Introduction
\nRecently, 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.
\nBy 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.
\nIn 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.
\n\n
The Infection Chain
\n\nThe 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”:
\nEmail subject: military financing program
\nFile name: “Military Financing.xlsm”
\nSHA-256:
\nefe51c2453821310c7a34dca30540
\n21d0f6d453b7133c381d75e3140901efd12
\n
\n
\n
\n
\n
\n
\n
\n
\n
Fig 1: Decoy document
\n\n
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.
\n\nFig 2: The infection chain
\n\n
Once the macros are enabled, two files are extracted from hex encoded cells within the XLSM document:
\nThe 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.
\n\n
Modified functionality includes:
\n\n\n
Fig 3: MoveFileW function hook: adds payload “execute” and “inject” functionality.
\n\n
The following is a demonstration of how it actually works:
\n\nFig 4: Remote payload execution demo
\n\n
\n
Victims
\nAs described in the infection flow, one of the first uses of the AutoHotKey scripts is to upload a screenshot from the compromised PC.
\nThe directory which those screenshots were uploaded to was left exposed, and could have been viewed by browsing to the specific URL:
\n\nFig 5: Open directory with victims’ screenshots
\n\n
However, those screenshot files were deleted periodically from the server, and eventually the “open directory” view was disabled.
\nUntil that time, we were able to ascertain some of the victims of these attacks, as most of the screenshots included identifying information.
\nFrom 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:
\nIt 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.
\nNevertheless, 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.
\n\n
Previous Campaigns
\nWhile 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.
\nDelivery
\nThe 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.
\nFor 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\nFig 6: SFX archive decoy image
\n\n
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.
\nThere 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:
\n\nFig 7: Russian decoy document
\n\n
SHA-256: 67d70754c13f4ae3832a5d655ff8ec2c0fb3caa3e50ac9e61ffb1557ef35d6ee
\nAfterwards, it would display finance-related Russian content:
\n\nFig 8: Russian decoy document – after macros are enabled
\n\n
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.
\n\n
The Payload
\nThroughout the campaigns multiple changes to the functionality of the malicious TeamViewer DLL, were introduced. Below are the feature highlights of each version:
\nFirst Variant (?-2018)
\n\n
Second Variant (2018)
\nFig 9: Help commands found in malicious DLL
\n
\n
Third Variant – as observed in the current campaign (2019)
\n\n
Attribution
\nAlthough 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.
\nBy following the trail from the previous campaigns we were able to find a `CyberForum[.]ru` user that goes by the name “EvaPiks”.
\nIn multiple instances, the user would suggest, or be advised by other users to use, some of the techniques we witnessed throughout the campaigns.
\nThe following are translated snippets from some of the threads in the forum:
\n\nFig 10: EvaPiks – suggested macro code
\n\n
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.
\nIn the following screenshot, we see EvaPiks suggesting a Delphi code snippet that “works great”:
\n\nFig 11: EvaPiks – suggested PHP code
\n\n\n
Fig 12: Panel URL found in DLL code
\n\n
In addition to the similar Delphi usage, the URL mentioned in the forum `(newpanel_gate/gate.php)` was used in one of the attacks.
\nBack in 2017, EvaPiks was the one seeking advice on the forum, with questions about API function call interception:
\n\n\n
Fig 13: EvaPiks – seeking Delphi hooking advise on the forums
\n\n\n
Fig 14: Hooks found in DLL code
\n\n
The same hooking technique of `CreateMutexA` and `SetWindowTextW` functions was utilized in the sample we have observed as well.
\nAn additional screenshot from the forum reveals how EvaPiks is experimenting with new features, some of which were integrated into the malicious DLLs:
\n\nFig 15: EvaPiks – development PC screenshot from the forums
\n\n
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:
\n\nFig 16: EvaPiks – complaining about a fellow user on a carding forum
\n\n
The Attack Infrastructure
\nAt 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]
\nAdditionally, we observed the following login panels, on the C&C servers utilized by the malicious DLLs:
\n\nFig 17: “Cyber Industries” login panel hosted on 193.109.69[.]5
\n\n\n
Fig 18: login panel hosted on 146.0.72[.]180
\n\n
Summary
\nOn 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.
