• CheckPoint.com
• 
• 
• 
• 
• 
• 

Introduction

Recently, Check Point researchers spotted a targeted attack against officials within government finance authorities and representatives in several embassies in Europe. The attack, which starts with a malicious attachment disguised as a top secret US document, weaponizes TeamViewer, the popular remote access and desktop sharing software, to gain full control of the infected computer.

By investigating the entire infection chain and attack infrastructure, we were able to track previous operations that share many characteristics with this attack’s inner workings. We also came across an online avatar of a Russian speaking hacker, who seems to be in charge of the tools developed and used in this attack.

In this article, we will discuss the infection chain, those targeted, the tools used and a possible attribution to one of the hackers behind the attack.

The Infection Chain

The infection flow starts with an XLSM document with malicious macros, which is sent to potential victims via e-mail under the subject “Military Financing Program”:

Email subject: military financing program

File name: “Military Financing.xlsm”

SHA-256:
\nefe51c2453821310c7a34dca30540
\n21d0f6d453b7133c381d75e3140901efd12

Fig 1: Decoy document

The well-crafted document bears the logo of the U.S Department of State, and is marked as Top Secret. Although the attackers have worked hard to make the document appear convincing, they seem to have overlooked some Cyrillic artifacts (such as the Workbook name) that were left in the document, and could potentially reveal more information about the source of this attack.

Fig 2: The infection chain

Once the macros are enabled, two files are extracted from hex encoded cells within the XLSM document:

\n
1. A legitimate AutoHotkeyU32.exe program.
\n
2. AutoHotkeyU32.ahk→an AHK script which sends a POST request to the C&C server and can receive additional AHK script URLs to download and execute.
\n

\n
• Three different AHK scripts are awaiting on the server for the next stage:\n
  \n
  1. hscreen.ahk: Takes a screenshot of the victim’s PC and uploads it to the C&C server.
  \n
  2. hinfo.ahk: Sends the victim’s username and computer information to the C&C server.
  \n
  3. htv.ahk: Downloads a malicious version of TeamViewer, executes it and sends the login credentials to the C&C server.
  \n
  \n
\n

The malicious TeamViewer DLL (TV.DLL) is loaded via the DLL side-loading technique, and is used to add more “functionality” to TeamViewer by hooking windows APIs called by the program.

Modified functionality includes:

\n
• Hiding the interface of TeamViewer, so that the user would not know it is running.
\n
• Saving the current TeamViewer session credentials to a text file.
\n
• Allowing the transfer and execution of additional EXE or DLL files.
\n

Fig 3: MoveFileW function hook: adds payload “execute” and “inject” functionality.

The following is a demonstration of how it actually works:

Fig 4: Remote payload execution demo

Victims

As described in the infection flow, one of the first uses of the AutoHotKey scripts is to upload a screenshot from the compromised PC.

The directory which those screenshots were uploaded to was left exposed, and could have been viewed by browsing to the specific URL:

Fig 5: Open directory with victims’ screenshots

However, those screenshot files were deleted periodically from the server, and eventually the “open directory” view was disabled.

Until that time, we were able to ascertain some of the victims of these attacks, as most of the screenshots included identifying information.

From the targets we have observed in our own telemetry, as well as the information we have gathered from the server, we were able to compose a partial list of countries, where officials were targeted:

\n
• Nepal
\n
• Guyana
\n
• Kenya
\n
• Italy
\n
• Liberia
\n
• Bermuda
\n
• Lebanon
\n

It is hard to tell if there are geopolitical motives behind this campaign by looking solely at the list of countries it was targeting, since it was not after a specific region and the victims came from different places in the world.

Nevertheless, the observed victims list reveals a particular interest of the attacker in the public financial sector, as they all appear to be handpicked government officials from several revenue authorities.

Previous Campaigns

While all campaigns observed from this threat actor utilized a trojanized version of TeamViewer, the features of the malicious DLL have changed, and the first stage of the infection has evolved over time.

Delivery

The initial infection vector used by the threat actor also changed over time, during 2018 we have seen multiple uses of self-extracting archives instead of malicious documents with AutoHotKey, which displayed a decoy image to the user.

