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diffing-malware-samples-to-find-changes

Compares two related malware samples to surface what changed between variants using section-level hashing, import-set differences, and fuzzy similarity scoring. Activates for requests to diff two samples, compare malware variants, or measure similarity between two binaries for variant tracking.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(4)

  • SKILL.md2.7 KB
  • LICENSE340 B
  • references/api-reference.md1.3 KB
  • scripts/analyst.py3.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Diffing Malware Samples to Find Changes

When to Use

  • You have two related samples (suspected variants of one family) and want a fast, structural diff before a full BinDiff.
  • You need to quantify similarity and pinpoint changed sections/imports for variant tracking.

Do not use this as authoritative function-level diffing — that requires BinDiff/Diaphora on disassembly. This skill does structural/statistical diffing and executes nothing.

Prerequisites

  • Two sample files (read inertly). Optional: ssdeep/tlsh for fuzzy scores (degrade gracefully).

Workflow

Step 1: Structural diff

python scripts/analyst.py diff a.bin b.bin

Compares file size, per-section SHA-256 and entropy, and the import name sets, reporting added/ removed/changed sections and imports.

Step 2: Similarity score

Reports a byte-level similarity ratio and, if available, ssdeep/tlsh fuzzy-hash comparison scores.

Step 3: Prioritize changed regions

Changed sections (same name, different hash) and new imports are the high-value targets for deeper disassembly diffing.

Step 4: Document

Record what changed and the similarity score to support variant/lineage tracking.

Validation

  • Section comparison keys on section name; identical sections report equal hashes.
  • Import diff lists are accurate (added vs removed vs common).
  • Fuzzy-hash scores are reported only when the library is available, else clearly omitted.

Pitfalls

  • Recompilation shifting addresses makes raw byte diff noisy — rely on section/import structure.
  • Packers making both samples look similar (packed) while the payloads differ — unpack first.
  • Treating a high byte-similarity as proof of same author without corroboration.

References

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