhstan 60.62% 11 Nepal 59.60% 12 Uzbekistan 59.42% 13 Ethiopia 59.23% 14 Ukraine 58.90% 15 Byelorussia 58.51% 16 Laos 58.46% 17 Rwanda 58.10% 18 Iraq 57.16% 19 Algeria 57.50% 20 Moldova 56.93%
These statistics are based on the 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 (fewer than 10,000 users).
** The percentage of unique users in the country with computers that blocked local threats as a percentage of all unique users of Kaspersky Lab products.
Somalia became the new leader of this rating in Q1, with 66.9%. Bangladesh, the leader for the past few quarters, dropped to seventh place (63.6%). Newcomers to this ranking are Uzbekistan in 12th place (59.4%), Ukraine in 14th place (58.9%), Belarus in 15th place (58.5%), Iraq in 18th place (57.2%) and Moldova in 20th (57.0%).
The safest countries in terms of local infection risks were the Czech Republic (27.2%), Denmark (23.2%) and Japan (21.0%).
An average of 44.5% of computers globally faced at least one local threat during Q1 2016, which is 0.8 p.p. more than in Q4 2015.
Infecting the Master Boot Record (MBR) and encrypting files is nothing new in the world of malicious programs. Back in 1994, the virus OneHalf emerged that infected MBRs and encrypted the disk contents. However, that virus did not extort money. In 2011, MBR blocker Trojans began spreading (Trojan-Ransom.Win32.Mbro) that infected the MBR and prevented the operating system from loading further. The victim was prompted to pay a ransom to get rid of the problem. It was easy to treat a system infected by these blocker Trojans because, apart from the MBR, they usually didn’t encrypt any data on the disk.
Today, we have encountered a new threat that’s a blast from the past. The Petya Trojan (detected by Kaspersky Lab products as Trojan-Ransom.Win32.Petr) infects the MBR preventing normal system loading, and encrypts the Master File Table (MFT), an important part of the NT file system (NTFS), thus preventing normal access to files on the hard drive.The infection scenario
The people spreading Petya attack their potential victims by sending spam messages containing links that download a ZIP archive. The archive contains the Trojan’s executable file and a JPEG image. The file names are in German (Bewerbungsunterlagen.PDF.exe, Bewerbungsmappe-gepackt.exe), are made to look like resumes for job candidates, and target HR staff in German-speaking countries.
Contents of the archives downloaded from links in spam
The cybercriminals didn’t bother with automatic escalation of privileges – the manifest of the Trojan’s executable file contains the following standard record:
If the user launches the malicious executable file Petya, Windows will show the standard UAC request for privilege escalation. If the system has been properly configured by the system administrators (i.e. UAC is enabled, and the user is not working from an administrator account), the Trojan won’t be able to run any further.
Unfortunately, a user who has the privileges to agree to a UAC request often underestimates the potential risks associated with launching unknown software with elevated rights.How it works The executable file and the packer
A Petya Trojan infection begins with the launch of the malicious executable file. The samples of the Trojan that Kaspersky Lab received for analysis are, just like most other malware samples, protected with a customized packer. When the executable file launches, the malicious packer’s code begins to work – it unpacks the malicious DLL Setup.dll into a newly designated RAM area, and then passes control to it.
Cybercriminals typically use packers to avoid detection – circumvent static signatures, trick the heuristic analyzer, etc. While investigating the Petya packer, we noticed an unusual trick used by the cybercriminals.
Cybercriminals often try to create the packer in such a way that a packed malicious executable file looks as similar as possible to a regular legitimate file. Sometimes, they take a legitimate file and substitute part of the code with malicious code. That’s what they did with Petya, with one interesting peculiarity: it was a part of the standard compiler-generated runtime DLL that was replaced with malicious code, while the function WinMain remained intact. The illustration below shows the transition, beginning from the entry point (“start”). As can be seen, the function of unpacking malicious code (which we dubbed “evil”) is called from the legal function __calloc_crt which is part of the runtime code.
Diagram of transitions between the malicious packer’s functions
Why do it that way? Obviously, the creators of the malicious packer were trying to trick an inattentive researcher or automatic analyzers: the file looks legitimate – WinMain doesn’t contain malicious code – so it’s possible that it will be overlooked. Besides, if the breakpoint is set at WinMain during debugging, then the malicious code works (and sends the system into BSOD, as we will discuss later in detail) and execution is over before the breakpoint is even reached.
Kaspersky Lab has detected Petya samples that masquerade as legitimate files written in C/C++ and in Delphi.The malicious DLL
Setup.dll is a DLL with just one export: _ZuWQdweafdsg345312@0. It is written in C and compiled in Microsoft Visual Studio. The cybercriminals used an implementation of cryptographic algorithms available in the public library mbedtls (formerly polarssl). Setup.dll is not saved to the hard drive as a separate file, but always remains in the RAM.
When Setup.dll receives control, it decrypts the data contained in the section ‘.xxxx’ and then proceeds to infect the victim computer.
The encrypted ‘.xxxx’ section containing data
Fragment of the decrypted data from the ‘.xxxx’ section
At a higher degree of abstraction, the actions of Setup.dll come down to the following:
- Re-write the boot record on the hard drive with its own malicious loader;
- Generate a key, infection ID and other auxiliary information, and save them to the hard drive;
- Cause a system abort and reboot, thereby passing control to the malicious loader.
Now let’s look in detail at how all of this is implemented in the Trojan. But before doing so, we need to define the terminology used.
Hard disk sector – the minimum addressable unit of a hard drive, typically 512 bytes.
Master boot record (MBR) – the code and the data written to Sector 0. After hardware is initialized, this code is used to boot the PC. Also, this sector contains the hard disks’ partition table. A disk partitioned with MBR may have up to four primary partitions, and the maximum partition size is ~2.2 TB.
GUID Partition Table (GPT) – a more modern standard of hard drive layout. It supports up to 128 partitions, each up to 9.4 ZB in size (1 ZB = 1021 bytes.)
Now let’s return to the Trojan under review. Setup.dll can infect disks partitioned according to either the older MBR standard or the more modern GPT standard. There are two alternative branches of execution sequences in the malicious program; the choice of execution branch depends on the data in the field PartitionStyle of the structure PARTITION_INFORMATION_EX.
Selection of the execution branch for disk infection, depending on whether the disk has MBR or GPT partitioningInfecting an MBR disk
When infecting an MBR disk, Setup.dll performs the following actions:
- Encrypts sector 0 (the original code and the MBR data) with the simple operation XOR 0x37 (ASCII ‘7’), writes the result to sector 56;
- Encrypts sectors 1-33 with the same operation XOR 0x37;
- Generates configuration data for the malicious loader, writes them to sector 54;
- Creates the verification sector 55 populated with the repeating byte 0x37;
- Copies the disk’s NT signature and the partition table saved from the original MBR into its own first-level loader; writes first-level malicious code to sector 0 of the disk, and writes second-level code to sectors 34-50 (referred to here as the malicious loader);
- Calls the function NtRaiseHardError, which causes the operating system to crash (BSOD – the ‘blue screen of death’).
When an MBR disk has been infected, the beginning of the disk has the following structure:Number of sector Content 0 First-level malicious loader 1 – 33 Encrypted sectors 1-33 (XOR 0x37) 34 – 50 Second-level malicious code … 54 Configuration sector of the malicious program 55 Verification sector (populated with byte 0x37) 56 Encrypted original MBR code (XOR 0x37) Infecting a GPT disk
When infecting a GPT disk, Setup.dll performs more actions:
- Based on Primary GPT Header data, it receives the address of GPT header copy;
- Encrypts the GPT header copy with XOR 0x37;
- Performs all the actions that are performed when encrypting an MBR disk.