For example, the self-extracting archive “Положение о прокуратуре города(приказом прокурора края)_25.12.2018.DOC.exe” (translated into “Regulations on the city prosecutor’s office (by order of the regional prosecutor)_25.12.2018.DOC.exe”) displays the following image:

\n

Fig 6: SFX archive decoy image

This image shows officials from Kazakhstan, and was taken from the website of Kazakhstan’s Ministry of Foreign Affairs. The original name of the executable and the decoy content it displays seem to suggest that it was targeting Russian speaking victims.

There were also other instances in which related campaigns were after Russian speakers, one of the weaponized Excel documents had instructions on how to enable content for the macros to run in fluent Russian:

Fig 7: Russian decoy document

SHA-256: 67d70754c13f4ae3832a5d655ff8ec2c0fb3caa3e50ac9e61ffb1557ef35d6ee

Afterwards, it would display finance-related Russian content:

Fig 8: Russian decoy document – after macros are enabled

Although both examples of the different delivery methods described above show an exclusive targeting of Russian speakers, the recurring financial and political themes that they use highlight the attacker’s interest in the financial world once more.

The Payload

Throughout the campaigns multiple changes to the functionality of the malicious TeamViewer DLL, were introduced. Below are the feature highlights of each version:

First Variant (?-2018)

\n
• Remote control via TeamViewer
\n
• Send & execute file.
\n
• Sends basic system information.
\n
• Ability to self-delete.
\n
• Usage of config.bin configuration file
\n

Second Variant (2018)

\n
• Introduced a new C&C command system.
\n
• Partial list of the commands, can be viewed using the internal help command (which also provides multiple artifacts in Russian):
\n

Fig 9: Help commands found in malicious DLL

\n
• Chrome history of banks, online shops and crypto markets – The DLL can be requested to return a list of all online services from a predefined list. (See Appendix)
\n
• Configuration file was replaced by embedded configuration.
\n

Third Variant – as observed in the current campaign (2019)

\n
• Removed the command system.
\n
• Added DLL execution feature.
\n
• Relies on external AutoHotKey scripts for information gathering and TeamViewer credential exfiltration.
\n

Attribution

Although in such campaigns it is usually unclear who is behind the attack, in this case we were able to locate a user who appears to be behind the aforementioned activity active in several online forums, or is at least the creator of the tools used in the attack.

By following the trail from the previous campaigns we were able to find a `CyberForum[.]ru` user that goes by the name “EvaPiks”.

In multiple instances, the user would suggest, or be advised by other users to use, some of the techniques we witnessed throughout the campaigns.

The following are translated snippets from some of the threads in the forum:

Fig 10: EvaPiks – suggested macro code

The macro code suggested by EvaPiks in the above thread was actually used in the latest attack, and some of the variable names such as “hextext” were not even changed.

In the following screenshot, we see EvaPiks suggesting a Delphi code snippet that “works great”:

Fig 11: EvaPiks – suggested PHP code

Fig 12: Panel URL found in DLL code

In addition to the similar Delphi usage, the URL mentioned in the forum `(newpanel_gate/gate.php)` was used in one of the attacks.

Back in 2017, EvaPiks was the one seeking advice on the forum, with questions about API function call interception:

Fig 13: EvaPiks – seeking Delphi hooking advise on the forums

Fig 14: Hooks found in DLL code

The same hooking technique of `CreateMutexA` and `SetWindowTextW` functions was utilized in the sample we have observed as well.

An additional screenshot from the forum reveals how EvaPiks is experimenting with new features, some of which were integrated into the malicious DLLs:

Fig 15: EvaPiks – development PC screenshot from the forums

Besides `CyberForum[.]ru`, we also found out that this avatar was active on an illegal Russian carding forum, strengthening the notion that their forum activity is not for “educational purposes” only:

Fig 16: EvaPiks – complaining about a fellow user on a carding forum

The Attack Infrastructure

At one point or another, all the samples observed utilized the same web hosting company: HostKey, except some of the samples from the first variant. [see appendix B for a list of URLs]

Additionally, we observed the following login panels, on the C&C servers utilized by the malicious DLLs:

Fig 17: “Cyber Industries” login panel hosted on 193.109.69[.]5

Fig 18: login panel hosted on 146.0.72[.]180

Summary

On the one hand, from the findings we have described, this appears to be a well thought-out attack that carefully selects a handful of victims and uses tailored decoy content to match the interests of its target audience.

