Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
150 lines
4.7 KiB
Markdown
150 lines
4.7 KiB
Markdown
---
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id: wiki-2026-0508-adversarial-code-stylometry
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title: Adversarial Code Stylometry
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [P-Reinforce-AUTO-36585B, Code Authorship Obfuscation]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [security, ml, privacy, deanonymization]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: scikit-learn
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---
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# Adversarial Code Stylometry
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## 매 한 줄
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> **"매 source code 의 author 를 statistical fingerprint 로 식별 — 그리고 attacker 는 이를 회피한다."**. Code stylometry 는 AST features + n-grams + lexical patterns 로 author 를 95% accuracy 로 deanonymize 가능; adversarial stylometry 는 transformation/obfuscation 으로 이를 무력화한다.
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## 매 핵심
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### 매 Feature Family
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- **Lexical**: identifier length, naming convention, comment density.
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- **Syntactic (AST)**: subtree frequency, depth distribution, control-flow patterns.
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- **Layout**: indentation, brace style, line length.
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- **Semantic**: API choice, idiom preference (list comp vs loop).
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### 매 Attack Surface
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- **Open-source contributors** — GitHub commits 의 deanonymization.
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- **Malware authorship** — APT attribution.
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- **Plagiarism detection** — academic/hiring context.
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- **Bug bounty / leak** — anonymous reporter identification.
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### 매 Defense
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1. Code transformation (Caliskan 2018 — paraphrase preserving semantics).
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2. LLM-mediated rewrite (rewrite via Claude/GPT to neutralize style).
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3. Style transfer to another author (mimicry).
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4. Mechanical normalization (autoformatter + identifier randomization).
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## 💻 패턴
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### AST Feature Extractor
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```python
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import ast
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from collections import Counter
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def ast_node_freq(source: str) -> Counter:
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tree = ast.parse(source)
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return Counter(type(n).__name__ for n in ast.walk(tree))
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```
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### Author Classifier (sklearn)
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```python
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.pipeline import Pipeline
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pipe = Pipeline([
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("tfidf", TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5))),
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("rf", RandomForestClassifier(n_estimators=300, n_jobs=-1)),
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])
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pipe.fit(train_sources, train_authors)
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```
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### Style Obfuscation via Rewrite
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```python
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import anthropic
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client = anthropic.Anthropic()
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def neutralize_style(code: str) -> str:
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=4096,
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messages=[{"role": "user", "content": f"""Rewrite this code to neutralize authorial style.
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Preserve semantics exactly. Use generic identifiers, standard idioms, mechanical formatting.
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```python
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{code}
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```"""}],
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)
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return msg.content[0].text
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```
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### Mimicry Attack (target style)
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```python
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def mimic(code: str, target_samples: list[str]) -> str:
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"""Rewrite `code` to look like `target_samples` author."""
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target_blob = "\n---\n".join(target_samples[:3])
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prompt = f"Target author samples:\n{target_blob}\n\nRewrite preserving semantics:\n{code}"
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return llm_call(prompt)
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```
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### Detection of Obfuscated Code
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```python
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def obfuscation_signal(code: str) -> float:
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"""High score → likely autoformatted/normalized."""
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feats = ast_node_freq(code)
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entropy = -sum((c/sum(feats.values())) * np.log2(c/sum(feats.values())) for c in feats.values())
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return 1.0 - entropy / np.log2(len(feats)) # uniform → 0, peaked → 1
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```
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### Defensive Pre-commit Hook
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```bash
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#!/usr/bin/env bash
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# .git/hooks/pre-commit
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ruff format --quiet .
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python -m style_neutralizer **/*.py
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Anonymous OSS contribution | LLM rewrite + autoformat |
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| Whistleblower | Full mimicry to public author |
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| Defensive (detection) | Char n-gram + AST RF |
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| Research baseline | Caliskan 2015 features |
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**기본값**: autoformat + LLM neutralization for adversarial; char n-gram TF-IDF + RF for detection.
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## 🔗 Graph
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- 변형: [[Code Obfuscation]]
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- Adjacent: [[Differential Privacy]]
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## 🤖 LLM 활용
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**언제**: style neutralization, mimicry attack, defensive paraphrase.
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**언제 X**: ground-truth authorship verification 에 LLM judgment 단독 사용.
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## ❌ 안티패턴
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- **Autoformatter 만 의존**: AST/lexical features 는 그대로 leak.
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- **Identifier rename only**: control-flow signature 가 식별 가능.
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- **Single-pass LLM rewrite**: subtle idioms 잔존 — multi-pass 필요.
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- **Train/test 동일 repo**: leakage — author-disjoint split 필수.
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## 🧪 검증 / 중복
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- Verified (Caliskan 2015 USENIX Sec, Abuhamad 2018 CCS).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — AST features, attack/defense patterns |
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