When a GPT disk has been infected, the beginning of the disk has the following structure:Number of sector Content 0 First-level malicious loader 1 – 33 Encrypted sectors 1-33 (XOR 0x37) 34 – 50 Second-level malicious code … 54 Configuration sector of the malicious program 55 Verification sector (populated with byte 0x37) 56 Encrypted original MBR code (XOR 0x37) … Backup LBA –
Backup LBA + 33 Encrypted copy of GPT Header (XOR 0x37) Generation of configuration data
In the configuration sector (sector 54), the Trojan keeps the data it needs to encrypt MFT and decrypt it if the victim pays the ransom. Generation of the configuration data consists of the following steps:
- Setup.dll generates a random string that is 16 characters long [1-9, a-x, A-X]; we will call this string password;
- Generate a pair of keys: ec_session_priv (a private key, a random large integer number) + ec_session_pub (public key, a point on a standard elliptic curve secp192k1);
- Calculate the session secret: session_secret = ECDH (ec_session_priv, ec_master_pub); the cybercriminals’ public key ec_master_pub is contained in the Trojan’s body;
- Calculate the aes_key = SHA512(session_secret) – only the first 32 bytes of the hash sum are used;
- Encrypt the ‘password’ string by XORing it with the first 16 bytes of ec_session_pub: password_xor = ec_session_pub[0, 15] xor password;
- Encrypt the result using AES-256 with the key aes_key: password_aes_encr = AES_enc(password_xor);
- Create the array ec_session_data = [ec_session_pub, password_aes_encr];
- Calculate base58: ec_session_data_b58 = base58_enc(ec_session_data);
- Use the result to calculate SHA256: digest = sha256(ec_session_data_b58);
- Create array: ec_data = [check1, check2, ec_session_data_b58], where check1, check2 are bytes calculated by the formulas:
a = digest & 0xF;
b = (digest & 0xF) < 10;
check1 = (digest >> 4) + 0x57 + ((digest >> 4) < 10 ? 0xD9 : 0);
check2 = a + 0x57 + (b ? 0xD9 : 0);
- Based on the ‘password’, create a key for MFT encryption;
- Generate IV – 8 random bytes which will be used during MFT encryption;
- Generate infection ID and use it to create “personalized” URLs for ransom payment webpages.
Pseudocode creating a key for MFT encryption
Ultimately, the configuration data structure looks like this:
In C language syntax, this structure can be presented as follows:
This is what the configuration data looks like after it is written to the hard drive:
Note that if the user turns off their computer after this stage and doesn’t switch it on again, only minimum damage will be done, as it is not difficult to decrypt data encrypted with 1-byte XOR. Therefore, a good piece of advice: if you launch an unknown file and your system suddenly crashes, showing a blue screen, you should switch off your computer and get help from a qualified specialist. The specialist should be able to identify a Petya infection and restore the disk sectors encrypted with XOR.
If, however, the computer was re-booted, then the Trojan’s third stage kicks in – the malicious code written to sectors 0 and 34–50.The malicious loader
After rebooting, the code in sector 0 (the first-level loader) gains control. It loads the main second-level malicious code from sectors 34–50 into the memory and passes control to it. This code, in turn, receives information about the hard drives available in the system, searches for the disk where the configuration is written, reads the configuration data from sector 54 and, depending on the value in the field ‘config.state’, begins encryption (if the value is 0) or asks the user to enter the decryption key that they have purchased (if the value is 1).
Fragment of code implementing the Trojan’s logicEncryption of MFT
The master file table (MFT) is a data structure with information about every file and directory on a volume formatted into NTFS, the file system that is used in all modern versions of Windows. The table contains the service data required to find each file on the disk. It can be compared to a table of contents in a book that tells you on which page to find a chapter. Similarly, MFT indicates which logical cluster a file is located in.
It is namely this critical area that is attacked by Petya. If the value of ‘config.state’ is equal to 0 during launch, it does the following:
- Displays a fake disk check message:
- Reads the key ‘config.salsa_key’ from the configuration sector into a local array; sets this field to zero on the disk, sets ‘config.state’ field at 1;
- Encrypts the verification sector 55 with the stream cipher Salsa20; this sector is populated beforehand with the byte 0x37 (see the section ‘Infecting an MBR disk’ above);
- Searches for each partition’s MFT on each connected hard drive;
- Encrypts the MFT data with cipher Salsa20. Encryption is performed in parts of 8 sectors (i.e. the size of each part is 4 KB). A counter of the encrypted parts is kept in sector 57 of the first disk.
- When encryption is over, it triggers a system reboot.
After the reboot, Petya displays an animated image of a flashing red and white skull drawn in ACCII-art style.
If the user presses any key, the Trojan displays a text which tells the victim in no uncertain terms what has happened.Ransom demand and decryption
On this screen Petya displays links to the ransom payment webpages located in the Tor network (the addresses are specified in config.mal_urls), and the “personal decryption code” which the victim has to enter at either of the above sites. In reality, this “code” is the content of the field ‘config.ec_data’, hyphenated every six characters.
So, how do the cybercriminals plan to decrypt MFT, and are they even capable of doing so?
The ‘Key:’ field on this screen accepts a text string from the user. This string is checked for length (a 16-character long string is required), and then the Trojan uses it to calculate a 32-byte ‘salsa_key’ (following the algorithm discussed above in the section ‘Generation of configuration data’). The Trojan then attempts to decrypt the verification sector 55 with this key, and checks that the decrypted sector is completely populated with the byte 0x37. If it is, the key is considered correct, and Petya uses it to decrypt MFT. Then it decrypts all starting sectors encrypted with XOR 0x37, decrypts the original MBR and prompts the user to reboot the computer.
Thus, the correct string to be entered in the ‘Key:’ field is that very same ‘password‘ string that is generated in the first step when the configuration data is created.
Screen message displayed after successful decryption
The question remains: how do the cybercriminals know this string so they can communicate it to a victim who has paid the ransom? No automatic communication with C&C servers is established during the entire infection life cycle. The answer lies in the description of the algorithm for generating configuration data.
The victim is prompted to manually enter their “personal decryption code” ec_data on the ransom payment webpage. The cybercriminal can then perform the following actions:
- Decode base58: base58_dec(ec_session_data_b58) = ec_session_data = [ec_session_pub, password_aes_encr]
- Calculate session_secret = ECDH(ec_session_pub, ec_master_priv), in accordance with the Elliptic curve Diffie–Hellman properties, where ec_master_priv is a private key known to the Trojan’s creators only;
- Calculate aes_key = SHA256(session_secret);
- Decrypt AES-256: password_xor = AES_dec(password_encr);
- Knowing ec_session_pub, calculate the original password based on password_xor.
When we visit the Tor site at the URL provided by the Trojan, we see a page that requires a CAPTCHA to be entered, after which the main ransom payment page is loaded. The design of the page immediately catches the eye, with its hammer and sickle and the word ‘ransomware’ in pseudo-Cyrillic. It looks like a USSR parody along the lines of the game Red Alert.
This page displays a countdown clock showing when the ransom price will be doubled, as well as regularly updated links to news and publications related to Petya.
When the ‘Start the decryption process’ button is pressed, you end up on a page that asks you to enter the value of ‘ec_data’, which is now called “your identifier” rather than “your personal decryption code”. It looks like the cybercriminals still haven’t decided what to call this part.