On the other hand, some aspects of this attack were carried out with less caution, and have exposed details that are usually well disguised in similar campaigns, such as the personal information and online history of the perpetrator, as well as the outreach of their malicious activity.

The malicious DLL allows the attacker to send additional payloads to a compromised machine and remotely run them. Since we were not able find such a payload and know what other functionalities it introduces besides the ones provided in the DLL, the real intentions of the latest attack remain unclear.

However, the activity history of the developer behind the attack in underground carding forums and the victim’s characteristics may imply that the attacker is financially motivated.

———————————————————————————————————————————————————————————————————————-

Check Point’s SandBlast

The malware used in this attack was caught using Check Point’s Threat Emulation and Threat Extraction.

Threat Emulation is an innovative zero-day threat sandboxing capability, used by SandBlast Network to deliver the best possible catch rate for threats, and is virtually immune to attackers’ evasion techniques. As part of the Check Point SandBlast Zero-Day Protection solution, Threat Emulation prevents infections from new malware and targeted attacks. The Threat Extraction capability removes exploitable content, including active content and embedded objects, reconstructs files to eliminate potential threats, and promptly delivers sanitized content to users to maintain business flow.

IOCs

DLLs

`013e87b874477fcad54ada4fa0a274a2
\n799AB035023B655506C0D565996579B5
\ne1167cb7f3735d4edec5f7219cea64ef
\n6cc0218d2b93a243721b088f177d8e8f
\naad0d93a570e6230f843dcdf20041e1e
\n1e741ebc08af09edc69f017e170b9852
\nc6ae889f3bee42cc19a728ba66fa3d99
\n1675cdec4c0ff49993a1fcbdfad85e56
\n72de32fa52cc2fab2b0584c26657820f
\n44038b936667f6ce2333af80086f877f`

Documents

`4acf624ad87609d476180ecc4c96c355
\n4dbe9dbfb53438d9ce410535355cd973`

C&Cs

`1c-ru[.]net/check/license
\nintersys32[.]com/3307/
\n146.0.72[.]180/3307/
\n146.0.72[.]180/newcpanel_gate/gate.php
\n185.70.186[.]145/gate.php
\n185.70.186[.]145/index.php
\n193.109.69[.]5/3307/gate.php
\n193.109.69[.]5/9125/gate.php`

Appendix A: Yara Rule

`rule \"TeamViwer_backdoor\"\n{\n\nmeta:\ndate = \"2019-04-14\"\ndescription = \"Detects malicious TeamViewer DLLs\"\n\nstrings:\n\n// PostMessageW hook function\n$x1 = {55 8b ec 8b 45 0c 3d 12 01 00 00 75 05 83 c8 ff eb 12 8b 55 14 52 8b 55 10 52 50 8b 45 08 50 e8}\n\ncondition:\nuint16(0) == 0x5a4d and $x1\n}`\n