\nOn 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.
\nThe 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.
\nHowever, 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.
\n———————————————————————————————————————————————————————————————————————-
\nCheck Point’s SandBlast
\nThe malware used in this attack was caught using Check Point’s Threat Emulation and Threat Extraction.
\nThreat 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.
\n\n
IOCs
\nDLLs
\n`013e87b874477fcad54ada4fa0a274a2
\n799AB035023B655506C0D565996579B5
\ne1167cb7f3735d4edec5f7219cea64ef
\n6cc0218d2b93a243721b088f177d8e8f
\naad0d93a570e6230f843dcdf20041e1e
\n1e741ebc08af09edc69f017e170b9852
\nc6ae889f3bee42cc19a728ba66fa3d99
\n1675cdec4c0ff49993a1fcbdfad85e56
\n72de32fa52cc2fab2b0584c26657820f
\n44038b936667f6ce2333af80086f877f`
Documents
\n`4acf624ad87609d476180ecc4c96c355
\n4dbe9dbfb53438d9ce410535355cd973`
C&Cs
\n`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`
\n
Appendix A: Yara Rule
\n`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\n
\n
Appendix B: Online services of interest
\nBanks
\n`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`
\n
Crypto Markets
\n`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`
\n
Online Shops
\n`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`
\n
\n
\n","status":"PUBLISHED","fileName":null,"link":"https://research.checkpoint.com/2019/finteam-trojanized-teamviewer-against-government-targets/","tags":[],"score":0.008337032049894333,"topStoryDate":null},{"id":"RS-24476","type":"Research_Publications","name":"Stopping Serial Killer: Catching the Next Strike","author":null,"date":1609783711000,"description":"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… Click to Read More","content":"\n
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?
\n\n\n\nTo 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.
\n\n\n\nWe 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.
\n\n\n\nThe 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.
\n\n\n\nDridex 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.
\n\n\n\nIn 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.
\n\n\n\nDridex 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.
\n\n\n\nThe key names in this story:
\n\n\n\nZeus 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.
\n\n\n\nThe timeline below shows key points in ZeuS evolution:
\n\n\n\n\n\n\n\nFigure 1 – Chronology of ZeuS evolution.
\n\n\n\nWhen 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.
\n\n\n\nIn 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\n\n\n\n\n\nFigure 2 – Description of Evgeniy Bogachev on the FBI site.
\n\n\n\nAfter 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.
\n\n\n\nMore names appear on the stage at this time.
\n\n\n\nMore names connected to Dridex can be found in US treasury sanctions statement.
\n\n\n\nThe timeline below shows some milestones in Dridex evolution:
\n\n\n\n\n\n\n\nFigure 3 – Chronology of Dridex evolution.
\n\n\n\nDridex 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.
\n\n\n\nIn 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.
\n\n\n\nAt 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\n\n\n\n\n\nFigure 4 – Description of Maksim Yakubets on the FBI site.
\n\n\n\nThere is also evidence of Maksim’s luxurious lifestyle, undoubtedly due to income from his malicious activities.
\n\n\n\n\n\n\n\nFigure 5 – Cars, girls, money; the luxurious lifestyle of Maksim Yakubets.
\n\n\n\nTo date, Maksim Yakubets has not been apprehended by law enforcement.
\n\n\n\nAs mentioned previously, in 2020, Dridex topped the lists of the most prevalent malware families in the world.
\n\n\n\nBefore 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.
\n\n\n\nWhen 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.
\n\n\n\nWhen 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\n\n\n\n\n\nFigure 6 – Dridex infection chain execution flow.
\n\n\n\nDridex targets different high-profile entities from various parts of the world:
\n\n\n\nTo 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\n\n\n\n\n\nFigure 7 – Examples of lures.
\n\n\n\nWhen the victim clicks the link, either the archive with the malicious document or the malicious document itself is opened.
\n\n\n\nWhen 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\n\n\n\n\n\nFigure 8 – Initial detection rate of the Dridex delivery file.
\n\n\n\nThe same is true for other delivery files.