When the user enters this string, the site displays the amount of ransom in BTC, information on how to purchase bitcoins, and the address where the money should be sent.
As well as that, there are two other pages on the website: FAQ and Support.
The FAQ page
The FAQ page is interesting in that it contains false information: in reality, RSA is not used by the Trojan in any way, at any stage of infection.
The Support page
On the Support page, the user is given the option of sending a message to the cybercriminals. One phrase in particular stands out: “Please write your message in english, our russian speaking staff is not always available”. This implies that there is at least one person in the group who speaks Russian.Geographic distribution
As we noted above, the spam messages target German-speaking victims. KSN statistics clearly show that Germany is the main target for the cybercriminals.
TOP 5 countries attacked by Petya Trojan by the number of attacked users:Country Number of attacked users 1 Germany 579 2 China 19 3 India 8 4 Japan 5 5 Russian Federation 5 Conclusion
After analyzing the Petya Trojan, we discovered that it is an unusual hybrid of an MBR blocker and data encryptor: it prevents not only the operating system from booting but also blocks normal access to files located on the hard drives of the attacked system.
Although Petya is noticeably different from the majority of ransomware that has emerged in the recent years, it can hardly be described as a fundamentally new development. The ideas behind the Trojan have been seen before in earlier malware; the creators of Petya have simply combined them all in a single creation. That said, it should be acknowledged that it requires a certain degree of technical skill to implement a low-level code to encrypt and decrypt data prior to OS booting.
Another interesting peculiarity about Petya is the pseudo-Soviet graphic design on the ransom payment website; the name of the Trojan also fits into the image of a “Russian Trojan” designed by cybercriminals. There is no certainty as to whether the Trojan’s creators originally come from Russia or other former Soviet states; however, the text on the payment page suggests there is at least one Russian speaker in the gang.
Kaspersky Lab’s products protect users from this threat: Petya’s executable files are detected with the verdict Trojan-Ransom.Win32.Petr; in addition, the behavior analyzer proactively detects even unknown versions of this Trojan with the verdict PDM:Trojan.Win32.Generic.P.S. How to decrypt your data without paying the ransom
On April 8, some independent researchers reported that they had found a method of restoring the password without paying the ransom to the cybercriminals. The method is based on a genetic algorithm; with the 8-byte long IV (stored in configuration sector 54) and the content of the encrypted verification sector 55, you can calculate the value of the password that generates the salsa key, which can then be used to decrypt the MFT.
We have selected the events from the first quarter of 2016 that, in our view, illustrate the main trends in the field of DDoS attacks and the tools used to perform them.A record-breaking reflection DDoS attack
DDoS attacks using amplification/reflection techniques are still popular and allow cybercriminals to break their peak power records. From a technical point of view, amplification methods are nothing new in DDoS attacks, but cybercriminals are discovering new ways and resources to enhance the capacity of their botnets. For example, according to a recently published report, 2015 saw the largest ever DDoS attack on record at 450-500 Gbps.DDoS attack on Trump
It’s possible that last year’s record didn’t last very long – at the very beginning of the year the official website of Donald Trump’s election campaign were subjected to DDoS attacks whose strength, according to unconfirmed sources, reached 602 Gbps. The hacktivist group New World Hacking claimed responsibility for both incidents.Use of the DNSSEC protocol
Criminals are increasingly using the DNSSEC protocol to carry out DDoS attacks. The protocol is intended to minimize DNS spoofing attacks, but besides the domain data a standard DNSSEC reply also contains additional authentication information. Thus, unlike a standard DNS reply of 512 bytes, the DNSSEC reply comes to about 4096 bytes. Attackers exploit this feature to perform amplification DDoS attacks. They usually use domains in the government zone .gov, because in the US such domains are required by law to maintain DNSSEC.Pingback attacks on WordPress
Web resources powered by the WordPress content management system (CMS) are still popular with cybercriminals carrying out DDoS attacks. Popular CMS-based resources often become targets of DDoS attacks exploiting the WordPress pingback function. The pingback function notifies the author of a post published on a WordPress site when someone else links to that post on another site running the same CMS. If the administrator of the site running WordPress has enabled the function, all links leading to the materials published on a site can perform a so-called pingback, i.e. send a special XML-RPC request to the original site. A huge number of pingback requests sent to the original site can cause a “denial of service”. This feature continues to attract the attention of cybercriminals and helps them perform DDoS attacks at the application level.Linux Mint hacking
On 21 February 2016, the head of Linux Mint, Clement Lefebvre, reported that someone had managed to hack the project infrastructure including its official website and forum, and substituted the link to the legitimate ISO image of the Linux Mint 17.3 Cinnamon edition with their own URL. The hacker’s modified ISO contained malicious code that used infected machines to perform DDoS attacks.Attacks on security companies
Cybercriminals also target companies working in information security, with most of the major players – especially those offering anti-DDoS services – having to regularly combat DDoS attacks on their resources. These attacks can’t cause much damage because all these resources are well-protected, but that doesn’t stop the cybercriminals.
In Q1 2016, resources in 74 countries were targeted by #DDoS attacks #KLreportTweet
In general, cybercriminals don’t go all out to bring down an IT security company’s site. The attacks tend not to last long, and in most cases, they are terminated as soon as the source notices that protection systems are working. The cybercriminals don’t want to waste their botnet resources when they could be earning money elsewhere. Nevertheless, the attacks continue.
Analysis of the correspondence on underground forums suggests that the criminal fraternity uses the websites of IT security companies as test bed, i.e. to test new methods and tools. This approach is no worse than others, but it does give us some valuable information. If worldwide DDoS statistics show the current state of things, then attacks on IT security companies allow us to some extent to predict the future of DDoS.
Data on the tactics, strength and types of attacks targeting Kaspersky Lab sites also allows us to forecast the trends in the DDoS industry for the coming months.
Once again, we have had to deal with amplification attacks. Their number has declined slightly compared to last year, but their maximum strength has increased fourfold. This confirms the trend of a general strengthening of these attacks – the criminals have to increase the strength to overcome protection measures used by Internet providers and information security companies. In our case, none of these attacks led to our sites being unavailable.
In Q1 2016, 93.6% of resources targeted by #DDoS attacks, were located in 10 countries #KLreportTweet
Considering the number of attacks on Kaspersky Lab resources in the first quarter of 2016, the “cream” of the cybercriminal community has gone back to the good old methods of attacks at the application level. Already in the first quarter of this year, we combated several times more HTTP(s) attacks than we did in the whole of 2015. Interestingly, there were several application-layer attacks performed simultaneously against a number of Kaspersky Lab resources. The strength of the DDoS resources was spread between several targets, reducing the effect on each target. This is most probably because the aim was not to disrupt Kaspersky Lab’s sites but to test tools and to see how we responded. The longest attack of this type lasted less than six hours.
We can assume that the proportion of Data Link layer attacks will gradually decline, and application-layer and multi-layer attacks (a combination of hardware and application-layer attacks) will come to the fore.
Powerful UDP amplification attacks came into general use a few years ago and are still a favorite tool of cybercriminals. The reasons for their popularity are clear: they are relatively easy to perform, they can be very powerful with a relatively small botnet, they often involve a third party, and it is extremely difficult to detect the source of the attack.
