Detection Engineering

How to Write Yara Rules For DotNet Malware

How to develop Yara rules for .NET Malware. Utilising IL instructions and associated bytecodes.

How to Write Yara Rules For DotNet Malware

In this blog, we will show a relatively quick and easy process that can be applied to manually generate yara rules for dotnet based malware.

This process will cover the process of locating a configuration loading function (generally the same in structure across samples) and generalising the associated bytecodes to produce a Yara rule.

This process can be generalized across many malware families that are based on the .NET/C# language.

Note that this style of yara rules are not necessarily efficient, but they are effective if performance constraints are not a major concern. You can (should) adjust the rule with additional filtering if performance is a concern.

Performing a RetroHunt - this rule matched on

  • 119 Confirmed Redline matches
  • 487 Unconfirmed matches, an initial review suggests they are redline.

All matched hashes are available at the end of this page. You can pick any of the matching hashes if you wish to follow along with the blog.

TLDR/Summary

To quickly produce a yara rule for .NET based malware.
- Locate the Configuration function
- Switch from C# to IL view to view opcodes/bytecodes
- Copy out raw code into text editor.
- Keep OpCodes, Wildcard on Operands
- Copy bytes into a yara rule
- Test Yara rule against malware repo.

Analysis

We will be using the file 0cc3a0f8b48ef8d8562b9cdf9c7cfe7f63faf43a5ac6dc6973dc8bf13b6c88cf.

Initial analysis of the file suggests that it is .NET-based and obfuscated using the Confuser protector. This suggests that DnSpy will be the most appropriate tool (.NET) and that the associated code may be obfuscated (Confuser)

Initial analysis of the Entropy Graph shows no areas of high entropy. This suggests that the file is likely a final payload and not a loader/packer with an embedded file.

The lack of entropy suggests that the file may be a "final payload" and a good target for a Yara rule designed to find additional samples.

Areas of high entropy suggest the file is a loader/packer, which are generally not good targets for Yara rules designed to find additional payloads.

Initial DnSpy Analysis

Since the file is .NET based, we can use Dnspy to analyse the file and obtain decompiled code.

This is the first part of the file (ignoring constructors/.cctor).

By clicking on the MainFrm() class, we're presented with the following code. Immediately visible are two values Arguments.IP and Arguments.Key.

This can indicate the location of the configuration values within the file.

Clicking on either value presents the following configuration-related code. Noting the presence of IP, ID , Key etc.

The associated methods to initialize those values are obfuscated, but this is not necessarily an issue if the intention is to create Yara rules for similarly obfuscated malware of an identical family.

Although configuration values will vary between samples, the configuration-related method (seen above) will often remain static and largely unchanged. This presents a detection opportunity that we can use for Yara rules.

To develop a rule, we must switch to the intermediate language (IL) view. This will show us the raw IL instructions and their associated bytecodes in the raw binary file.

To develop a Yara rule, we can copy out the entire section highlighted below.

This should result in the following view within a text editor. Any text editor will do, but Notepad++ is preferred as it allows you to easily delete columns.

Within this view, we want to focus primarily on this column. Which contains OpCodes (First Byte (2 chars) and Operands/values (Generally anything after the first 2 chars)

The screenshot below may explain this better. But the TLDR is that we only want the bytecodes column

By holding Alt and selecting an area, we can easily delete columns using NotePad++.
. Anything

You can also remove columns manually or with regex if you like. Anything is fine. But the notepad++ method is the easiest.

Whatever your preferred method, you should end up with something like this.

We now want to separate the opcodes from operands. You can do this manually or with regex, whatever you prefer.

Note that we will be leaving 2B00 and not wildcarding on the 00. The 00 is techncially not part of the OpCode. 2B00 is essentially a Jump 0.

Now, Replace all operands with wildcards. This will enable Yara to locate all files with an identical opcode sequence. The operands and values can differ, allowing similar files to be discovered.

Note that this produces a lot of wildcards and is not going to produce an "efficient" rule.
For efficiency purposes you would want to do further research into the operands and exactly which values change between samples. Using this you may be able to narrow down the values into a predictable range.

Alternatively, you could use a shorter sequence, (eg cut this function in half), but then you open yourself up to more false positives.

This is a balance that you must maintain and will be different for each situation. We won't go into it here.

Moving on, we can now convert the bytes into a Yara-friendly sequence, which is effectively the same thing without newlines or lots of spacing.

This will produce the following sequence.

Which in turn produces the following (very simple) Yara rule.

Validating a Yara Rule Locally

Running this rule against a folder with 10 redline samples, 9 out of 10 samples can be identified.

How To Validate a Yara Rule With a RetroHunt

At this point, you can do additional validation using any tooling with a retrohunt feature. We personally use unpacme as it's affordable (free or <$20USD per month) and provides almost all the same functionality as more expensive options.

This results in 604 hits for the Yara rule. 117 of these results are matched to known Redline malware.

Investigating "False" Hits

Only 117 of the 604 results are matched against a known Redline sample. This is a pretty bad ratio and may suggest that the rule needs tuning.

However, investigating the "known" hits against "unknown" hits. There are a lot of similarities.

Some of the unknown hits have high confidence for Redline on virustotal. eg f4a74294fb587bdf5cf32d2d58aec90458e13c3cdfa2bbdd2ac14722e1c5e4c2

Others eg 8f89153fc26e75380795a57e39456803d45d90ed13a6595b85a3ba3eef452261 are extremely similar to Redline when downloaded and viewed in Dnspy.

This suggests that at least some (and maybe many) of the "unknown" hits are also Redline. Which provides increased confidence in the rule. (albeit it may perform terribly from a performance perspective due to wildcards).

Additional Notes

As mentioned multiple times, this is not a performance-optimized rule. It may perform badly depending on your situation, tooling and resources.

When testing locally, the rule matched on 9/10 samples. The remaining sample is also Redline but contains a slightly different config where values are set to "". This changes the opcodes as no call is associated with each ldstr,stsfld.

TLDR this breaks the Yara pattern and an additional signature will need to be added.

Conclusion

This blog was intended to demonstrate the general concept of using DnSpy and IL bytecodes to create Yara rules for dotnet malware. By locating the configuration and associated OpCodes, we were able to locate (at least) 117 other samples based on only a single one.

There are many opportunities to improve this rule (optimization, adding filtering for dotnet files etc). But hopefully, this has demonstrated the concept, and you can use it to generate Yara rules for yourself :)

Final Yara Rule

Note that mscoree.dll has been added to hone in on .NET files, which typically include this string. 


rule win_redline_bytecodes_oct_2023
{
	meta:
		author = "Matthew @ Embee_Research"
		created = "2023/08/27"
		description = "Configuration related bytecodes in redline .net files"
		sha_256 = "0cc3a0f8b48ef8d8562b9cdf9c7cfe7f63faf43a5ac6dc6973dc8bf13b6c88cf"
		sha_256 = "8d07a2a48e4c8c3adc8ce2e5b6c5427c6b9a4c847431e208fb52aab51cde499c"
		
	strings:

		$s1 = { 20 ?? ?? ?? ?? 2B 00 28 ?? ?? ?? ?? 80 ?? ?? ?? ?? 20 ?? ?? ?? ?? 2B 00 28 ?? ?? ?? ?? 80 ?? ?? ?? ?? 72 ?? ?? ?? ?? 80 ?? ?? ?? ?? 20 ?? ?? ?? ?? 2B 00 28 ?? ?? ?? ?? 80 ?? ?? ?? ?? 2A }
		$s2 = "mscoree.dll" ascii
		
	condition:
		
		$s1 and $s2
		

}

Matching Hashes

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