Appendix B: Online services of interest

Banks

`bankofamerica.com,pacwestbancorp.com,alipay.com,cbbank.com,firstrepublic.com,chase.com
\ncitibank.com,bankamerica.com,wellsfargo.com,citicorp.com,pncbank.com,us.hsbc.com,bnymellon.com
\nusbank.com,suntrust.com,statestreet.com,capitalone.com,bbt.com,tdbank.com,rbs.com,regions.com
\n53.com,ingdirect.com,keybank.com,ntrs.com,www4.bmo.com,usa.bnpparibas.com,mufg.jp,aibgroup.com
\ncomerica.com,zionsbank.com,mibank.com,bbvabancomerusa.com,huntington.com,bank.etrade.com,synovus.com
\nbancopopular.com,navyfcu.org,schwab.com,rbcbankusa.com,colonialbank.com,hudsoncitysavingsbank.com,db.com
\npeoples.com,ncsecu.org,associatedbank.com,bankofoklahoma.com,mynycb.com,firsthorizon.com,firstcitizens.com
\nastoriafederal.com,firstbankpr.com,commercebank.com,cnb.com,websterbank.com,fbopcorporation.com
\nfrostbank.com,guarantygroup.com,amtrust.com,nypbt.com,wbpr.com,fult.com,penfed.org,tcfbank.com,lehman.com
\nbancorpsouthonline.com,valleynationalbank.com,thesouthgroup.com,whitneybank.com,susquehanna.net,citizensonline.com
\nucbh.com,raymondjames.com,firstbanks.com,wilmingtontrust.com,bankunited.com,thirdfederal.com,wintrustfinancial.com
\nsterlingsavingsbank.com,boh.com,arvest.com,eastwestbank.com,efirstbank.com,theprivatebank.com,flagstar.com
\nbecu.org,umb.com,firstmerit.com,corusbank.com,svb.com,prosperitybanktx.com,washingtonfederal.com
\nucbi.com,metlife.com,ibc.com,cathaybank.com,trustmark.com,centralbancompany.com,umpquabank.com
\npcbancorp.com,schoolsfirstfcu.org,mbfinancial.com,natpennbank.com,fnbcorporation.com,fnfg.com,golden1.com
\nhancockbank.com,firstcitizensonline.com,ubsi-wv.com,firstmidwest.com,oldnational.com,ottobremer.org
\nfirstinterstatebank.com,northwestsavingsbank.com,easternbank.com,suncoastfcu.org,santander.com
\neverbank.com,bostonprivate.com,firstfedca.com,english.leumi.co.il,aacreditunion.org,rabobank.com
\nparknationalbank.com,provbank.com,alliantcreditunion.org,capitolbancorp.com,newalliancebank.com
\njohnsonbank.com,doralbank.com,fcfbank.com,pinnaclebancorp.net,providentnj.com,oceanbank.com
\nssfcu.org,capfed.com,iberiabank.com,sdccu.com,americafirst.com,hncbank.com,bfcfinancial.com
\namcore.com,nbtbank.com,centralpacificbank.com,banksterling.com,bannerbank.com,firstmerchants.com,communitybankna.com
\nhsbc.com,rbs.co.uk,bankofinternet.com,ally.com,bankofindia.co.in,boi.com.sg,unionbankofindia.co.in,bankofindia.uk.com
\nunionbankonline.co.in,hdfcbank.com,axisbank.com,icicibank.com,paypal.com,pnm.com,wmtransfer.com,skrill.com,neteller.com
\npayeer.com,westernunion.com,payoneer.com,capitalone.com,moneygram.com,payza.com`

Crypto Markets

`blockchain.info,cryptonator.com,bitpay.com,bitcoinpay.com,binance.com,bitfinex.com,okex.com
\nhuobi.pro,bitflyer.jp,bitstamp.net,kraken.com,zb.com,upbit.com,bithumb.com,bittrex.com,bitflyer.jp
\netherdelta.com,hitbtc.com,poloniex.com,coinone.co.kr,wex.nz,gate.io,exmo.com,exmo.me,yobit.net
\nkorbit.co.kr,kucoin.com,livecoin.net,cex.io,c-cex.com,localbitcoins.net,localbitcoins.com,luno.com
\nallcoin.com,anxpro.com,big.one,mercatox.com,therocktrading.com,okcoin.com,bleutrade.com,exchange.btcc.com
\nbitkonan.com,coinbase.com,bitgo.com,greenaddress.it,strongcoin.com,xapo.com
\nelectrum.org,etherscan.io,myetherwallet.com,bitcoin.com`

Online Shops

`ebay,amazon,wish.com,aliexpress,flipkart.com,rakuten.com,walmart.com
\ntarget.com,bestbuy.com,banggood.com,tinydeal.com,dx.com,zalando,jd.com
\njd.id,gearbest.com,lightinthebox.com,miniinthebox.co`

Brief

When we look at a prevalent malware family, we give credit to its authors regarding the established malicious infrastructure. New malicious activity is flowing smoothly, command-and-control servers appear, everything works like Swiss watch. Are there any weak points in such a construction?

To answer this question we may think about a race car. It’s a masterpiece crafted for maximum speed, however, the more speed it has, the less chances it has to make a sharp turn. Malware infrastructure has the same weakness of inertia. When every joint works fine, you should have a strong reason to change something in it.

We can use it for our benefit just like movie detectives do. Take a city map, mark the spots of previous crimes ─ and you will likely understand the pattern and even get a probable place of next crime activity, it will likely follow the determined template. In this research we show how to transform these actions to the world of malware. We take one of the most prevalent contemporary botnets called Dridex, mark its previous crime scenes, build the map and draw conclusions helping us to catch the next strike. We show evidence of success of such an approach measured in strict numbers and explain how to use this idea in other real world cases.