Although in Q1 of 2016 our Kaspersky DDoS Prevention service continued to combat UDP amplification attacks, we believe that they will gradually disappear. The once daunting task of combining the efforts of Internet providers and IT security companies to effectively filter the junk traffic generated by UDP attacks is almost solved. Having faced the risk of their main channels being clogged up due to large volumes of UDP packets, providers have acquired the necessary equipment and skills and cut this traffic off at the root. This means amplification attacks on a Data Link Layer are becoming less effective and, as a result, less profitable.
In Q1 2016, the largest numbers of #DDoS attacks targeted victims in #China, the #USA & #SouthKorea #KLReportTweet
To execute application-layer attacks on web services, large botnets or several high-performance servers and a wide output channel are required, as well as thorough preparatory work to study the target and find its vulnerabilities. Without this, they are ineffective. If the application-layer attack is carried out properly, it is difficult to counter it without blocking access to legitimate users – malicious requests look authentic and every bot faithfully fulfills the connection procedure. The only anomaly is the high demand for the service. We registered these sorts of attempts in the first quarter. This suggests that the DDoS market has developed so that complex, expensive attacks are becoming cost-effective, and better qualified cybercriminals are trying to make money using them.
Moreover, there is a real danger of these methods being used by cybercriminals en masse – the more popular the technique, the more tools are offered for it on the black market. And if application-layer attacks really do become widespread, we should expect to see a growth in the number of customers for this type of DDoS attack and more competent attackers.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.
The DDoS Intelligence system (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 first 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 longest #DDoS attack in Q1 2016 lasted for 197 hours (or 8.2 days) #KLreportTweet
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 that were detected and analyzed by Kaspersky Lab. It should also be highlighted 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.Q1 Summary
- In Q1, resources in 74 countries were targeted by DDoS attacks (vs. 69 in Q4 of 2015).
- 93.6% of the targeted resources were located in 10 countries.
- China, the US and South Korea remained the leaders in terms of number of DDoS attacks and number of targets. France and Germany were newcomers to the Top 10.
- The longest DDoS attack in Q1 2016 lasted for 197 hours (or 8.2 days) which is far less than the previous quarter’s maximum (13.9 days). Multiple attacks on the same target became more frequent (up to 33 attacks on one resource during the reporting period).
- SYN DDoS, TCP DDoS and HTTP DDoS remain the most common DDoS attack scenarios, while the number of UDP attacks continues to fall from quarter to quarter.
- Overall, command servers remained located in the same countries as the previous quarter, but Europe’s contribution increased – the number of C&C servers in the UK and France grew noticeably.
In Q1 2016, the geography of DDoS attacks narrowed to 74 countries.
93.6% of targeted resources were located in 10 countries.
Distribution of DDoS attacks by country, Q1 2016 vs. Q4 2015
The Top 3 most targeted countries remained unchanged. However, South Korea’s share grew from 18.4% to 20.4% while the US’s contribution dropped by 2.2 percentage points. Also of note is the fact that Q1 2016 saw an increase in the number of attacks targeting resources in Ukraine – from 0.3% to 2.0%.
The statistics show that 94.7% of all attacks had targets within the Top 10 most targeted countries:
Distribution of unique DDoS attack targets by country, Q1 2016 vs. Q4 2015
The number of targets in South Korea increased by 3.4 percentage points. China’s share fell from 50.3% in Q4 2015 to 49.7% in the first three months of 2016. The percentage of DDoS attacks targeting resources in the United States also decreased (9.6% in Q1 2016 vs. 12.8% in Q4 2016). Despite the change in figures, South Korea, China and the US maintained their positions in the Top 3, coming well ahead of all other countries.
SYN #DDoS, TCP DDoS & HTTP DDoS remain the most common DDoS attack scenarios in Q1 2016 #KLreportTweet
The first quarter of 2016 saw Ukraine enter the Top 5 DDoS targets: its share grew from an insignificant 0.5% at the end of last year to 1.9% in Q1 2016.
Taiwan and the Netherlands’ share fell 0.8 and 0.7 percentage points respectively, meaning both dropped out of the Top 10 most attacked countries.Changes in DDoS attack numbers
In Q1 2016, DDoS activity was distributed more or less evenly, with the exception of one peak on 6 February. The peak number of attacks in one day was 1,272, recorded on 31 March.
Number of DDoS attacks over time* in Q1 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.
As in the previous quarter, Monday (16.5% of attacks) was the most active day of the week for DDoS attacks. Thursday moved up to second (16.2%). Tuesday, which was in second place in Q4 2015 (from 16.4% to 13.4%), became the quietest day of the week in terms of DDoS attacks.
Distribution of DDoS attack numbers by day of the weekTypes and duration of DDoS attacks
The ranking of the most popular attack methods remained constant from quarter to quarter. Those used most often were the SYN DDoS method, although its share fell compared to the previous quarter (57.0% vs 54.9%), and TCP DDoS which fell by 0.7 percentage point. The proportion of ICMP DDoS attacks grew significantly, rising to 9%; however, it did not affect the order of the Top 5.
Distribution of DDoS attacks by type
Noticeably, the figure for UDP DDoS has fallen continually over the last year: from 11.1% in Q2 2015 to 1.5% in Q1 2016.
Like the previous quarter, about 70% of attacks lasted no more than 4 hours. At the same time, the maximum duration of attacks decreased considerably. The longest DDoS attack in the last quarter of 2015 lasted for 333 hours; in Q1 2016, the longest registered attack ended after 197 hours.
Distribution of DDoS attacks by duration (hours)C&C servers and botnet types
In Q1, South Korea remained the leader in terms of the number of C&C servers located on its territory, with its share growing from 59% in the previous quarter to 67.7% in the first quarter of 2016.
China came second; its share grew from 8.3% to 9.5%. As a result, China pushed the US down to third (6.8% vs 11.5% in Q4 of 2015). For the first time during the reporting period France appeared in the Top 10 countries hosting the most C&C servers. This correlates with the increased number of attacks in the country.
Distribution of botnet C&C servers by country in Q1 2016
99.73% of DDoS targets in Q1 2016 were attacked by bots belonging to one family. Cybercriminals launched attacks using bots from two different families (used by one or more botnet masters) in just 0.25% of cases. In 0.01% of cases three or more bots were used, mainly from the Sotdas, Xor and BillGates families.
Correlation between attacks launched from Windows and Linux botnets
When it came to the number of attacks launched from Windows and Linux botnets in Q1 2016, Windows-based botnets were the clear leader. For the third quarter in a row, the difference between the share of Windows- and Linux-based attacks was approximately 10 percentage points.Conclusion
The events of the first quarter of 2016 once again demonstrated that the attackers are not resting on their laurels and are increasing their computing resources to perform DDoS attacks. Amplification scenarios, which have de facto become the standard tool for carrying out a powerful attack, exploit vulnerabilities in new network protocols. The reasons for an attack can vary: from disrupting pre-election campaigns and attacking candidates’ resources to showdowns between competitors on the black market. There have been frequent incidents of DDoS attacks targeting the very organizations that specialize in countering them. With the spread of vulnerable devices and workstations and the abundance of configuration drawbacks at the application level, the cost of a significant attack is going down. Therefore, reliable protection is needed to ensure these attacks are financially unviable for the criminals.
This year’s DBIR release from Verizon exposes valuable and well organized data on global incidents this past year. Our contributions on targeted attack activity and other areas to a report like this one over the past several years is important to help to improve cyber-security awareness and education both in the security industry and the general public.
