VirusTotal += Mandiant Permhash: Unearthing adversary infrastructure and toolkits by leveraging permissions similarity

Last Monday our colleagues over at Mandiant rolled out Permhash. In their own words, Permhash is an extensible framework to hash the declared permissions applied to Chromium-based browser extensions and APKs allowing for clustering, hunting, and pivoting similar to import hashing and rich header hashing. We are excited to announce that we have been working closely with Jared Wilson on the Mandiant side to support Permhash similarity pivoting in VirusTotal.

VirusTotal already supports multiple similarity pivots: vhash (VirusTotal’s home-grown static feature hash), behash (same concept but for dynamic analyses), ssdeep, imphash, TLSH, telfhash, main icon dhash, etc. We have blogged extensively in the past about how similarity can be used to expand context and map out threat campaigns, we even hosted a joint webinar with Trend Micro and Trinity Cyber on this very topic. But let’s see how Permhash builds upon VirusTotal’s threat hunting swissknife and provides yet another orthogonal vehicle to track threat actors and their toolkits, going beyond IoCs and rather focusing on repeatable toolkit patterns.

In their article, Mandiant writes about UNC3559 and CHROMELOADER. UNC3559 is a financially motivated threat cluster that has distributed the CHROMELOADER dropper since at least early 2022. CHROMELOADER is a dropper that subsequently downloads a malicious Chrome extension, which can display advertisements in the browser and capture browser search data. Mandiant shares a particular CHROMELOADER manifest, you can use that initial input to pivot to other similar files via Permhash, and you can combine it with other search modifiers to narrow down results to actual Chrome Extensions as opposed to manifests:

 

permhash:d4d1b61f726a5b50365c8c18b2c5ac7ab34b3844e0d50112f386dfd875b6afac type:crx

With a single click we get to 19 other potential variations by the same threat group, many of them with low detection coverage by the industry (we are starting to get proactive):

 

Now we can dig further into these to understand the group’s infrastructure and modus operandi. For instance, we can leverage VirusTotal Commonalities to identify patterns that repeat themselves across all variations, as well as distribution infrastructure:

 

 

That’s how, among other ranked aggregations, we are able to identify the following in-the-wild distribution URLs, all of which were fully undetected at the time of writing:

 

The use of the .xyz TLD and archive.zip file name stand out as a repeatable pattern that may be combined with others to climb the pyramid of pain and hunt for the group based on behavioral patterns and TTPs, as opposed to hashes. At the same time, Commonalities allow us to understand even more about the distribution vectors and kill chain:

Indeed, the execution parents tell us about those files that when detonated in our sandboxes drop the Chrome extensions under study. That’s how we can learn that the first stage malware consists of both DMG files (6 files, example) and Powershell scripts/commands (3 files, example):

By the way, VirusTotal Code Insight comes in very handy in understanding the 3 powershell scripts that drop Chromeloader (see for yourself):

By iteratively calculating the commonalities of the first stage malware we can identify other repeatable patterns to detect these campaigns and even understand when and where has this group been active based on crowdsourced telemetry gathered from VirusTotal’s open community:

It seems to have been a relatively targeted campaign mostly targeting US orgs and active during July 2022.

This is by no means an exhaustive investigation but rather a quick post showcasing how Permhash similarity can work with other features in VirusTotal to mature our hunting program. As you can see, while EDR tools and other security technologies might not yet generate Permhash fingerprints to support threat hunting use cases, VirusTotal’s pivots and analytical capabilities allow us to translate it into actionable intelligence in the form of hashes but also related network indicators and repeatable patterns that may indeed be logged in common security telemetry being ingested in SIEMs/XDRs/TDRs/etc.

Moreover, now that we have a group of variants as opposed to a single instance, we can study those files or even leverage tools like VTDIFF to build a YARA rule that can be used to hunt within our environment or to track relevant adversaries going forward in time (Livehunt) and take proactive actions as they evolve. 

Oh, and one more thing, stay tuned because we will soon provide consolidated similarity searching across all similarity pivots taking into account prevalence and overlaps to identify best matches without having to search for each different similarity vector (vhash, ssdeep, permhash, imphash, etc.). 