\n\n\n\nThe Dridex sample consists of the loader and the payload. We discuss key points of each part below.
\n\n\n\nThe 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\n\n\n\n\n\nFigure 9 – Loop with 0xBEBBE7C (around 200 million) iterations calling OutputDebugStringW.
\n\n\n\nThe output looks like this in the log:
\n\n\n\n\n\n\n\nFigure 10 – Dridex debug messages that overwhelm the analysis log.
\n\n\n\nThe 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\n\n\n\n\n\nFigure 11 – Example of the call resolution in the Dridex payload.
\n\n\n\nAll the functions important for key Dridex’ tasks are called this way.
\n\n\n\n\n\n\n\nFigure 12 – Example of resolved calls to Internet functions.
\n\n\n\nWe used the Labeless tool to resolve obfuscated function calls.
\n\n\n\nStrings in the malware are obfuscated using the RC4 algorithm and the decryption key stored inside the sample.
\n\n\n\nThe main point of interest inside the payload is its configuration. It contains the following important details:
\n\n\n\nAn example of the configuration:
\n\n\n\n\n\n\n\nFigure 13 – Example of the Dridex configuration inside the payload.
\n\n\n\nThe bot ID in this example is 12333. The Command and Control servers are:
\n\n\n\nDridex 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\n\n\n\n\n\nFigure 14 – The Dridex botnet infrastructure.
\n\n\n\nThe information which is sent by the malware to the C&C servers contains the following data:
\n\n\n\nThis data is encrypted with the RC4 algorithm, the key for which is stored among encrypted strings inside the malware.
\n\n\n\nThere are at least 6 different types of request; among them are the following ones:
\n\n\n\nThe 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.
\n\n\n\nHowever, 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.
\n\n\n\nLet’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\n\n\n\n\n\nFigure 15 – Different stages of Dridex detection.
\n\n\n\nAt different stages of the Dridex infection, we can use the following indicators for its detection.
\n\n\n\nWe 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.
\n\n\n\nThat’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.
\n\n\n\nThe 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.
\n\n\n\nAnother 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.
\n\n\n\nWe 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:
\n\n\n\nIPs | \nDate | \nDomains | \n
---|---|---|
84.38.182.248 | \nMay 10 | \nrokadorc.com nrokadorc.com | \n
84.38.183.77 | \nJune 17 | \njuneusdousigninc.com usdousigninc.com | \n
84.38.182.236 84.38.183.213 | \nJune 22 | \nmarutoba.com terrasimonad.com enterassimonad.com | \n
84.38.181.195 | \nJune 28 | \ncaranatrium.com | \n
84.38.183.114 84.38.183.237 | \nJuly 06 | \nmenodlap.com turendong.com madustag.com | \n
While this factor alone is not enough to identify Dridex, this is a good auxiliary detail to refer to when dealing with Dridex IOCs.
\n\n\n\nThe graphs below show Dridex spikes on different dates when we caught the incoming threats at its earliest stages.
\n\n\n\n\n\n\n\nFigure 16 – Dridex infection spike on June 29.
\n\n\n\n\n\n\n\nFigure 17 – Dridex infection spike between July 6 – July 8.
\n\n\n\nIt 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.
\n\n\n\nSince 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\n\n\n\n\n\nFigure 18 – Recent September spike in Dridex activity.
\n\n\n\nDridex operators updated the 1st 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.
\n\n\n\nWe’re constantly monitoring this botnet and detecting its payload at different stages of execution.
\n\n\n\nWe 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.
\n\n
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.
\nFueled 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.
\n
Banker.Win.Dridex.A
Banker.Win.Dridex.B
Banker.Win.Dridex.С
Banker.Win.Dridex.D
\nBanker.Win.Dridex.E
\nBanker.Win.Dridex.F
\nBanker.Win.Dridex.gl.H
\nBanker.Win.Dridex.J
\nBanker.Win.Dridex.K
\n\n\n\nBelow we list some of the indicators linked to Dridex. Please note that the list is not full by any means.
