external/trailofbits-security/differential-review/skills/differential-review/SKILL.md
Performs security-focused differential review of code changes (PRs, commits, diffs). Adapts analysis depth to codebase size, uses git history for context, calculates blast radius, checks test coverage, and generates comprehensive markdown reports. Automatically detects and prevents security regressions.
npx skillsauth add seikaikyo/dash-skills differential-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Security-focused code review for PRs, commits, and diffs.
| Rationalization | Why It's Wrong | Required Action | |-----------------|----------------|-----------------| | "Small PR, quick review" | Heartbleed was 2 lines | Classify by RISK, not size | | "I know this codebase" | Familiarity breeds blind spots | Build explicit baseline context | | "Git history takes too long" | History reveals regressions | Never skip Phase 1 | | "Blast radius is obvious" | You'll miss transitive callers | Calculate quantitatively | | "No tests = not my problem" | Missing tests = elevated risk rating | Flag in report, elevate severity | | "Just a refactor, no security impact" | Refactors break invariants | Analyze as HIGH until proven LOW | | "I'll explain verbally" | No artifact = findings lost | Always write report |
| Codebase Size | Strategy | Approach | |---------------|----------|----------| | SMALL (<20 files) | DEEP | Read all deps, full git blame | | MEDIUM (20-200) | FOCUSED | 1-hop deps, priority files | | LARGE (200+) | SURGICAL | Critical paths only |
| Risk Level | Triggers | |------------|----------| | HIGH | Auth, crypto, external calls, value transfer, validation removal | | MEDIUM | Business logic, state changes, new public APIs | | LOW | Comments, tests, UI, logging |
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
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: 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
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.
Before delivering:
audit-context-building skill:
issue-writer skill:
issue-writer --input DIFFERENTIAL_REVIEW_REPORT.md --format audit-reportInput: 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
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
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
For these cases, use standard code review instead.
Immediate escalation triggers:
These patterns require adversarial analysis even in quick triage.
Do:
Don't:
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.
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.