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differential-review

Reviews code changes for security risks using Git history, caller counts, and test coverage. Produces a Markdown report with evidence, potential attack scenarios, affected code, and clear limits on what was reviewed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.

설치 방법 보기

이럴 때 유용합니다

  • Reviewing a pull request for security risks
  • Checking whether a fixed bug has returned
  • Assessing which callers a change could affect
  • Finding changed code without test coverage

포함된 파일(7)

  • SKILL.md7.3 KB
  • adversarial.md4.9 KB
  • agents/openai.yaml245 B
  • assets/trail-of-bits-mark.svg3.0 KB
  • methodology.md6.6 KB
  • patterns.md6.8 KB
  • reporting.md6.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Differential Security Review

Security-focused code review for PRs, commits, and diffs.

Core Principles

  1. Risk-First: Focus on auth, crypto, value transfer, external calls
  2. Evidence-Based: Every finding backed by git history, line numbers, attack scenarios
  3. Adaptive: Scale to codebase size (SMALL/MEDIUM/LARGE)
  4. Honest: Explicitly state coverage limits and confidence level
  5. Output-Driven: Always generate comprehensive markdown report file

Rationalizations (Do Not Skip)

RationalizationWhy It's WrongRequired Action
"Small PR, quick review"Heartbleed was 2 linesClassify by RISK, not size
"I know this codebase"Familiarity breeds blind spotsBuild explicit baseline context
"Git history takes too long"History reveals regressionsNever skip Phase 1
"Blast radius is obvious"You'll miss transitive callersCalculate quantitatively
"No tests = not my problem"Missing tests = elevated risk ratingFlag in report, elevate severity
"Just a refactor, no security impact"Refactors break invariantsAnalyze as HIGH until proven LOW
"I'll explain verbally"No artifact = findings lostAlways write report

Quick Reference

Codebase Size Strategy

Codebase SizeStrategyApproach
SMALL (<20 files)DEEPRead all deps, full git blame
MEDIUM (20-200)FOCUSED1-hop deps, priority files
LARGE (200+)SURGICALCritical paths only

Risk Level Triggers

Risk LevelTriggers
HIGHAuth, crypto, external calls, value transfer, validation removal
MEDIUMBusiness logic, state changes, new public APIs
LOWComments, tests, UI, logging

Workflow Overview

Pre-Analysis → Phase 0: Triage → Phase 1: Code Analysis → Phase 2: Test Coverage
    ↓              ↓                    ↓                        ↓
Phase 3: Blast Radius → Phase 4: Deep Context → Phase 5: Adversarial → Phase 6: Report

Decision Tree

Starting a review?

├─ Need detailed phase-by-phase methodology?
│  └─ Read: methodology.md
│     (Pre-Analysis + Phases 0-4: triage, code analysis, test coverage, blast radius)
│
├─ Analyzing HIGH RISK change?
│  ├─ Read: adversarial.md
│  │  (Phase 5: Attacker modeling, exploit scenarios, exploitability rating)
│  └─ Or delegate to: differential-review:adversarial-modeler agent
│     (Autonomous attacker modeling with concrete exploit scenarios)
│
├─ Writing the final report?
│  └─ Read: reporting.md
│     (Phase 6: Report structure, templates, formatting guidelines)
│
├─ Looking for specific vulnerability patterns?
│  └─ Read: patterns.md
│     (Regressions, reentrancy, access control, overflow, etc.)
│
└─ Quick triage only?
   └─ Use Quick Reference above, skip detailed docs

Agents

differential-review:adversarial-modeler — Models attacker perspectives and builds exploit scenarios for HIGH RISK code changes. Follows the 5-step adversarial methodology (attacker model, attack vectors, exploitability rating, exploit scenario, baseline cross-reference) and produces structured vulnerability reports. Delegate to this agent when Phase 5 analysis is needed on high-risk changes, passing that full namespaced name as subagent_type — a bare adversarial-modeler is unregistered and the dispatch fails at runtime.


Quality Checklist

Before delivering:

  • All changed files analyzed
  • Git blame on removed security code
  • Blast radius calculated for HIGH risk
  • Attack scenarios are concrete (not generic)
  • Findings reference specific line numbers + commits
  • Report file generated
  • User notified with summary

Integration

audit-context-building skill:

  • Pre-Analysis: Build baseline context
  • Phase 4: Deep context on HIGH RISK changes

issue-writer skill:

  • Transform findings into formal audit reports
  • Command: issue-writer --input DIFFERENTIAL_REVIEW_REPORT.md --format audit-report

Example Usage

Quick Triage (Small PR)

Input: 5 file PR, 2 HIGH RISK files
Strategy: Use Quick Reference
1. Classify risk level per file (2 HIGH, 3 LOW)
2. Focus on 2 HIGH files only
3. Git blame removed code
4. Generate minimal report
Time: ~30 minutes

Standard Review (Medium Codebase)

Input: 80 files, 12 HIGH RISK changes
Strategy: FOCUSED (see methodology.md)
1. Full workflow on HIGH RISK files
2. Surface scan on MEDIUM
3. Skip LOW risk files
4. Complete report with all sections
Time: ~3-4 hours

Deep Audit (Large, Critical Change)

Input: 450 files, auth system rewrite
Strategy: SURGICAL + audit-context-building
1. Baseline context with audit-context-building
2. Deep analysis on auth changes only
3. Blast radius analysis
4. Adversarial modeling
5. Comprehensive report
Time: ~6-8 hours

When NOT to Use This Skill

  • Greenfield code (no baseline to compare)
  • Documentation-only changes (no security impact)
  • Formatting/linting (cosmetic changes)
  • User explicitly requests quick summary only (they accept risk)

For these cases, use standard code review instead.


Red Flags (Stop and Investigate)

Immediate escalation triggers:

  • Removed code from "security", "CVE", or "fix" commits
  • Access control modifiers removed (onlyOwner, internal → external)
  • Validation removed without replacement
  • External calls added without checks
  • High blast radius (50+ callers) + HIGH risk change

These patterns require adversarial analysis even in quick triage.


Tips for Best Results

Do:

  • Start with git blame for removed code
  • Calculate blast radius early to prioritize
  • Generate concrete attack scenarios
  • Reference specific line numbers and commits
  • Be honest about coverage limitations
  • Always generate the output file

Don't:

  • Skip git history analysis
  • Make generic findings without evidence
  • Claim full analysis when time-limited
  • Forget to check test coverage
  • Miss high blast radius changes
  • Output report only to chat (file required)

Supporting Documentation


For first-time users: Start with methodology.md to understand the complete workflow.

For experienced users: Use this page's Quick Reference and Decision Tree to navigate directly to needed content.

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같은 저장소의 스킬

Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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aflpp

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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Running C/C++ fuzzing across multiple CPU cores
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  • Monitoring fuzzing progress and stability
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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Reviewing workflows that run AI coding agents
  • Tracing issue and PR content into agent prompts
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한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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trailofbits/skills7.7천2026년 10월 20일 업데이트

atheris

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Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Fuzzing a Python package or parser
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trailofbits/skills7.7천2026년 10월 20일 업데이트

Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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