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CISO Summary

On March 15, CofenseTM Research reported that the Emotet botnet is changing the way it communicates, in a likely attempt to evade malware detection. Since then, Cofense IntelligenceTM has seen the same trend: Geodo-Emotet isn’t relying on cookies to make certain requests, instead performing HTTP POSTs to what seems to be the C2. Baking requests into cookies is a time-honored and easily detected pattern of  behavior. Switching this up makes it harder to see when the malware is calling home.

Moreover, Geodo-Emotet is now using a new infection chain, utilizing JavaScript files as droppers instead of macro-packed Office documents. These changes in behavior and delivery methods are the threat actors’ latest attempts to keep ahead of network defenders. They will very likely require security teams to adjust—once more.

Full Details

Cofense Intelligence has observed a change in the way that the Emotet botnet communicates, along with  the use of a new infection chain. In past versions, a compromised client would typically perform a GET request with data contained in the cookie value. As of approximately 11pm UTC on March 14th, this changed. The clients have begun to perform HTTP POST’s to what appear to be their C2’s. An educated guess: the primary driver behind this transition appears to be an attempt to bypass established detection methods. In tandem with this update, Geodo has begun experimenting with delivering its binaries with JavaScript files acting as droppers, and not via Office documents laden with macros as has been most common.

Historically, Geodo has passed data to its C2 using the Cookie field of the HTTP header. Information about the system, as well as identifiers, would be encrypted, wrapped in Base64 and added to the HTTP header before transport. This was a consistent and easily identifiable pattern of behavior, which led to near universal enterprise detection. Figure 1 shows an example of this exfiltration method.

Figure 1: An example of classic Geodo C2 comms using the Cookie field. Source: app.any.run

Despite being a valid and oft-used header field, there are several other tells – such as direct communication with an IP address for which no DNS resolution was performed. This, when combined with the cookie, is an easy way to identify a Geodo infection calling home.

The latest iteration of Geodo, however, has transitioned away from this legacy method to submitting data to its C2 via HTTP POST as a form. Figure 2 shows an example of this updated communication method.

Figure 2: The new method of C2 comms

Experimenting with JavaScript

Geodo operates various tiers of payload distribution by using payload-agnostic droppers and relying on the Windows file-type handlers to correctly execute what is downloaded. This means that payloads can be hot-swapped at any point during a campaign. This behavior was observed late in 2018 when a payload location, for a short period of time, swapped a Geodo executable for that of QakBot. By making the payload system agnostic, the actors behind Geodo can experiment with varying payloads without affecting the overall integrity of the infection chain. Despite the sophistication and robustness of the Geodo delivery infrastructure, the JavaScript payload observed by Cofense Intelligence was minimally obfuscated and immediately legible to an experienced eye. If one traces the execution, though, things begin to become a little bit murky. Figure 3 shows a snippet of the obfuscated dropper, verbatim.

Figure 3: The obfuscated payload showcasing cleartext strings

After deobfuscation, the flow of the code is somewhat easier to interpret. The code is broken out into 5 distinct functions, with two anonymous functions—one at the head and one at the tail—responsible for execution. Figure 4 shows the first two functions and an array.

Figure 4: Two functions responsible for shuffling an array and retrieving an element by index, respectively.

The shuffling function is likely there to slow down manual analysis of the file. It could also be used to defeat unsophisticated emulation techniques. The second function simply returns an item from an array by its index.

The next two functions, seen in figures 5 and 6, are responsible for downloading and response code verification, and looping through available URLs, respectively.

Figure 5: The code responsible for downloading payloads and verifying the response code

Figure 6: Looping through five URLs, and attempting to execute the retrieved payload

Although the dataset is entirely too small to accept as correlation, the use of 5 payload locations is in line with the standard Geodo modus operandi. During analysis, it was noticed that one of the payloads was not like the others, however. Figure 7 shows the rather interesting subject matter returned during analysis of the payload locations.

Figure 7: A blog page returned in lieu of a binary payload.

Figure 8 shows the code responsible for finding the path of, and writing files to, the %temp% directory.

Figure 8:  The dropper generates a pseudo-random filename as which to write the file

Figure 9 is the code responsible for kicking off the main functions of the script.

Figure 9: The code responsible for starting the download and execute operations. Comments added for clarity

With routine changes in behavior and delivery methods, Geodo’s operators consistently find ways to evolve how the botnet behaves—always attempting to stay ahead of the cat-and-mouse game they play with network defenders. The change in how form data is passed will almost certainly allow Geodo to overcome certain detection technologies, requiring immediate retooling. Identifying a highly dynamic family, such as Geodo, requires highly agile security infrastructure coupled with responsive threat intelligence.

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