\n\n\n\nDomains:
\n\n\n\nrokadorc[.com | \n
nrokadorc[.com | \n
juneusdousigninc[.com | \n
usdousigninc[.com | \n
marutoba[.com | \n
terrasimonad[.com | \n
enterassimonad[.com | \n
caranatrium[.com | \n
menodlap[.com | \n
turendong[.com | \n
madustag[.com | \n
fattnumdelordine[.com | \n
armomaq[.com | \n
caissefamilylaw[.com | \n
secretpath[.xyz | \n
IP addresses:
\n\n\n\n84[.38.181.195 | \n
84[.38.182.236 | \n
84[.38.182.248 | \n
84[.38.183.77 | \n
84[.38.183.114 | \n
84[.38.183.213 | \n
84[.38.183.237 | \n
Dridex 1st layer proxy C&C servers:
\n\n\n\nhttps://45.79.8.25[:443 | \n
https://185.201.9.197[:9443 | \n
https://217.160.78.166[:4664 | \n
https://108.175.9.22[:33443 | \n
https://51.38.124.206[:443/ | \n
https://207.180.230.218[:3389/ | \n
https://2.58.16.87[:8443/ | \n
https://45.177.120.36[:691/ | \n
https://52.114.132.73[:443 | \n
https://192.232.251.32[:443 | \n
https://162.144.41.190[:443 | \n
https://40.122.160.14[:443 | \n
https://67.213.75.205[:443 | \n
https://217.160.78.166[:4664 | \n
https://108.175.9.22[:33443 | \n
https://185.201.9.197[:9443 | \n
URLs:
\n\n\n\nhttps:[//discuss.ojowa.com/themes/wowonder/javascript/tinymce/js/dkfjgbji.gif | \n
https:[//sjoeberg.nu/a/jdfggo.rar | \n
https:[//greatstr.com/webadmin/djfhgeh.pdf | \n
https:[//axalta.grupojenrab.mx/wp-admin/ssfisjgniwerg.pdf | \n
https:[//bombshellshow.me/wp-content/jdfggo.rar | \n
https:[//amaimaging.net/wp-content/rjkthgowertgoiwe.zip | \n
https:[//pharmacy.binarybizz.com/vendor/njdfhgeroig.rar | \n
https:[//construtorahabite.com.br/wpadmin/rjkthgowertgoiwe.zip | \n
https:[//drinkangola.com/wp-content/plugins/wordpress-seo/config/composer/dkfjgbji.gif | \n
https:[//mcciorar.iglesiamcci.cl/njdfhgeroig.rar | \n
https:[//eduserve.sezibwa.com/images/njdfhgeroig.rar | \n
https:[//idklearningcentre.com.ng/wp/wp-content/plugins/jetpack/3rd-party/dkfjgbji.gif | \n
https:[//agencia.fal.cl/wp-includes/njdfhgeroig.rar | \n
https:[//sweepegy.com/djfhgeh.pdf | \n
https:[//tallermecanicoyllantera.grupojenrab.mx/wp-admin/rjkthgowertgoiwe.zip | \n
https:[//neocuboarquitetura.com.br/viewer/ssfisjgniwerg.pdf | \n
https:[//vyvanse.co/auth14/zxc.zip | \n
https:[//minsann.se/NewFolder/ad/style/theme/upload/84348fh34hf.pdf | \n
https:[//admin.grandoceanvilla.com/pug/includes/css/84348fh34hf.pdf | \n
https:[//glowtank.in/js/ssfisjgniwerg.pdf | \n
https:[//leandrokblo.com/wp-content/plugins/w3-total-cache/ini/apache_conf/dkfjgbji.gif | \n
https:[//medszoo.in/jdfggo.rar | \n
https:[//properties.igpublica.com.br/excelPo/rjkthgowertgoiwe.zip | \n
https:[//coomiponal.com/simulador/zxc.zip | \n
https:[//inkrites.com/wp-content/themes/zerif-lite/ti-prevdem/img/84348fh34hf.pdf | \n
https:[//manogyam.com/storage/njdfhgeroig.rar | \n