The report is well organized, offering trending information from Point of Sale incidents to cyber-espionage, web application hacking, cybercrime, and skimming. And it simplifies most of the data into nine categories for ease of discussion. The data demonstrates that intruders will use tried and true techniques before moving on to the newest and most expensive. Like most years in cybersecurity, “It’s like déjà vu, all over again.” —Yogi Berra
You can download the 2016 DBIR here, its 85 pages of data and diagrams can help provide informed discussion around these topics on a greater scale. We look forward to another great writeup in 2017 from the DBIR guys at Verizon.
BeEF Wrapped Up and Delivered in 2016
The embedded BeEF content appears not to be fully configured, and only partially implemented. Perhaps a limited data set was of interest for this attacker, or this was an early attempt at deploying BeEF.
This incident is interesting because at the same time and a bit earlier, another group was heavily relying on repackaging open source offensive security product in their toolset by deploying both BeEF and Metasploit-produced components across a select set of strategic web compromises. This particular APT has years of low-tech elaborate social engineering schemes and re-purposed open source efforts under its belt.
While we call them the NewsBeef APT, they have been reported in the past as Charming Kitten or Newscaster in 2014, social engineering their way into sensitive circles of trust with spoofed LinkedIn profiles and phony news media organizations.
They continue to be highly active, but this time, they are using a slightly more technical toolset. On one hand, they have developed skills or discovered tools to compromise select web applications and sites, supporting their watering hole campaigns. On the other hand, they have repackaged leaked bot source code and repackaged open source Metasploit and PowerSploit components to produce and administer backdoors and downloaders.
Newsbeef/Newscaster will find a way to compromise a web site, usually the vulnerability appears to be CMS related, in an outdated WordPress plugin, Joomla version, or Drupal version. Attackers usually perform one of two things, Newsbeef has been performing the first of the two:
- inject a src or iframe link into web pages or css sheets
The injected link will redirect visitors’ browsers to a BeEF server. Usually, the attackers deliver some of the tracking and system/browser identification and evercookie capabilities. Sometimes, it appears that they deliver the metasploit integration to exploit and deliver backdoors (we haven’t identified that exploitation activity in our ksn data related to this group just yet). Sometimes, it is used to pop up spoofed login input fields to steal social networking site credentials. We also haven’t detected that in ksn, but some partners have privately reported it about various incidents. But we have identified that attackers will redirect specific targets to laced Adobe Flash and other installers from websites that they operate.
So, the watering hole activity isn’t always and usually isn’t delivering backdoors. Most of the time, the watering hole injections are used to identify and track visitors or steal their browser history. Then, they deliver the backdoors to the right targets.
In addition to the University site and the NewsBeef APT, in the past couple of months, we identified a variety of compromised sites around the world serving the BeEF. Most are cleaned up. Deployments to interesting and strategic web sites and their true reach on a global scale appears to be on the increase:
- Middle eastern embassy in the Russian Federation
- Indian military technology school
- High conflict regional presidency
- Ukrainian ICS Scanner mirror
- European Union education diversification support agency
- Russian foreign trade management organization
- Progressive Kazakh news and politics media
- Turkish news organization
- Specialized German music school
- Japanese textile manufacturing inspection corporate division
- Middle Eastern social responsibility and philanthropy
- surprisingly popular British “lifestyle” blog
- Algerian University’s online course platform
- Chinese construction group
- Russian overseas business development and holding company
- Russian gaming developer forum
- Romanian Steam gaming developer
- Chinese online gaming virtual gold seller
- Brazilian music instrument retailer
Key to these incidents are the development, distribution, and ease of use of toolkits like BeEF.
BeEF itself is an open source collection of tools and tricks, some years old, that combined together can effectively hook a visiting web browser for evaluation and full exploitation. Because of its capabilities, we have seen increased adoption of the framework for the past year or so.
- Browser enumeration and reporting
- Plugin enumeration and reporting
- Retrieve visited domains (based on an old browser cache fetch timing trick)
- Social engineering via live sessions and phishing within the browser
- Network exploration, discovery, and exfiltration tunneling
- Metasploit exploit integration and autopwning
- Evercookie deployment for persistent tracking – multiple platforms
- XSS evaluation and exploitation
At the same time, many of the techniques implemented are very old and public. The kit is extensible, customizable, and integrates with metasploit for autopwnage. Some of the techniques were discussed during Jeremiah Grossman’s 2006 Black Hat conference presentation. The delay in deployment for techniques of this type indicates that some teams are dependent on open source tool packaging and ease of use. We have seen this sort of reliance on both open source offensive toolkits and legitimate software in the past from APT like Crouching Yeti, TeamSpy, and now the Newsbeef.
Preventing the social engineering sessions for credential theft and Metasploit exploit integration makes immediate sense and can be incorporated at the network and more effectively at the host level. AntiAPT can help wipe out most of an operation on the network at scale, but these measures can be evaded as well. In other words, dealing with a determined attacker using tools like this one is difficult.
Cash machines have been part of our lives since 1967 when a London branch of Barclays Bank unveiled the first ATM. Millions of people around the world now use ATMs every day to withdraw cash, pay in to their account or make a variety of payments. When using ATMs people give little or no thought to the hardware, software or security of the machines. Unfortunately, ATM manufacturers and their primary customers – banks – don’t pay much attention to the security of cash machines either. This is confirmed by the increasing number of thefts from ATMs using non-destructive methods, i.e. without the use of metal cutting tools or explosives.
To understand why this is happening, let’s first look at what exactly a cash machine is.Hardware
An ATM is basically a construction kit. The manufacturer builds them from a dispenser, a card reader and other units produced by different companies. The units are placed in a housing which usually consists of two parts: the top box called the cabinet, or the servicezone, and the lower section called thesafe.
The cabinet includes units such as the system unit (yes, a standard system unit, which sometimes even has the same housing as a typical home computer), the EPP (Encrypting PIN Pad) the card reader, and so on. The service zone, according to ATM manufacturers, contains everything that makes it impossible to access the money. Probably for this reason the cabinet cover is made of plastic and the service zone is protected from unauthorized access by just a simple lock. By the way, a set of locks and separate keys can both easily be purchased online as the manufacturers install the same locks on their devices, and most banks usually don’t bother to replace them.
The safe has much better protection: it is a ‘sandwich’ of steel and concrete with two types of locks – one coded (electronic or limb, sometimes electro-mechanical) and the other a key lock (usually a lever tumbler lock). The safe contains the devices directly related to the money – a dispenser from which cash is withdrawn, and a cash-in module.
All devices are connected to the system unit, which in this case performs the function of the host (as we shall refer to it) via the USB or RS232 ports (often referred to as a COM port). Sometimes these ports are located directly on the system unit; if there aren’t enough ports, a USB/COM hub is used. Older ATM models can still be found that are connected via the SDC bus.Software
The software used on almost every ATM is straightforward:
- operating system
- ATM units management software
- software used to interact with the user (ATM consumer or operator)
- software used to communicate with the processing center (which provides the information and technological sides of the transaction)
- anti-virus software, or integrity control software.
This is sufficient for the ATM to carry out its immediate functions, but for some reason certain banks also install Acrobat Reader 6.0, Radmin, TeamViewer and other unnecessary and in some cases even dangerous software.
When it comes to the operating system, the vast majority of ATMs still use … Windows XP! Despite the fact that Microsoft stopped issuing security updates for it in April 2014. Of course, 0-day vulnerabilities for this system will remain unpatched. The engineers servicing ATMs often think that if the ATM is working, it is better “not to touch” (read: “not to update”) it. As a consequence, some cash machines still have the unpatched critical vulnerability MS08-067 which allows remote code execution.
