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FSGv110EngdulekxtBorlandDelphiBorlandC

yara LOW Yara-Rules
community
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: 2026-09-02T11:00:00Z · Confidence: medium

Hunt Hypothesis

This detection identifies potential legacy or custom software artifacts compiled with Borland Delphi and C++ by analyzing file signatures via YARA rules to distinguish them from known benign applications. A proactive hunt is essential in Azure Sentinel to uncover hidden persistence mechanisms or obfuscated binaries that may evade standard signature-based defenses due to their use of older development frameworks.

YARA Rule

rule FSGv110EngdulekxtBorlandDelphiBorlandC
{
      meta:
		author="malware-lu"
strings:
		$a0 = { 2B C2 E8 02 00 00 00 95 4A 59 8D 3D 52 F1 2A E8 C1 C8 1C BE 2E [2] 18 EB 02 AB A0 03 F7 }
	$a1 = { 2B C2 E8 02 00 00 00 95 4A 59 8D 3D 52 F1 2A E8 C1 C8 1C BE 2E [2] 18 EB 02 AB A0 03 F7 EB 02 CD 20 68 F4 00 00 00 0B C7 5B 03 CB 8A 06 8A 16 E8 02 00 00 00 8D 46 59 EB 01 A4 02 D3 EB 02 CD 20 02 D3 E8 02 00 00 00 57 AB 58 81 C2 AA 87 AC B9 0F BE C9 80 }
	$a2 = { EB 01 2E EB 02 A5 55 BB 80 [2] 00 87 FE 8D 05 AA CE E0 63 EB 01 75 BA 5E CE E0 63 EB 02 }

condition:
		$a0 at pe.entry_point or $a1 at pe.entry_point or $a2 at pe.entry_point
}

Deployment Notes

This YARA rule can be deployed in the following contexts:

This rule contains 3 string patterns in its detection logic.

False Positive Guidance

Here are 4 specific false positive scenarios for the FSGv110EngdulekxtBorlandDelphiBorlandC detection rule, along with targeted mitigation strategies:

Original source: https://github.com/Yara-Rules/rules/blob/main/packers/packer.yar