https:[//radiantmso.com/wp-content/plugins/smart-slider-3/library/media/dkfjgbji.gif | \n
https:[//etsp.org.pk/uploads/jdfggo.rar | \n
https:[//tmpartners-gh.com/djfhgeh.pdf | \n
https:[//heraldfashion.store/wp-admin/zxc.zip | \n
https:[//danojowacollection.com/djfhgeh.pdf | \n
https:[//leboudoirstquayportrieux.fr/image/ssfisjgniwerg.pdf | \n
https:[//quiz.walkprints.com/wp-includes/js/tinymce/themes/inlite/84348fh34hf.pdf | \n
https:[//siebuhr.com/pmosker/zxc.zip | \n
https:[//karyagrafis.com/njdfhgeroig.rar | \n
https:[//businessquest.com.my/schedule/jdfggo.rar | \n
https:[//maisaquihost.com.br/teste/rjkthgowertgoiwe.zip | \n
https:[//getsolar4zerodown.info/djfhgeh.pdf | \n
https:[//emyhope.com/wp-content/plugins/jetpack/_inc/blocks/84348fh34hf.pdf | \n
https:[//igpublica.com.br/asset/zxc.zip | \n
https:[//speakerpedia.in/images/zxc.zip | \n
https:[//timamollo.co.za/sitepro/jdfggo.rar | \n
https:[//eb3tly.online/njdfhgeroig.rar | \n
Hashes (malicious documents):
\n\n\n\n15d3edcf37b1e4d03a5c61c1c7752130a9899b978c94f80d8dabc45f416fc253 | \n
16b98e2156fb721a760cd3d4e5c1a8c18dee54f795c6d8624339e25c5e33c2b1 | \n
97defc4fa68d6d3d76226b2ab02c8c3c0544b4d035083057b52d101f5884cbf1 | \n
99842250e5da8f987227c22d864ea6552cbf176710cd5c45f430bc2765cbf534 | \n
9a54d7a8551641f3c77a6f2743890f30e5d5ed4854fcadb25fc1a45bf928cefb | \n
a633110b7d2f045d88b43c95838372d556de7bf9d2543149b9e5a984f9377539 | \n
cbbb3ffd6f20060d8176954afb0f26fb220a281fd0e49facd02be8f597f24645 | \n
d3e9f6933d519b6bd1514ceaaa14df64722214c0c6c2a60a6924c92f284b3c08 | \n
d77234374d79b24022c26ecdd16a684ae7e94efba502422d74852b0eddd4f1b4 | \n
d943478cb08756734a766eb5da189eef45577c29d33cbd679976e5cb97f2c9f2 | \n
Hashes (malware samples):
\n\n\n\n84d3573747fbdf7ca822fd5a48726484c8b617e74a920dc2a68dd039b8f576fd | \n
a633e85176faf87dfa99e89e559e3be3f2854592a3adb9f6ea6aab88c06dd198 | \n
ad4d2f9fcadce231e18e50de3bb58028ae13eaf76a9c085d0073230e0fa17a9e | \n
b0699861417da2e3626eb78d62d305b7ca5e03f06e5e6bfd0eea99d64306495e | \n
b5b71c61a29f80c667772f5d008789816e0c7a53193536fc660a6f72009b23de | \n
b66a5d391335b6dc827225b6531f172151d8a87c7514de789bcaf1999b0645ff | \n
c37accc1f995cb32235edbea877813109627eca4b209f060bee357489c6bb31b | \n
c6de2ef240cdca97e8d5d6fdcfc7bfd8d5c81a47204d268bd08e4b963d66a64b | \n
c8cca37f43f4aa66b4bfbf811931c57971d2f1571cfebbb7d24235c07e108f26 | \n
cc33c8c4eb3588fdd48ddb081f77040283c2f6b8c37777f8202b858b64a5952b | \n
d18d211cf75fbc048d785af92b76a1aa7a01e381313b1a5e66e9cf564cbe78d4 | \n
f8c974a6572fd522a64d22da3bf36db7e912ccb700bd41623ed286f1e8b0e939 | \n
fa61c3c9e2089deb3f2b40333f5ee0860177692c436c50b07eef85993a1dbfa9 | \n
fcc0db0ce710f68915b4d73274d69bb5765012b02631bb737c66a32a9a708aab | \n