ATM units are implemented on microcontrollers based on real-time operating systems (RTOS), which is particularly irksome for the guys with IDA Pro because static analysis is almost unheard for such systems.
That’s basically all the information cybercriminals need to start hacking.Malware
In 2009, the appearance of Trojan Backdoor.Win32.Skimer caught the world’s attention: it was the first malicious program targeting ATMs. Skimer attacked ATMs from a particular manufacturer – one of the market leaders. Using this malicious program the criminals emptied the cash dispensers and also skimmed the data from bank cards processed in infected ATMs. Since then, ATMs of different manufacturers have been repeatedly exposed to malware infection.
The process of stealing money from ATMs using malware consists of four stages:
- The attacker gains local/remote access to the machine.
- Malicious code is injected into the ATM system.
- As a rule, infection is followed by rebooting of the ATM. The system seems to reboot in standard mode but at the same time comes under the control of a malicious program, i.e. cybercriminals.
- The final stage, i.e. the main aim of the process, is the theft of money.
Getting access to the inside of an ATM is not a particularly difficult task, as the experts at the Positive Hack Days, the international forum on practical information security, demonstrated. The process of infecting is also fairly clear – arbitrary code can be executed on an insecure (or insufficiently secure) system. There seems to be no problem with withdrawing money either – the malware interface is usually opened by using a specific key combination on the PIN pad or by inserting a “special card”, and then all you need to do is stuff your pockets full of cash.
Here we will focus on how a malicious program can gain control of an ATM.The XFS standard
So the attackers have infected the ATM system unit. What next?
Here again, a short explanation is required. As already mentioned, the ATM is managed by a Windows-based application. Its task is to organize interaction between the user (client or services), the processing center which sends commands to the ATM and the equipment that executes these commands. The message exchange with the processing center occurs via direct connect protocols (NDC or DDC): users communicate with the GUI while service providers are responsible for the operation of each ATM unit (gateways to these units). To send commands to the service providers and on to the equipment as well as to receive status messages, a level called XFS Manager is used in accordance with WOSA.
ATM operations in the context of the XFS standard
XFS (CEN/XFS, and earlier WOSA/XFS), or the eXtensions for Financial Services, is a standard that provides a client-server architecture for financial applications on the Microsoft Windows platform, especially peripheral devices such as ATMs. XFS is intended to standardize software so that it can work on any equipment regardless of the manufacturer, and provides a common API for this purpose.
Thus, any application that is developed with the XFS standard in mind can control low-level objects by using only the logic described in this standard. And that application could well be the Tyupkin backdoor or any other malicious program.
What opportunities does XFS offer?
For example, the dispenser, which is the most interesting part for the attackers, can give out money without authorization. Or use of XFS on some ATM models means cybercriminals can manipulate the code to open the safe and unlock the ATM cassettes.
Exploitation of the MS08_067 vulnerability allowing execution of arbitrary code. The video was shot by experts at BlackHat Europe 2014
With regard to the card reader, XFS allows the reading and recording of data from the bank card magnetic stripe and even retrieval of the transaction history stored on the EMV card chip.
Of special note is the Encrypting PIN Pad (EPP). It is believed that the PIN cannot be intercepted because it is entered on the ATM PIN pad and is converted directly inside the encryption module into a PIN block (EPP contains keys to do this, two of which are in the bank’s Hardware Security Module). However, XFS allows the PIN pad to be used in two modes:
- Open Mode – for entering different numeric values, such as the sum to be withdrawn;
- Secure Mode, which EPP switches to in order to enter a PIN and encryption keys.
This allows cybercriminals to implement a “man-in-the-middle” (MiTM) attack. They only have to intercept the command sent from the host to the EPP to switch to Secure Mode and then to inform the device that work is continuing in Open Mode. In the reply message, the EPP will send the keystrokes as plain text – exactly what the attacker needs.
But what about authentication and exclusive access? And surely the standard’s specifications are inaccessible?
Unfortunately, this is not the case with XFS. The standard does not provide any authentication, and exclusive access to service providers is implemented, but not for security reasons. This is just a single-threaded command sending function to avoid accidentally breaking delicate hardware by simultaneously sending two identical commands.
Surprisingly, although it is a standard for financial applications, it doesn’t even mention security. Where can you find the specifications to check if this is true? Just try entering “ATM XFS” in any search engine and you’ll find the answer among the first few results.Integrity control software
Banks sometimes use integrity control software on their ATMs that supposedly prevents the execution of unauthorized code based on a whitelist, controls connected devices and drives, as well as providing other useful methods which should, in theory, counter attacks.
But we shouldn’t forget that first of all it is software, and just like any other software, it’s not perfect. It may be vulnerable to attacks as such kiosk mode bypassing, whitelist bypassing, buffer overflow, privileges escalation to SYSTEM user, etc. As you know, existing vulnerabilities often allow cybercriminals to gain access to the operating system and to do their dirty work.Undocumented features
The bad guys may use modified utilities that were originally provided by ATM developers or manufacturers to test a machine’s operability. One of the functions of these utilities is to test the dispenser function, including the dispensing of cash. In order to carry out a test, the engineer has to confirm his legitimacy by opening the safe door or performing actions with the dispenser cassettes. The logic is simple: if you can open the safe, you have the key, i.e. you are a licensed engineer or a cash-in-transit guard. But by simply replacing a couple of bytes in the utility, the “right” people can “test” cash withdrawals without any checks.
Yet another way criminals have of lining their pockets is to change the denomination of banknotes dispensed by the ATM using a diagnostic utility. As a result, the attacker receives banknotes with the largest nominal value (e.g., a 100 dollar/euro banknote) while the ATM “thinks” it is dispensing the smallest of the available denominations (five or ten). It means several hundred thousand can be withdrawn from a card with a balance of just a few hundred.Black box
So-called black box attacks are another type of attack that is getting increased coverage in the news. On surveillance camera videos the following occurs: someone opens the service zone, connects a magic box to the ATM, closes the cabinet and leaves. A little later several people who appear to be customers approach the ATM and withdraw huge sums of money. Of course, the criminals retrieve their little device from the ATM once they have achieved their goal. Usually, these black box attacks are only discovered a few days later when the empty cassettes and the withdrawal logs don’t tally, leaving the bank employees scratching their heads.
However, there is no magic involved – the attackers connect a specially programmed microcomputer to the dispenser in such a way that it bypasses the security measures implemented on the host (antivirus, integrity control, full disk encryption, etc.).Communications insecurity
As mentioned above, USB, RS232, or SDC can be used as a data transmission channel between the system unit and the devices. It’s likely that nothing will prevent the attackers from sending the necessary commands directly to the device port bypassing its service provider. The standard interfaces often do not require any specific drivers. Authorization is not required either, which basically makes these insecure proprietary protocols an easy target – just sniff and replay. The result is direct control over ATM units, the use of undocumented functions (e.g., changing the unit firmware). The criminals may also use a software or hardware traffic analyzer, installing it directly on the port of a particular device such as a card reader in order to obtain the transmitted data. And this analyzer will be difficult to detect.
Direct control over the dispenser means the ATM cassettes can be emptied without any entries being made in the ATM software logs.
A typical packet – the command to dispense a banknote from the first cassette of the dispenser
For those who are unaware, it may look like magic. Every great magic trick consists of three parts or acts. There are dispensing money from the cassette, opening the shutter, and presenting money to the client.
