Wednesday, February 21, 2024

,

Following MITRE's footsteps in analyzing malware behavior

The MITRE framework helps all defenders speak the same language regarding attackers' modus operandi. VirusTotal provides multiple data points where MITRE's Tactics and Techniques are dynamically extracted from samples when detonated in our sandboxes. In particular, samples' MITRE mapping can be found under the BEHAVIOR tab of a file's report. This data is searchable in VirusTotal Intelligence (VTI) with the help of a set of specific file search modifiers.
In this article, we'll illustrate how security analysts can leverage MITRE for malware detection and behavior-based hunting for ransomware and keylogger samples.

Hunting for Ransomware

The security industry historically identified a set of commonly used techniques in Ransomware campaigns, including inhibiting the system recovery and discovering local files and network shares for later data encryption, usually combined with exfiltration and/or Command and Control techniques.

Common TTPs of modern ransomware groups by Kaspersky

In VT Intelligence we can use 2 search modifiers to query files behavior mapped to MITRE ATT&CK:
In addition to the "attack_tactic" and "attack_technique" modifiers, VirusTotal provides extra modifiers listed on the Appendix I - Behavior search modifiers for procedures-based queries at the end of this post.
Let's do an example. We want to find samples given a set of ransomware-related techniques combined with the "behavior:CryptEncrypt" operating system API call (check Appendix I for details). Additionally, we specify the entity we are interested in (files) and the first submission date (fs) to filter out files submitted before 2024-01-01.
The resulting query is as follows:

Let's analyze in more detail one of the query's resulting files (35619594724871138875db462eda6cf24f2a462e1f812ff27d79131576cd73ab). According to the community, the file belongs to a BlackHunt Ransomware campaign threat that compromised multiple companies in Paraguay.
Its BEHAVIOR report tab, provides details on the techniques detected during sample's detonation:
  • T1490 (Inhibit System Recovery), the sample deletes the shadow copies (as highlighted in the Capabilities section below) and it also modifies Windows boot settings via bcdedit.
  • T1083 and T1135: The sample runs discovery processes to get system local files and directories, and also network shares.
  • The encryption process is visible by the CryptEncrypt operating system API call, functionality provided by the Advapi32.dll, and visible under the file's DETAILS tab.

Hunting for Keyloggers

Keyloggers are a particular form of Spyware designed for stealing user data, that commonly share some MITRE Tactics, including collecting data and/or discovering data for later exfiltration and/or Command and Control communication.
For our VTI query we will specify the T1056.001: Input Capture: Keylogging sub-technique of the Collection tactic, which identifies keystrokes interception. Additionally, we use the first submitted time condition (fs) and both Command and Control or Exfiltration tactics (attack_tactic), since we are not really interested in restricting the way the data gets outside of the victim environment.

One of the retrieved files (975b67e75c046e95b1f418c2db85a726dc5d38c21853c5446393b9805c6d1bd5) with a 25 out of 71 AV detection ratio is cataloged as Remcos, a commercial Remote Access Tool with keylogger capabilities among many others, which has been used by several Threat Actors.
On its BEHAVIOR tab, we can see details on the keystrokes interception performed via polling method. The report also reveals additional functionality, including capturing screenshots, reading victims' clipboard and geographical location of the abused device.

Conclusions

In this post we have seen using a couple of examples how present the MITRE framework is on VirusTotal and how it can be used to search for files with a particular TTP-based behavior using VirusTotal Intelligence searches. MITRE-related data is based on behavior detected during samples' sandbox detonation.
We have additionally created an Appendix I (below) detailing some of the most interesting behavior-search modifiers you can use in your queries. This fits particularly well with other TTP-based modifiers, allowing you to refine results by adding particular technical characteristics specific for the malware under analysis.
We hope you found this post interesting and useful. For suggestions or feedback please feel free to reach out here, we will be happy to hear from you.
Happy hunting!

Appendix I - Behavior search modifiers

The following search modifiers provide a more granular way of searching files based on their behavior, allowing more restrictive queries while using Tactics/Techniques ("attack_tactic", "attack_technique") search modifiers.

Tuesday, February 06, 2024

VT Livehunt Cheat Sheet

Today we are happy to announce the release of our “Livehunt Cheat Sheet”, a guide to help you quickly implement monitoring rules in Livehunt. You can find the PDF version here.
VirusTotal Livehunt is a service that continuously scans all incoming indicators and notifies you when any of them matches your rules. Livehunt not only monitors files, but also domains, URLs, and IP addresses. In this post we detail a few practical examples along with useful tips.

VT Module

This YARA module was created for VT Hunting services to provide all available context data, which is structured in two main sections: metadata and behaviour (sandbox execution). You can find more information about the VT module here.