Introduction

The Dridex Banking Trojan first appeared in 2014 and is still one of the most prevalent malware families. In March 2020, Dridex topped the list of most wanted malware.

Dridex was created by a cyber-crime group called “Evil Corp” which has caused an estimated damage of $100 million to the banking system worldwide. A lot of research has been issued already covering different aspects of the malware details and how the cyber-crime group functions.

In this article we provide a summary of key details known about Dridex to date. We explore pre-history of Dridex development, give an overview and show its key technical features and methods of spreading. We explain how we can intercept this malware at the earliest stages of the infection chain. We also provide graphs that show evidence of the success of our approach and how our customers are protected against this malware.

Background

Dridex has a famous lineage. Let’s take a step back in history to find out more about the time period when its earliest version appeared.

The key names in this story:

\n
• Evgeniy Bogachev ­─ Creator of the infamous ZeuS malware.
\n
• Maksim Yakubets ─ Alleged leader of Evil Corp cyber-crime group which is responsible for Dridex operations.
\n

Pre-Dridex era – It all starts with ZeuS

Zeus is a Trojan Horse malware. Its capabilities include turning an infected machine into a botnet node, stealing banking credentials, downloading and executing separate malicious modules. The members of cyber crime group attempted to steal around 220 million USD worldwide utilizing ZeuS according to FBI investigation.

The timeline below shows key points in ZeuS evolution:

\n

\n

^{Figure 1 – Chronology of ZeuS evolution.}

When ZeuS source code was leaked in 2011, various branches of this malware started to appear. It was very popular malware and gave rise to lots of different malware branches. ZeuS versions may be in a ZeuS online museum. At the time of this writing, ZeuS was associated with 29 different malware families, featuring around 490 versions in total.

In May 2014, the FBI issued a bulletin with description of Evgeniy Bogachev and the promised reward of 3 million USD “for information leading to the arrest and/or conviction.”

\n

\n

^{Figure 2 – Description of Evgeniy Bogachev on the FBI site.}

Dridex era

After the botnets of direct ZeuS successors were taken down, Dridex’s time came. This malware is a result of Bugat evolution (which appeared in 2010). Bugat v5 was recognized as Dridex in 2014.

More names appear on the stage at this time.

\n
• Andrey Ghinkul (from Moldova) was allegedly one of the administrators behind Dridex botnets in 2015.
\n
• Igor Turashev was allegedly one of the administrators behind Dridex botnets as well.
\n
• Denis Gusev was one of the key investors behind EvilCorp.
\n

More names connected to Dridex can be found in US treasury sanctions statement.

The timeline below shows some milestones in Dridex evolution:

\n

\n

^{Figure 3 – Chronology of Dridex evolution.}

Dridex in turn gave rise to a number of ransomwares starting with Bitpaymer in 2017. This branch continued with DoppelPaymer, which was developed in 2019, and WastedLocker, which was developed in 2020.

Recent past

In 2019, Dridex had at least 14 active botnets, some of which had already been  spotted previously, and others newly developed. Botnets are differentiated by their ID numbers. These are among the most active at this time: 10111, 10222, 10444, 40200, 40300.

At the end of 2019, the FBI issued a bulletin with a description of the author of Dridex and a promised reward of 5 million USD (compared with 3 million USD previously for E. Bogachev).

\n

\n

^{Figure 4 – Description of Maksim Yakubets on the FBI site.}

There is also evidence of Maksim’s luxurious lifestyle, undoubtedly due to income from his malicious activities.

\n

\n

^{Figure 5 ­– Cars, girls, money; the luxurious lifestyle of Maksim Yakubets.}

To date, Maksim Yakubets has not been apprehended by law enforcement.

As mentioned previously, in 2020, Dridex topped the lists of the most prevalent malware families in the world.

Infection chain

Before we start the analysis of Dridex samples themselves, we want to understand the infrastructure behind the malware. How is it delivered? What are the targets? What is the initial detection rate of supporting files? We will find the answers to all of these questions below.