Happy hunting!

Read More

Using similarity to expand context and map out threat campaigns

TL;DR: VirusTotal allows you to search for similar files according to different orthogonal notions (structure, visual layout, icons, execution behaviour, etc.). File similarity can be combined with the “have:” search modifier in order to gain more context about threats, e.g. what are the emails or URLs that distribute them.

This is the second blog post in our similarity series, the first article focused on how to trigger file similarity searches and the different similarity vectors at your disposal. In the context of this series we have also done a webinar that can be viewed on-demand, it focuses on using similarity to automatically produce optimal YARA rules to detect a given malware framework/family/campaign via VTDIFF.

This situation might sound familiar. As a SOC analyst or Incident Responder you are often confronted with files you know nothing about. Your SIEM describes their internal sightings and actions but fails to transmit the bigger picture. You are constrained by the narrow visibility of your corporate logs. Context is king and the problem is that you are fighting threat actors that operate globally with just a piece of the puzzle, your local data.

What is this file? Who is behind it? What is their modus operandi? How did it get there? Are there other related components? What does it do? Are there other variants that could have impacted my organization in the past? Any that could impact us in the future? How do I contain it? Your SIEM, case management system, EDR, firewall, IDS etc. don’t answer these questions. You are missing a necessary layer in your defense-in-depth security strategy.

VirusTotal is your saving grace. You jump into VT ENTERPRISE and look up the hash: threat reputation is useful, but you need further context. Your task is to identify IoCs that can be used for remediation, e.g. by blocking a command-and-control domain in the network perimeter, as well as artefacts that can be used for proactive threat hunting purposes, to determine whether there has been a breach and what is its scope. The issue is that sometimes VirusTotal does not have full context for a specific individual file in terms of sandbox reports, in-the-wild sightings, relationships, etc. and so your investigation might end here.

How to do it better

Isolated hashes are of limited value. Many times they are unique per victim or campaign, so a better idea would be finding the cluster/family/campaign they belong to in order to unearth remediation IoCs and threat hunting patterns. Most importantly, you need to leverage those groupings in order to surface command-and-control domains, dropzones, distribution URLs, phishing emails, etc. that can be used for mitigation and containment, and, to build proper understanding and situational awareness.

Similarity and the “have” search modifier to the rescue. Let’s imagine the initial hash that popped up as an alert in our environment was a first stage EMOTET dropper, i.e. a document that delivers a malicious payload through macros.





Threat reputation allows you to perform an immediate first assessment (alert triage), but other than that there is little context in terms of remediation IoCs and hunting artifacts. We still know nothing about how this file gets distributed, i.e. its delivery vector. Similarly, we fully ignore whether this is something spear phished exclusively against our organization or part of a larger campaign. What about the threat network infrastructure? Does it download additional payloads? Does it communicate with a command-and-control?

The next step in an incident response engagement - and this is what most analysts fail to do - is to jump into the file’s cluster (its family/framework/campaign) in order to expand context and surface IoCs. This is just one click away:



For documents there is a limited number of approaches to find similar files (other file formats will expose more), this said, they are very rich because they are fully orthogonal: structural features, visual layout, local sensitive fuzzy hashing, execution behaviour similarity. Let’s jump to other similar files based on the document’s visual layout by clicking on “Similar by icon/thumbnail” or on the thumbnail itself, located in the top right: main_icon_dhash:23232b2b00010000.




There are too many matches, we would have to iterate over every single one in order to surface particular patterns that may allow us to understand the campaign.

Finding phishing emails that distribute the threat

We can narrow down the search above to match exclusively those files that have been seen as an attachment in some email uploaded to VirusTotal:

main_icon_dhash:23232b2b00010000 AND have:email_parents
(Note that you can also use tag:attachment instead of have:email_parents)

We can now run through the matching files, open up their Relations tab and jump into the pertinent email parent, so as to understand the deception techniques being used in the campaign:


This particular instance poses as some kind of World Health Organization report on COVID. It is important to inspect all the other emails because not only will they tell us more about the lures, it will also allow us to identify targeted industries, geographical spread, activity time spans, etc. For instance, there could be other localized variants that could be targeting some other corporate branches. Access to these emails will not only give us greater insight into the attacker, it is also something we can leverage tactically in order to improve filtering in our email gateways.