Using metadata information in Livehunt rules

Analysts can create rules to hunt based on the metadata information that VirusTotal gathers and processes. We are referring to hunting files by characteristics (type, size, signatures), reputation (antivirus detections, submission patterns), and even contextual details (file names, tags, etc).
For example, this would allow analysts to detect files of a certain type that were submitted several times from a given country, and that more than 5 antiviruses have flagged as malicious. Here you have some detailed examples:

Example 1: Malicious DOCX files that use macros:
This example defines a rule focused on detecting potentially malicious DOCX files with macros.
First we check the file type with vt.metadata.file_type == vt.FileType.DOCX.
The next condition (vt.metadata.analysis_stats.malicious > 5) matches files flagged as malicious by more than 5 antivirus engines in VirusTotal. This filters out most of the benign files, and can be adjusted according to the investigation.
Finally, it loops all tags given by security tools in the analysis pipeline and searches for the tag “macros”: for any tag in vt.metadata.tags:(tag == “macros”)

import "vt"

rule malicious_docx_macros {
  meta:
    description = "Detect malicious documents using macros."
  condition:
    vt.metadata.file_type == vt.FileType.DOCX and
    vt.metadata.analysis_stats.malicious > 5 and
    for any tag in vt.metadata.tags:(tag == “macros”)
}

Example 2: Possible LNK execution through CommandLineArguments Exif metadata field:
The following rule is designed to identify PowerShell execution by manipulating metadata fields of .lnk files. This technique is frequently utilized by malware to avoid detection and initiate attacks. For example, this malicious .lnk file report shows the target command line which will execute PowerShell code to download the “powercat.ps1” script.


In this case, the condition checks for the “powershell” string within two EXIF metadata fields usually used to store the powershell command line - “CommandLineArguments” and “RelativePath”:
vt.metadata.exiftool["CommandLineArguments"] icontains "powershell"
vt.metadata.exiftool["RelativePath"] icontains "powershell"

import "vt"

rule LNK_metadata_execution_powershell {
  meta:
    description = "Detect possible LNK execution through CommandLineArguments Exif metadata field"
  condition:
    vt.metadata.exiftool["CommandLineArguments"] icontains "powershell" or
    vt.metadata.exiftool["RelativePath"] icontains "powershell"
}

Using behaviour information in Livehunt rules

Dynamic analysis can bring great value on top of static one. In VirusTotal, we run executable files through multiple sandboxes and its output is normalized into a common format, which can be leveraged through the “vt” module.

Example 3: Malicious files that use persistence using VBScript:
The following rule identifies persistence under the "RunOnce" registry key using VBS files. This key allows programs to automatically execute once when a user logs in, often exploited by malware to maintain presence on a system.
For this rule, we iterate over vt.behaviour.registry_keys_set looking for "\\CurrentVersion\\RunOnce\\" with a value that ends with ".vbs".

import "vt"

rule persistence_runonce_vbs {
  meta:
    description = "Detect persistence by establishing a VBS file in the runonce key"
  condition:
    for any registry_key in 
     vt.behaviour.registry_keys_set: (registry_key.key icontains
     "\\CurrentVersion\\RunOnce\\") and (registry_key.value endswith ".vbs")
}

Example 4: Suspicious shell scripts in "profile.d" folder:
This rule detects activity involving the creation or modification of shell scripts (.sh files) within the "/etc/profile.d/" directory on Linux systems. This directory is often used to host scripts that automatically execute during user login, making it a common target for malware seeking persistence or automatic execution.
First condition iterates through files dropped (vt.behaviour.files_dropped) during execution as observed in VirusTotal's behavioral analysis and checks if the dropped file's path contains "/etc/profile.d/" and ends with ".sh" in order to match shell scripts.
The second condition is very similar but checks the file path for files written (vt.behaviour.files_written) during detonation.

import "vt"

rule profile_folder_shell_script {
  meta:
    description = "Detects shell script creation in "profile.d" path."
  condition:
    for any dropped in vt.behaviour.files_dropped :(
     dropped.path contains"/etc/profile.d/"
     and dropped.path endswith ".sh"
    )
    or
    for any file_path in vt.behaviour.files_written :(
     file_path contains"/etc/profile.d/"
     and file_path endswith ".sh"
    )
}

Wrapping up

The VirusTotal (vt) YARA module brings you unprecedented flexibility in crafting Livehunt rules combining traditional file content analysis with rich metadata information and behavioral patterns from dynamic analysis.
Our “VT Intelligence Cheat Sheet” provides a quick guide to implement some of the most used techniques. If you have any suggestions or want to share feedback please feel free to reach out here.

Happy Hunting!

Monday, January 22, 2024

Uncovering Hidden Threats with VirusTotal Code Insight

In the constantly changing world of cybersecurity, generative AI is becoming an increasingly valuable tool. This blog post shows various examples that elude traditional detection engines yet are adeptly unveiled by Code Insight. We explore diverse scenarios, ranging from firmware patches in DJI drones that disable red flight lights, to the covert theft of WhatsApp session cookies, phishing targeting Tesla customers, automated login attempts on the Medtronic CareLink Network, Bitcoin wallet attacks, Tik-Tok viewbots, unauthorized Netflix account access, cheaters for Roblox, and automation of Tinder’s match-making, along with a range of other scenarios.