A black box attack on an ATM. Video was prepared by experts for demonstration purposes at BlackHat Europe 2014
Hardware skimmers are ‘so yesterday’. Direct connection makes it possible to read and record the magnetic strip of a credit card. Traffic analyzers, which are freely available on the Internet, can also be used as a direct connection. Rumor has it that in one fairly large bank all the ATMs were used as skimmers: the attackers had found vulnerabilities in the bank’s network and installed a USB sniffer on the ATMs, allowing them to collect bank card data in plain text for five years! Who knows, maybe your card was among those affected.
The intercepted data of a Track2 cardThe network
The connection between ATMs and the processing center can be protected in various ways. For example, using a hardware or software VPN, SSL/TLS encryption, a firewall or MAC-authentication, implemented in xDC protocols. However, all these measures often appear to be so complex for banks that they don’t bother using any network protection at all.
In such cases, a MiTM attack can be launched that will result in the attacker getting both bank card data and all the money in the ATM. This requires remote access to the device, which is usually obtained by using vulnerable services that can be accessed from the Internet, as well as social engineering techniques. Physical access to the network hardware, including the ATM Ethernet-cable will also suffice.
On the way to the real processing center a fake one pops up; it sends commands to the ATM software to dispense banknotes. Withdrawing money is possible with any card, even one that has expired or has a zero balance, as long as the fake processing center “recognizes” it. A fake processing center can be either “homemade” software that supports communication with the ATM via the xDC-protocol, or a processing center simulator originally designed to check network settings (yet another “gift” from the vendors to the cybercriminals).
The commands for giving out 40 banknotes from the fourth cassette sent from a fake processing center and stored in the ATM software logs. They look almost like the real thing.
Where do the criminals find ATMs that can be attacked via the network? Do they scan all the nearby networks or buy the information on underground forums?
It turns out that you just need to enter the correct request in a search engine – https://www.shodan.io/ (this Internet of Things scanner is well-known by the experts). The data collected by this scanner is usually enough to launch such attacks.
— Eugene Kaspersky (@e_kaspersky) 9 февраля 2016 г.
Or you could just take a closer look at the ATMs in retail and business centers.
Sometimes the ATM system can be accessed without even opening it – all the communications are located on the outsideWho’s to blame and what can be done
This part is usually the most depressing, and here’s why.
When we detect a vulnerability while analyzing ATM security, we send a notification to the vendor with a description of the problem and ways to solve it. And often the answers are bewildering:
“The vulnerabilities are essentially normal specifications of the card readers and not unexpected. As long as the ATM is running within normal parameters, these problems cannot possibly occur.”
“However this vulnerability is inherent in the USB technology and is expected be mitigated by the use of appropriate physical controls on access to the ATM top box.”
“We regret informing you that we had decided to stop producing this model more than 3 years ago and warranties for our distributors been expired.”
Indeed, why should vendors bother about ATMs with expired warranties that are still used by banks around the world, and whose physical security often leaves much to be desired? Unfortunately, reality shows that manufacturers are only interested in selling new products and not in eliminating the shortcomings of existing systems, while banks lack the necessary skills to cope with the problems on their own.
Fortunately, some manufacturers understand the dangers of unauthorized ATM use, and release security updates. To prevent attacks on dispensers, two-way authentication and cryptography are used. It should be noted, however, that not all cryptography is correctly implemented cryptography.
While the existing countermeasures can protect ATMs from malware, they are powerless against black box or network attacks. A huge number of security flaws and vulnerabilities that can be exploited with minimum expertise make cash machines a prime target for those desperate to get rich illegally.So. Is everything lost?
ATM manufacturers can reduce the risk of attack on cash machines.
- Firstly, it is necessary to revise the XFS standard with an emphasis on safety, and introduce two-way authentication between devices and legitimate software. This will help reduce the likelihood of unauthorized money withdrawals using Trojans and attackers gaining direct control over ATM units.
- Secondly, it is necessary to implement “authenticated dispensing” to exclude the possibility of attacks via fake processing centers.
- Thirdly, it is necessary to implement cryptographic protection and integrity control over the data transmitted between all hardware units and PC inside ATM.
And what should banks do? They need to take action!
Encourage those who sell ATMs and software to make them secure. The manufacturer must eliminate vulnerabilities as soon as possible; it is necessary to tell them about it as often as possible. To prevent hacking of ATMs it is necessary to make use of all the available protection tools. A completed PCI DSS Self-Assessment Questionnaire is not a silver bullet and won’t protect ATMs from attacks, or banks from financial and reputational losses. Proactive protection, including regular ATM security assessment and penetration testing, is better (and often much cheaper) than security incident and the subsequent investigation.
Bad guys are watching.
PS: No cash machines were harmed in the preparation of this material.
PPS: This overview of the security issues in cash machines is not intended as a hacking guide.
Major football tournaments such as the World Cup and the European Championship, traditionally attract a lot of spammer activity. Euro 2016 will be held this summer in France, and it’s not only the fans and players who are getting ready but also Internet fraudsters. The latter have started sending out fake notifications about lottery wins dedicated to the upcoming tournament. Their emails often contain attachments adorned with graphic elements including official emblems, the Euro 2016 logo and those of its sponsors.
The contents of the attachments are the standard stuff: the lottery was held by an authorized organization, the recipient’s address was randomly selected from a large number of email addresses, and in order to claim your prize you have to reply to the email and provide some personal information. We have recorded cases where the same attachment was sent in messages with a different text, but the theme of the email is essentially the same. The fraudsters also use different email addresses and change those used in the body of the message and the attachment.
We have also come across advertising spam in different languages, for example in Dutch, asking recipients to buy a 2-euro commemorative coin issued specifically for Euro 2016.
We expect to see a growth in football-themed spam as the start date of Euro 2016 approaches. This type of fraudulent spam can be one of the most dangerous for users: the perpetrators are unlikely to limit their activity to fake lotteries, and will start spreading various emails offering the chance to win tickets to the games, as was the case before the World Cup in Brazil. The amount of spam targeting users in France, which is hosting the championship, may also increase.
A detailed presentation of this research was delivered at RSA US 2016, and is available at https://www.rsaconference.com/writable/presentations/file_upload/tech-t09-smart-megalopolises.-how-safe-and-reliable-is-your-data.pdf
In the past two years traffic sensors have mushroomed in Russian cities. Drivers using speed camera detectors were the first to spot the white boxes stuck to posts along the roadside. Their devices, designed to warn drivers about traffic enforcement cameras, react to the signals emanating from the new sensors in the same way they do to the radar guns used by traffic police. Helping enforce the speed limit is merely a positive side effect, however; the city authorities installed the sensors for a completely different reason. The devices count the number of cars of varying size in each lane, determine their average speed, and send the data to a unified traffic control center.
A traffic sensor in Moscow
As a result, the city authorities receive information about traffic intensity, which allows them to, for example, adjust traffic light phasing or plan further road infrastructure. The weekly reports issued by Moscow’s traffic authorities present information about the slowest and the fastest highways, based on data coming from both the Center for Road Traffic Management and from Yandex. While the latter’s data comes from apps running on users’ smartphones, the former would not be able to collect its data without the road infrastructure that will be discussed here.
Each week the Moscow city authorities publish data about the city’s fastest and slowest highways
These sensors are the lowest tier of ‘smart city’ infrastructure – they collect raw data about traffic and pass it on; without that data, no analysis can be done and systems cannot be configured properly. Therefore, the information coming from the sensors has to be accurate. But is that actually the case? Can an outsider manipulate the operation of the sensors and the information that they collect? We will try to answer these questions and identify any improvements that can be made to the urban IT infrastructure.How to search for devices and information about them
Any research begins by collecting all the available data, and research into embedded systems, to which traffic sensors belong, is no exception. Even in a narrow market segment, you cannot know all sensor types and models by sight unless you are dealing with them professionally every day. The odds are that you won’t be able to immediately identity the manufacturer of a device by just looking at it. This makes all the logos and manufacturer labels on devices all the more valuable.