Discovering URLs that distribute this threat

We want to see if this campaign is also being distributed via download URLs. If that´s the case we can block them in our network perimeter or use them to search across web proxy logs. Let’s ask VirusTotal whether any of the files in the cluster have associated in-the-wild URLs:
main_icon_dhash:23232b2b00010000 AND have:itw

We can now jump into the Relations tab in order to export these additional IoCs:



There are over 3K files with in-the-wild URLs, note that we can automate all of this via the API.

Identifying command-and-control/exfiltration infrastructure

The next step is to understand whether any of the machines in our corporate fleet are beaconing out to infrastructure tied to this campaign. At the same time, we will probably want to block the CnC and exfiltration points in order to mitigate the impact of historical undetected breaches. Let’s filter down the search to focus exclusively on those files that exhibited network communications when executed in a dynamic analysis sandbox:

main_icon_dhash:23232b2b00010000 AND have:behaviour_network



Most of the matching files have been analysed by several sandboxes participating in our multi-sandbox effort. This gives us unparalleled visibility into the campaign. For an attacker it is easy to evade a single sandbox, it is far more complex to do so for 17+ of them at the same time. Each one of them set up in a different geographical region, going out to the internet through a different IP address, running different OS versions, with different software and language packages installed, etc. As a result, we now have very interesting sightings in terms of infrastructure:


These communication points can be very easily triaged. Remember that VirusTotal also characterizes domains, IP addresses and URLs. Threat reputation for these domains further confirms that they are accurate IoCs:



The domain relationships (in-the-wild sightings) tell the same story:



We now have additional IoCs that we can feed into our stack in order to proactively defend our organization from other variants. As a bonus point, pivoting to other campaign files that have sandbox behaviour reports allows us to shed more light into other TTPs that we might be tracking via MITRE ATT&CK (e.g. installation, actions on objectives, etc.).

Gaining context through the community

Furthering on the use of the “have” search modifier, we can also leverage it to find files on which some VT Community user has placed a comment providing more context:

main_icon_dhash:23232b2b00010000 AND have:comments

Community comments often give us interesting details in terms of in-the-wild observations, malware capabilities, reverse engineering reports, attribution, etc. For example, in this particular case we learn about additional distribution URLs:

This other case helps us understand that this first stage is EMOTET and allows us to jump into a pastebin dump with further context about the campaign in terms of related hashes and network infrastructure:

Additional context

The “have” modifier accepts many other values, some of the more representative ones are:

• compressed_parents: the files were seen inside a compressed file uploaded to VirusTotal.
• pcap_parents: the files were seen in a network traffic recording uploaded to VirusTotal.
• embedded_(urls/domains/ips): a URL/domain/IP address pattern was extracted from the binary bodies of the files.
• behaviour: the files managed to execute in at least one sandbox and produced the pertinent dynamic analysis report.
• behaviour_registry: the files executed in a sandbox and interacted with the Windows Registry.
• crowdsource_yara_rule: the files match some YARA rule coming from open source community repositories, these rules often provide additional references and descriptions about a threat.

Summing up

VirusTotal aggregates orthogonal means to cluster together groups of related files. Files which may belong to the same malware family/framework/campaign/actor. These file similarity vectors range from structural features to dynamic analysis observations.

We started off with a single IoC for which we had little context, neither did VirusTotal, beyond basic threat reputation. By leveraging file similarity we managed to find thousands of other files related to the campaign/malware framework. Through the “have” search modifier we then narrowed down our searches to identify phishing emails used by the attackers, distribution URLs, additional network infrastructure such as CnCs and context shared by other threat researchers.

All of this is tactical intelligence that can be fed into network perimeter defenses, but also context that can be operationalized and digested into TTPs in order to characterize threat actors. Finally, this blog post presented an incident response scenario but the very same logic can be applied to threat actor tracking or campaign monitoring use cases.

This post was authored by Emiliano Martinez.

Read More