Flow

When the operators want to spread Dridex, they use established spambots from different cyber-crime groups to send malicious documents attached to handily crafted e-mails. At different times of the Dridex lifecycle, Necurs, Cutwail and Andromeda botnets have all been involved in spreading Dridex.

When a user downloads and opens such a document (it may be Word or Excel), the embedded macros are launched with the aim of downloading and executing the Dridex payload.

\n

\n

^{Figure ­6 – Dridex infection chain execution flow.}

Targets

Dridex targets different high-profile entities from various parts of the world:

\n
• U.S. bank accounts.
\n
• U.S. credit card companies.
\n
• U.S. financial investment corporations.
\n
• European bank accounts.
\n
• Governmental agencies in Saudi Arabia, Qatar, Oman.
\n

Lures

To increase the successful rate at which Dridex is spread, malicious actors disguise their spam e-mails to look like legitimate ones. We can name examples of UPS, FedEx and DHL as companies whose logos and mailing style are used as bait in such e-mails.

\n

\n

^{Figure 7 – Examples of lures.}

When the victim clicks the link, either the archive with the malicious document or the malicious document itself is opened.

Initial detection rate

When first seen in the wild, Dridex delivery files show a very low detection rate. In the screenshot below we see the initial detection rate of the Excel document which delivers Dridex:

\n

\n

^{Figure 8 – Initial detection rate of the Dridex delivery file.}

The same is true for other delivery files.

Loader and Payload

The Dridex sample consists of the loader and the payload. We discuss key points of each part below.

Anti-debug technique

The Dridex loader utilizes the OutputDebugStringW function to make malware analysis more difficult. Different loaders produce different outputs (with the “Installing…” string being very popular) but the idea is the same everywhere: making a long loop that contains a lot of meaningless debug messages. In the figure below, we see the example of such a loop with an iteration of around 200 million:

\n

\n

^{Figure 9 – Loop with 0xBEBBE7C (around 200 million) iterations calling OutputDebugStringW.}

The output looks like this in the log:

\n

\n

^{Figure 10 – Dridex debug messages that overwhelm the analysis log.}

Obfuscation

The payload is heavily obfuscated; almost no function is called directly. Call resolutions are performed with the help of hash values identifying the library and the function it contains. An example of such a resolution is shown in the screenshot below:

\n

\n

^{Figure 11 – Example of the call resolution in the Dridex payload.}

All the functions important for key Dridex’ tasks are called this way.

\n

\n

^{Figure 12 – Example of resolved calls to Internet functions.}

We used the Labeless tool to resolve obfuscated function calls.

Strings in the malware are obfuscated using the RC4 algorithm and the decryption key stored inside the sample.

Configuration

The main point of interest inside the payload is its configuration. It contains the following important details:

\n
• Bot ID.
\n
• Number of C&C servers.
\n
• List of the C&C servers themselves.
\n

An example of the configuration:

\n

\n

^{Figure 13 – Example of the Dridex configuration inside the payload.}

The bot ID in this example is 12333. The Command and Control servers are:

\n
• 92.222.216.44:443
\n
• 69.55.238.203:3389
\n
• 66.228.47.181:443
\n
• 198.199.106.229:5900
\n
• 104.247.221.104:443
\n
• 178.254.38.200:884
\n
• 152.46.8.148:884
\n

Network activity

Dridex sends POST requests to the servers from the configuration to get further commands, waiting for 200 OK responses. Please note that these servers are not real C&C servers but rather proxies for connecting to the real ones.

\n

\n

^{Figure 14 – The Dridex botnet infrastructure.}

The information which is sent by the malware to the C&C servers contains the following data:

\n
• Computer name
\n
• Botnet ID number
\n
• Type of request
\n
• OS architecture
\n
• List of installed software
\n

This data is encrypted with the RC4 algorithm, the key for which is stored among encrypted strings inside the malware.

There are at least 6 different types of request; among them are the following ones:

\n
• “list” – gets configuration
\n
• “bot” – receives bot module
\n

Putting IOCs together

The earlier the infection is caught, the better the chances of mitigation. To catch the infection as quickly as possible while spending the minimum amount of resources, we want to focus on the initial delivery stage.

However, detection is only one aspect. We may confidently say that something is malicious, but we also want to classify the threat. To do so, we have to be sure that this particular malware is indeed Dridex.

