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Antigravity Agent 9a135bd19d docs(10_Wiki): Topic_Business/General/Graphic/Programming을 Topics/ 하위로 이동

최상위 10_Wiki/Topic_*였던 4개 카테고리 폴더를 10_Wiki/Topics/Topic_* 로 재배치.
콘텐츠 변경 없음(순수 폴더 이동) — Topics/ 하위 나머지 폴더는 이미 지난 커밋에서
전부 정리된 상태(잔존 항목은 에이전트 운영 상태 및 사용자가 보존을 요청한
업데이트0615/무제 3.canvas 뿐).

2026-07-05 00:44:01 +09:00

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title

Adversarial Code Stylometry

매 한 줄

"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.

매 핵심

매 Feature Family

Lexical: identifier length, naming convention, comment density.
Syntactic (AST): subtree frequency, depth distribution, control-flow patterns.
Layout: indentation, brace style, line length.
Semantic: API choice, idiom preference (list comp vs loop).

매 Attack Surface

Open-source contributors — GitHub commits 의 deanonymization.
Malware authorship — APT attribution.
Plagiarism detection — academic/hiring context.
Bug bounty / leak — anonymous reporter identification.

매 Defense

Code transformation (Caliskan 2018 — paraphrase preserving semantics).
LLM-mediated rewrite (rewrite via Claude/GPT to neutralize style).
Style transfer to another author (mimicry).
Mechanical normalization (autoformatter + identifier randomization).

💻 패턴

AST Feature Extractor

import ast
from collections import Counter

def ast_node_freq(source: str) -> Counter:
    tree = ast.parse(source)
    return Counter(type(n).__name__ for n in ast.walk(tree))

Author Classifier (sklearn)

from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline

pipe = Pipeline([
    ("tfidf", TfidfVectorizer(analyzer="char_wb", ngram_range=(2, 5))),
    ("rf", RandomForestClassifier(n_estimators=300, n_jobs=-1)),
])
pipe.fit(train_sources, train_authors)

Style Obfuscation via Rewrite

import anthropic

client = anthropic.Anthropic()

def neutralize_style(code: str) -> str:
    msg = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=4096,
        messages=[{"role": "user", "content": f"""Rewrite this code to neutralize authorial style.
Preserve semantics exactly. Use generic identifiers, standard idioms, mechanical formatting.

```python
{code}
```"""}],
    )
    return msg.content[0].text

Mimicry Attack (target style)

def mimic(code: str, target_samples: list[str]) -> str:
    """Rewrite `code` to look like `target_samples` author."""
    target_blob = "\n---\n".join(target_samples[:3])
    prompt = f"Target author samples:\n{target_blob}\n\nRewrite preserving semantics:\n{code}"
    return llm_call(prompt)

Detection of Obfuscated Code

def obfuscation_signal(code: str) -> float:
    """High score → likely autoformatted/normalized."""
    feats = ast_node_freq(code)
    entropy = -sum((c/sum(feats.values())) * np.log2(c/sum(feats.values())) for c in feats.values())
    return 1.0 - entropy / np.log2(len(feats))  # uniform → 0, peaked → 1

Defensive Pre-commit Hook

#!/usr/bin/env bash
# .git/hooks/pre-commit
ruff format --quiet .
python -m style_neutralizer **/*.py

매 결정 기준

상황	Approach
Anonymous OSS contribution	LLM rewrite + autoformat
Whistleblower	Full mimicry to public author
Defensive (detection)	Char n-gram + AST RF
Research baseline	Caliskan 2015 features

기본값: autoformat + LLM neutralization for adversarial; char n-gram TF-IDF + RF for detection.

🔗 Graph

변형: Code Obfuscation
Adjacent: Differential Privacy

🤖 LLM 활용

언제: style neutralization, mimicry attack, defensive paraphrase. 언제 X: ground-truth authorship verification 에 LLM judgment 단독 사용.

❌ 안티패턴

Autoformatter 만 의존: AST/lexical features 는 그대로 leak.
Identifier rename only: control-flow signature 가 식별 가능.
Single-pass LLM rewrite: subtle idioms 잔존 — multi-pass 필요.
Train/test 동일 repo: leakage — author-disjoint split 필수.

🧪 검증 / 중복

Verified (Caliskan 2015 USENIX Sec, Abuhamad 2018 CCS).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — AST features, attack/defense patterns

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