NOVEMBER 20, 2018

Emotet is back in full swing with massive, North-America focused E-Mail campaign

Emotet, one of the most prolific financial malware threats of the last two years is being spread en-masse these days as part of a wide E-mail campaign

Emotet, one of the most prolific financial malware threats of the last two years is being spread en-masse these days as part of a wide E-mail campaign. Since the beginning of November, Deep Instinct (as well as other security vendors) has witnessed a significant rise in Emotet activity. Emotet variants that surfaced in October, included new e-mail exfiltration capabilities. It seems that threat actors behind Emotet are now using harvested email accounts, trying to further expand the malware outreach.

Data from Deep Instinct’s D-Cloud (Deep Instinct’s threat-intelligence and telemetry cloud) indicates the attack is focused in North-America, with some targets found in other parts of the world - EMEA and APAC. We have spotted targets spanning across multiple verticals such as healthcare, education, financial services and hospitality to name a few. Since the beginning of November, we’ve identified and prevented thousands of attempted infections, carried out by a large set of droppers and 2nd stage payloads, which are mutated repeatedly on a daily basis.

Background

Emotet is one of the most prevalent financial malware threats of the last years. It first appeared in German speaking countries in Europe around mid-2014, and after two years without significant activity, Emotet reappeared in 2017, attacking mainly in the UK and US.

From its very first version Emotet spread mainly via Spam campaigns, imitating financial statements, transfers and payment invoices. Once it is dropped and run, Emotet intercepts and logs network traffic, injects to browsers and tries to access banking sites, in order to steal and store financial data.

In recent attacks (as of 2017), the dropper in use is usually an Office document containing VBA which downloads the PE payload, which might also drop additional malware. Emotet has developed since its reappearance in 2017 when it started adopting new evasion techniques. One example is searching for common applications found on security products such as debuggers and for common usernames found on sandboxing environments. And another is a rather rare code-injection technique where instead of using RunPE based process hollowing for injection, some new Emotet versions use windows API functions to create a Timers-queue timer, which call Emotet’s payload instead of calling the windows API callback function.

Emotet’s Back, Again…

Since its return to the financial malware scene in 2017, Emotet has been replicated and mutated frequently, both manually and by using automation to generate large numbers of variants. Emotet’s samples are based on continuous mutations (which vary in scope, complexity and evasion) of droppers and payloads for each target or attack wave. The latest attack wave is based on a set of mostly simple mutations of several dozen newly prepared droppers and payloads.

In several occasions in 2018 Emotet’s infection infrastructure and droppers have also been used to spread other financial malware/spyware such as Trickbot and Dridex. We can confirm this is case in the latest Emotet campaign – several droppers communicate with C2s serving both Emotet and TrickBot payloads.

As seen in previous attacks, the current campaign’s emails are financially related.

[caption id="attachment_4711" align="aligncenter" width="839"] Spam email and malicious attachment of the Emotet dropper, from the November 2018 campaign[/caption]

 

 

The droppers are either PDFs carrying malicious JavaScript or MS-office files with an obfuscated malicious Macro embedded. The content of the documents aims to lure users to allow and run the active content which is essential for infection to occur.

Once enabled and run, the malicious script (JS/VBA) will drop and execute Emotet’s payload with a simple cmd.exe command on the victim’s machine. As mentioned earlier, in some cases, the payload dropped is a of different malware strain known as Trickbot. This recurring scenario where Trickbot and Emotet share dropper and payload distribution infrastructures, or one is using the other as a secondary payload or a loader, might indicate a shared backend environment, in attackers have implemented a logic or mechanism which controls and decides the malware to serve.

Associating many of the new payloads to Emotet can be done based on several static artifacts known to be related to Emotet. In addition, Deep-Instinct’s deep-learning malware classification module (a secondary neural network used to classify malware to threat types and families) classifies the payloads which are part of this campaign as Spyware, immediately upon prevention.

[caption id="attachment_4758" align="aligncenter" width="974"] Event page in Deep-Instinct’s management console following Emotet payload prevention[/caption]

 

The new, large-scale campaign proves Emotet is here to stay and remains an imminent threat. Its polymorphic and mutating nature make its detecting challenging to many anti-malware products. New dropper and payloads variants are coming out daily and are successful at evading a wide range of solutions as is indicated by reports of infection as well as multi-scanner threat intelligence.

Deep Instinct customers are fully protected from Emotet. All new droppers and payloads are detected and prevented pre-execution using Deep Instinct’s Deep-Learning based solution.

 

IoC (prevalent samples)

Droppers

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1f09bdbe1013422deff83817196f4c3bf6a9c83481c485d69851c0c0ae9d5f92

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a2ca4385f6cad68879e4d279c0a24bbfb8e105e9ba54397e87ee1f5433822e7f

a54e00135723727834499e280282d93ffe9d6f79adc224b012a2e3660a0c03a2

c06786289db6e826a8b2e0129bf90355bec19d6bf399ce3fa5603537ddbdcbd9

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4b24be8697cb45f22249dab86c46bd77cfdd8eac2c237aecf0d14de99578ae53

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1ebcecb51f02464ffe76c566dce08bca74af6f0213e4e62ae554bcf7edd8e36e

2074aa67a1a9dd4573a5fd34359e9a0b551faff26cb55f4f67593036944adcd5

28b434b6d8ba77390fddefe16ba0e488d507211b384d34cf3eead7fc5a95a998

2e8c933b1a647c86a2b97c1aaad5f8670272f3a6cd991bdef59513f9e9516127

39a36eee98f1e55f71b6bf80e9c87f4f9c1683c45739075dcc5241e2e98bb600

3b971f116d4dc6f32146729441cae789e3fb4860f435fe1647e70a63f1655a4a

4a455e0a53007d2bc3092d2ed1ba66ca53993255f154100d6e4675822aeff947

4dd713cba9002fb3d769973a7cb59fd21e304ebbdc48c2814ff0692d69201e64

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58b152ad9500d8df9bf401cfa0505f03012ea99f5b216f78a6c661473c8967c3

633b5b4541f0f037760050ca6a8b8717635ee4ce76baa1d4717d7b0f08ae0bb8

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73d7f5cabfc82d7bd8a54e03ecb51567d81ac5dfa8dc1bd36670daede4e6c482

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Payload

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0ea0d10daa8441022afe01bc1bdae16d5a858b77311c3f71a6d1c535e645e623

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