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detect RedLeaves in memory

yara LOW Yara-Rules
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This rule was pulled from an open-source repository and enriched with AI. Validate in a test environment before deploying to production.
View original rule at Yara-Rules →
Retrieved: 2026-07-02T23:00:00Z · Confidence: medium

Hunt Hypothesis

This detection rule identifies the presence of the RedLeaves threat actor’s artifacts within system memory, signaling potential post-exploitation activity or lateral movement attempts. Proactively hunting for these indicators in Azure Sentinel allows the SOC team to validate low-severity alerts against known adversary behaviors before they escalate into critical incidents.

YARA Rule

rule RedLeaves {
          meta:
            description = "detect RedLeaves in memory"
            author = "JPCERT/CC Incident Response Group"
            rule_usage = "memory block scan"
            reference = "https://blogs.jpcert.or.jp/en/2017/05/volatility-plugin-for-detecting-redleaves-malware.html"
            hash1 = "5262cb9791df50fafcb2fbd5f93226050b51efe400c2924eecba97b7ce437481"

          strings:
            $v1 = "red_autumnal_leaves_dllmain.dll"
            $b1 = { FF FF 90 00 }

          condition: $v1 and $b1 at 0
}

Deployment Notes

This YARA rule can be deployed in the following contexts:

This rule contains 2 string patterns in its detection logic.

References

False Positive Guidance

Here are 5 specific false positive scenarios for the detect RedLeaves in memory rule, tailored for an enterprise environment:

Original source: https://github.com/Yara-Rules/rules/blob/main/malware/MalConfScan.yar