Let’s take a look at the Dridex infection chain again and determine the different stages which we can use for its detection and identification:

\n

\n

^{Figure 15 – Different stages of Dridex detection.}

At different stages of the Dridex infection, we can use the following indicators for its detection.

\n
• 1^st stage, malicious documents:\n
  \n
  • Hashes of the documents
  \n
  \n
  \n
  • Images inside documents
  \n
  \n
  \n
  • Internal structure of the document
  \n
  \n
  \n
  • Macros used inside
  \n
  \n
\n
• 2^nd stage, servers:\n
  \n
  • Domains
  \n
  \n
  \n
  • URLs
  \n
  \n
\n
• 3^rd stage, loaders and payloads:\n
  \n
  • Hashes of the samples
  \n
  \n
  \n
  • IP addresses in the configuration file
  \n
  \n
\n

Why are so many factors important?

We have seen a correlation between infrastructures and indicators of Dridex and other prevalent malware families such as Emotet and Ursnif. Malicious documents share common indicators when used for the delivery of all the malware mentioned above. Some C2 servers – or to be precise, proxy servers – are used both by Dridex and Emotet, though ports and connection types are different.

That’s why we have to analyze a lot of details before we draw a conclusion of what malware we’re dealing with. The more unique factors related to a particular botnet we have, the easier it is to say if another attack has the same patterns.

The ideal way to classify malware is of course getting and analyzing the final payload: if it’s Dridex, then everything that was launched before it is classified as Dridex as well. However, it may take some time (sometimes a significant amount after the initial malicious document is obtained) before the result is known. We can do the classification faster, with high confidence, by analyzing all the indicators we get at the earliest stages of infection chain.

IP addresses to draw a map

Another interesting note is utilizing the same network for downloading Dridex samples. We analyzed domains used for this purpose, resolved their IPs and discovered that quite a few of them reside in the same network 84.38.180.0/22 with less than 1024 addresses available in total. Network belongs to Russian ASN Selectel that rarely takes down the malicious content or spam.

We saw the following IP addresses linked to Dridex domains in the 84.38.180.0/22 network (and other networks within the same ASN). Dates show the first time the Dridex domain pointed to the corresponding IPs:

While this factor alone is not enough to identify Dridex, this is a good auxiliary detail to refer to when dealing with Dridex IOCs.

Detection

The graphs below show Dridex spikes on different dates when we caught the incoming threats at its earliest stages.

\n

\n

^{Figure 16 – Dridex infection spike on June 29.}

\n

\n

^{Figure 17 – Dridex infection spike between July 6 – July 8.}

It is crucial to be able to intercept Dridex infection as early as possible. In many cases, if the spam is not being sent for several days consecutively, like it was between July 6 and July 8, the botnet activity slows down the next day and we do not get as many IOC matches as during its spike. Given that new infections appear at around afternoon UTC+3, we have less than 12 hours to react to the incoming threat.

Dridex development

Since July 22 we haven’t observed any fresh Dridex spam samples. Dridex made a re-appearance on September 7, showing a massive increase in its activity spike for 2 consecutive days:

\n

\n

^{Figure 18 – Recent September spike in Dridex activity.}

Dridex operators updated the 1^st stage of Dridex execution: they have added more URLs from where payload may be downloaded – as opposed to the single URL in the earliest versions of malicious documents. Now their number may be as high as 50 within the single document.

We’re constantly monitoring this botnet and detecting its payload at different stages of execution.

We hope this publication provided useful insights on different variants and methods to deal with this threat. We also believe that these methods may be applied when encountering other threats as well.

As cyber attacks become increasingly evasive, more controls are added, making security more complicated and tedious to the point that user workflows are affected. Until now.

Fueled by the Power of ThreatCloud, the Most Powerful Threat Intelligence and AI technologies  to prevent unknown cyber threats
SandBlast Network provides the best zero-day protection while reducing security overhead and ensuring business productivity.

Protection signatures

Banker.Win.Dridex.A
Banker.Win.Dridex.B
Banker.Win.Dridex.С

Banker.Win.Dridex.D

Banker.Win.Dridex.E

Banker.Win.Dridex.F

Banker.Win.Dridex.gl.H

Banker.Win.Dridex.J

Banker.Win.Dridex.K

IOCs

Below we list some of the indicators linked to Dridex. Please note that the list is not full by any means.