Code Insight, based on Google Cloud Duet AI, was unveiled at RSA Conference 2023 as a novel feature of VirusTotal. It's specialized in analyzing code snippets and generating reports in natural language from a cybersecurity and malware expert's perspective. Since its introduction, millions of files have been analyzed by Code Insight. The reports generated are readily accessible for consultation and can be leveraged through the VirusTotal Enterprise service for large-scale result aggregation and exploitation. This functionality allows security teams to quickly and efficiently examine vast amounts of code, pinpoint potential threats, and enhance their overall security posture.

Let's delve into some intriguing anecdotal examples that demonstrate how we can uncover threats by utilizing the reports generated by Code Insight. These instances not only showcase the tool's analytical strength but also illustrate the practical applications of its findings in real-world cybersecurity scenarios.

Imagine working on the cybersecurity team at Roblox and wanting to explore what Code Insight has discovered. A simple query in VT Enterprise, such as codeinsight:Roblox, would yield more than 2,000 related files.


Continuing from the previous exploration with Code Insight, let's focus even more closely. Say you're an Anti-Cheat Software Engineer at Roblox interested in the "Murder Mystery 2" game. By refining your search in VT Enterprise to codeinsight:Roblox AND codeinsight:"Murder Mystery 2" AND codeinsight:cheat, the results are much more specific. This refined query leads to a fascinating find - a single file.


Initially received by VirusTotal as a text file, Code Insight correctly classifies it as a Lua script and provides a detailed report on its functionality. This example demonstrates Code Insight's precision in identifying and analyzing content within a specific context, proving invaluable for targeted cybersecurity investigations.

6cc2daf625f329f10c0771eea5924d868edf5445de6565acdd2e02d9c89f70b6

Shifting our focus, let's say we are now investigating a technique used to modify the firmware of DJI drones that turns off LED lights during flight. To discover if Code Insight has identified such modifications, we could use a targeted VT Enterprise search: codeinsight:DJI AND codeinsight:firmware AND codeinsight:lights. Voilà, the search results reveal this:

eb252a56cdfe3c66ba45b4a87863d0dafb18dce49ea42bcbb766e769dbba9e6e

As the previous examples demonstrate, locating interesting samples through the “codeinsight:” operator is remarkably easy. This is largely due to the fact that the searches are conducted within the natural language reports generated by AI, which analyze the code and functionality of files. This approach significantly simplifies the task of finding relevant cybersecurity threats.

Next, we'll present more intriguing cases that have been detected using Code Insight, further showcasing its effectiveness in the cybersecurity landscape: .

Stealing cryptocurrency by replacing addresses from the clipboard

0cf3a43dca5fdb2df9fd6743c8ecac228c1b27823ad0134fc92f384c6b497245

Script that automates the process of logging into the Medtronic CareLink Network

56742933e6384a2911a4f27ab14c927941ece836f34e5fa7699caf17f4a1dd72

Script that steals WhatsApp session cookies

4d128460d2426ba0a47dfe70b3d54ee6e9d331c7889bef61974b3fd5af0e38c3

More examples:

These are just a few examples of how Code Insight can augment our threat intelligence processes and assist in identifying new targeted threats. We encourage you to try it in your investigations, experiencing its capabilities in enhancing your cybersecurity efforts. Stay tuned, as we will soon announce new features for Code Insight. Until then, happy hunting!

Monday, January 01, 2024

Monitoring malware trends with VT Intelligence

Please note that this blogpost is part of our #VTMondays series, check out our collection of past publications here.
VT Intelligence can be a powerful tool for monitoring malware trends, enhancing your detection capabilities and enabling proactive defense against evolving threats. To leverage it effectively, analysts can refine searches with threat indicators relevant to their business, technologies and to the malware trends occurring at the moment. Analysts can use this intelligence to identify and hunt emerging malicious samples and investigate new trends and capabilities.

To begin with a simple query we will search for new files (“entity:files”) first seen during the last week (“fs:7d+”) and detected by AV vendors as keylogger (“engines:keylogger”) with more than 5 positives (“p:5+”).

In our second query we search for fresh (“fs:7d+”) Windows, Linux or MacOS files (“type:peexe or type:elf or type:macho”). To focus on popular/emerging malware, we will use the submissions modifier with a relatively high number (“submissions:10+”), these thresholds serve as illustrative examples and can be adjusted according to the investigation.

Finally, we will look for Zip files (“type:zip”) that potentially contain ransomware. For discriminating using verdict of AV engines we use the “engines” keyword (“engines:ransom or engines:ransomware”) and use both “ransom” and “ransomware” strings as some engines use different criteria for verdicts. An alternative way of detecting ransomware is through dedicated YARA rules (“crowdsourced_yara_rule:ransomware”).

You can learn more about file search modifiers in the documentation.
As always, we would like to hear from you.
Happy hunting!