If you do succeed in identifying the model of a road sensor just by looking at it, you can find various documentation on the vendor’s site (or that of their integrator), and, if you are lucky enough, you will also find the software used for working with the devices. You will almost certainly find a marketing leaflet about your device; there is also a good chance you will find a larger sales-oriented document. It is also not uncommon to come across documentation, but finding a full-fledged technological description with the device’s command system is a rare piece of luck.
It is practical to automate the process of working with the sensors, so you don’t have to sit under each and every device with a laptop. Nowadays, such automation is quite normal – wireless connections are no longer a rarity for ‘smart city’ components. However, to ensure such automation, you first need to know which communication protocol each sensor uses, and how to separate the devices you need from all the other devices.
For this purpose, you can use any identifiers, including peculiarities in how the devices communicate their data. For example, most MAC addresses are reserved to specific manufacturers (however, anonymous MAC addresses also exist). As well as numeric IDs, devices also typically have alphabetic names that may also follow some type of standard, i.e. the device model plus an incremental index.
All of this makes it possible to write a scanner to search for devices that are of interest to us. One of the sensor models installed in Moscow uses Bluetooth for data communication. These devices have both MAC addresses and ‘friendly names’ that are quite distinctive, so we can add only these traffic sensors to the list and filter out all nearby smartphones and TV sets. A discussion on Bluetooth security goes beyond the scope of this topic, so we will not talk here of compromising Bluetooth devices. We informed the Moscow city authorities about the configuration drawbacks in November 2015.
Traffic sensor records saved to a database
I used Python, PostgreSQL and a bit of C. In real time, as I drive by each traffic sensor, the scanner identifies each device’s MAC address, friendly name and coordinates. The fields with the vendor name and the physical address are filled out later on a separate pass through the database based on the data already collected. Determining the device’s physical address from coordinates is, to a certain degree, a time-consuming procedure, so it shouldn’t be done simultaneously while searching for devices. Establishing a Bluetooth connection is not fast either, so if you want to find traffic sensors, you’ll have to drive slowly.What can be done with the firmware
The openness shown by the manufacturers to installation engineers, their readiness to give them access to tools and documents, automatically means they are open to researchers. (I respect this sort of approach; in my view, this sort of openness combined with a ‘bug bounty’ approach yields better results than secrecy.) After selecting any of the identified sensors, you can install the device configuration software supplied by the vendor on your laptop, drive to the location (the physical address saved in the database), and connect to the device.
As we do in any research of embedded system security, we first of all check if it’s possible to reinstall the firmware on the device.
The configuration software allows the firmware on the traffic sensor to be changed
Yes, we can install new firmware on the device via this wireless connection designated for servicing purposes. It is just as easy to find the manufacturer’s firmware and as it is its software. The firmware looks reminiscent of Intel iHex or Motorola SREC, but this is the manufacturer’s proprietary product. If we remove the overhead information (‘:’ – the write instruction, serial numbers, memory addresses and checksums) from the data blocks for the digital signal processor (DSP) and the main processing unit (MPU), we obtain the clean code. However, we don’t know the architecture of the controllers in the device, so we cannot simply open the file with a disassembler.
The traffic sensor firmware
Oddly enough, LinkedIn helps out here – it’s not just a handy resource for careerists and HR departments. Sometimes the device architecture is not a secret, and engineers who used to work for the manufacturer may be willing to talk about it. Now, as well as the file, we also have an understanding of the architecture which the firmware was compiled for. However, our good fortune only lasts until we launch IDA.
You can find the controller types even if they aren’t specified in the documentation
Even when we know the architecture, the firmware remains a meaningless jumble of bytes. However, we could find out from the same engineer how exactly the firmware is encrypted, as well as the encryption algorithms and key tables. I didn’t have the device to hand, so at that stage I decided this black box mode of firmware modification was showing little promise, and set it aside. We have to admit that in this specific case the microelectronics engineers know how to protect firmware. However, this did not mean the end of the road for our sensor research.Only trucks travel at night
Firmware modification is “good” in that new functional capabilities can be added. However, there is sufficient functionality in the manufacturer’s standard software. For example, the device has about 8 MB of memory that is used to keep a copy of traffic data until the memory is full. This memory can be accessed. The firmware allows you to change the way that passing vehicles are classified according to their length, or change the number of lanes. Would you like a copy of the information collected on traffic? No problem. Would you like to classify all vehicles as trucks driving in the right lane? You can do that too. Of course, this will affect the accuracy of the statistics that are collected, with all ensuing consequences.
Sample of the data traffic sensors collect and pass along. The same data is stored on the device
If someone wants to get a copy of Moscow traffic statistics or manipulate the data, they will have to walk or drive around all the traffic sensors; however, they won’t have to launch software for each of the thousands of sensors and modify the settings manually. In this specific case, there is a description of the proprietary system of commands for the devices. This is not something that you come across a lot when researching embedded systems. In any case, after establishing a connection to the traffic sensor using the manufacturer’s software, the commands are no longer a secret – they are visible using a sniffer. Even then, there’s a description in English that saves us the trouble of analyzing the machine-language communication protocol.
Documented device commands make traffic analysis unnecessary
Bluetooth services are not actually implemented on the traffic sensor; the wireless protocol in this case is only a data communication environment. The data is communicated via a regular serial port. Software handling of such ports is no different from reading and writing to files, the code for sending commands is trivial. For these purposes, it’s not even necessary to implement the usual multithreaded port handling – it’s sufficient to send bytes and receive the response in a single thread.
To sum up, a car driving slowly around the city, a laptop with a powerful Bluetooth transmitter and scanner software is capable of recording the locations of traffic sensors, collecting traffic information from them and, if desired, changing their configurations. I wouldn’t say that traffic stats are a major secret, but tampering with sensor configurations could affect their validity. And that data could be used as a basis for controlling ‘smart’ traffic lights and other traffic equipment.
The sensor sent a response, the command has been accepted. Because we know the command system, it is easy to ‘translate’ the responseWhat can be done?
It turns out that the answers to both questions we raised at the beginning are negative: traffic data is not protected, and it can be manipulated. Why is that? Well, there was no authorization except that required for Bluetooth, and that was not configured properly. The manufacturer of the road sensors we examined is very generous when it comes to service engineers, with a lot of information about the devices publicly available on the manufacturer’s official website and elsewhere. Personally, I agree with the manufacturer and respect them for this, as I don’t think the “security through obscurity” approach makes much sense these days; anyone determined enough will find out the command system and gain access to the engineering software. In my view, it makes more sense to combine openness, big bounty programs and a fast response to any identified vulnerabilities, if for the only reason that the number of researchers will always be bigger than the number of employees in any information security department.
At the installation stage, it makes sense to avoid using any standard identifiers. Obviously, manufacturers need to advertise their products, and the servicing teams may need to collect additional information from the adhesive labels on a device, but besides the convenience there are also issues of information security to consider. Last but not least, it’s not worth relying solely on the standard identification implemented in well-known protocols. Any additional proprietary protection that is properly implemented will be relevant and make penetration much more difficult.
A detailed presentation of this research was delivered at RSA US 2016, and is available at RSA conference website.