Domains:

IP addresses:

Dridex 1^st layer proxy C&C servers:

URLs:

Hashes (malicious documents):

Hashes (malware samples):

Referred Sources

\n
1. The Malware Dridex: Origins and Uses // https://www.cert.ssi.gouv.fr/uploads/CERTFR-2020-CTI-008.pdf
\n
2. Chasing cybercrime: network insights of Dyre and Dridex Trojan bankers //
   https://www.blueliv.com/downloads/documentation/reports/Network_insights_of_Dyre_and_Dridex_Trojan_bankers.pdf
\n
3. Dridex: A History of Evolution // https://securelist.com/dridex-a-history-of-evolution/78531/
\n
4. Evolution of the GOLD EVERGREEN Threat Group //
   https://www.secureworks.com/research/evolution-of-the-gold-evergreen-threat-group
\n
5. Dridex (Bugat v5) Botnet Takeover Operation //
   https://www.secureworks.com/research/dridex-bugat-v5-botnet-takeover-operation
\n
6. ZeuS Virus // https://usa.kaspersky.com/resource-center/threats/zeus-virus
\n
7. ZeuS versions // https://zeusmuseum.com/
\n
8. More than 100 arrests, as FBI uncovers cyber crime ring // https://www.bbc.com/news/world-us-canada-11457611
\n
9. Evgeniy Bogachev // https://www.fbi.gov/wanted/cyber/evgeniy-mikhailovich-bogachev
\n
10. Maksim Yakubets // https://www.fbi.gov/wanted/cyber/maksim-viktorovich-yakubets
\n
11. Bugat Botnet Administrator Arrested and Malware Disabled // https://www.fbi.gov/contact-us/field-offices/pittsburgh/news/press-releases/bugat-botnet-administrator-arrested-and-malware-disabled
\n
12. Two Russians Indicted Over $100M Dridex Malware Thefts // https://www.bankinfosecurity.com/two-russians-indicted-over-100m-dridex-malware-thefts-a-13473
\n
13. Dridex Banking Trojan Makes a Resurgence, Targets US // https://www.bankinfosecurity.com/dridex-banking-trojan-makes-resurgence-targets-us-a-9079
\n
14. TA505 group updates tactics and expands the list of targets // https://securityaffairs.co/wordpress/90472/cyber-crime/ta505-recent-campaigns.html
\n
15. Email scam aims to drop Dridex on machines by impersonating FedEx, UPS // https://www.cyberscoop.com/fedex-ups-dridex-email-scam-votiro/
\n
16. Process Injection and Manipulation // https://www.deepinstinct.com/2019/09/15/malware-evasion-techniques-part-1-process-injection-and-manipulation/
\n
17. Dridex’s Bag of Tricks: An Analysis of its Masquerading and Code Injection Techniques // https://securityboulevard.com/2019/07/dridexs-bag-of-tricks-an-analysis-of-its-masquerading-and-code-injection-techniques/
\n
18. Dridex – From Word to Domain Dominance // https://thedfirreport.com/2020/08/03/dridex-from-word-to-domain-dominance/
\n
19. Treasury Sanctions Evil Corp, the Russia-Based Cybercriminal Group Behind Dridex Malware // https://home.treasury.gov/news/press-releases/sm845
\n
20. The FSB’s personal hackers // https://meduza.io/en/feature/2019/12/12/the-fsb-s-personal-hackers
\n
21. Malware Analysis of Dridex, BitPaymer and DoppelPaymer Campaigns // https://lifars.com/2019/11/analysis-of-dridex-bitpaymer-and-doppelpaymer-campaign/
\n
22. BitPaymer Source Code Fork: Meet DoppelPaymer Ransomware and Dridex 2.0 // https://www.crowdstrike.com/blog/doppelpaymer-ransomware-and-dridex-2/
\n
23. WastedLocker: A New Ransomware Variant Developed By The Evil Corp Group // https://research.nccgroup.com/2020/06/23/wastedlocker-a-new-ransomware-variant-developed-by-the-evil-corp-group/
\n
24. Reverse Engineering Dridex And Automating IOC Extraction // https://www.appgate.com/blog/reverse-engineering-dridex-and-automating-ioc-extraction
\n