Security review

Security Review

Identify HIGH-CONFIDENCE security vulnerabilities with real exploitation potential. Two-axis scoring (severity + confidence), OWASP 2025 aligned, false positive filtered.

Parse Arguments

Output Path Configuration:

• If $ARGUMENTS contains --post-to-pr: enable GitHub PR posting (Phase 6)
• Remaining non-flag arguments: use as output directory (default: ./reviews/security/)

Example usage:

• /review:security-review — local report to ./reviews/security/
• /review:security-review audits/sec — local report to audits/sec/
• /review:security-review --post-to-pr — local report + post to GitHub PR
• /review:security-review audits/sec --post-to-pr — both

Git Analysis

GIT STATUS:

!`git status`

FILES MODIFIED:

!`git diff --name-only origin/HEAD...`

COMMITS:

!`git log --no-decorate origin/HEAD...`

DIFF CONTENT:

!`git diff --merge-base origin/HEAD`

Review the complete diff above. Focus ONLY on security implications newly added by this PR.

Objective

• Only flag issues where you have HIGH confidence of actual exploitability
• Skip theoretical issues, style concerns, or low-impact findings
• Prioritize vulnerabilities leading to unauthorized access, data breaches, or system compromise
• Use two-axis scoring: severity (impact) and confidence (accuracy) are independent

Security Categories (OWASP 2025 Aligned)

Category	Key Checks	OWASP
Access Control	IDOR, privilege escalation, SSRF, CORS, CSRF, path traversal	A01
Security Misconfiguration	Default credentials, debug endpoints, cloud misconfig, XXE	A02
Supply Chain	Dependency confusion, unpinned actions, vulnerable deps, CI/CD risks	A03
Cryptographic Failures	Hardcoded keys, weak algorithms, insecure randomness, cert validation	A04
Injection	SQLi, command injection, XSS, template injection, NoSQL injection	A05
Auth & Session	Authentication bypass, JWT vulns, session management, missing MFA	A07
Deserialization & Integrity	Unsafe deserialization, prototype pollution, unsigned updates	A08
Error Handling	Fail-open patterns, exception swallowing, verbose error disclosure	A10
API Security	BOLA, mass assignment, shadow APIs, missing rate limiting	API Top 10
LLM/AI Security	Prompt injection, unsafe output handling, excessive agency	LLM Top 10

See WORKFLOW.md for detailed subcategories and severity assignment reference.

Analysis Methodology

Phase 1 — Repository Context: Identify existing security frameworks, sanitization patterns, and security model in the codebase.

Phase 1.5 — Automated Security Scan (Optional): Run Trivy (vulnerability/IaC scanning) and Gitleaks (secret detection) if available. Locate the scan script via Glob for **/review/scripts/security-scan.sh, then execute: bash {script_path} --quick --output-dir {output-directory}. If tools are not installed, skip gracefully — this phase is informational only. See WORKFLOW.md for details.

Phase 2 — Comparative Analysis: Compare new code against established secure practices. Flag deviations and new attack surfaces. Cross-reference with automated scan results if available.

Phase 3 — Vulnerability Assessment: Trace data flow from user inputs to sensitive operations. Confirm sink reachability and check for sanitizers in the path.

See WORKFLOW.md for detailed methodology and sub-task orchestration.

Two-Axis Scoring

Severity (Impact)

Severity	Criteria	Example
CRITICAL	RCE, auth bypass, mass data exfiltration	Deserialization RCE, SQLi with shell access
HIGH	Significant data access or privilege escalation	SQLi read, stored XSS, SSRF to cloud metadata
MEDIUM	Limited impact or requires user interaction	Reflected XSS, CSRF, IDOR on non-sensitive data
LOW	Defense-in-depth, minimal direct impact	Missing headers, verbose errors

Confidence (Accuracy)

Confidence	Description	Action
HIGH	Data flow confirmed, clear exploit path	Report — include in findings
MEDIUM	Pattern match, context needed to confirm	Report only if severity >= HIGH
LOW	Theoretical or framework likely handles	Do not report

Confidence & Signal Quality

Before reporting any finding, assess using both axes:

Severity \ Confidence	HIGH	MEDIUM	LOW
CRITICAL	Report	Report	Suppress
HIGH	Report	Report	Suppress
MEDIUM	Report	Suppress	Suppress
LOW	Suppress	Suppress	Suppress

Finding caps: Max 8 meaningful findings (Blocker + Improvement + Question) and max 2 Nits per review. Keep the highest-severity, highest-confidence items.

Self-reflection: After generating all candidate findings, re-evaluate each in context. Remove redundant, low-signal, or theoretical items. Apply false positive filtering from WORKFLOW.md.

Triage Matrix

Categorize every finding:

• [Blocker]: Must fix before merge — CRITICAL/HIGH severity + HIGH confidence. RCE, auth bypass, injection with confirmed data flow (confidence >= HIGH)
• [Improvement]: Strong recommendation — HIGH severity + MEDIUM confidence, or MEDIUM + HIGH. Clear vulnerability pattern, may need context verification (confidence >= MEDIUM)
• [Question]: Seeks clarification — potential vulnerability depending on context, intent unclear (confidence >= MEDIUM)
• [Nit]: Minor hardening suggestion, optional — max 2 per review
• [Praise]: Acknowledge good security practice — max 1 per review

Phase 4 — Per-Finding Haiku Verification

After Phase 3 identifies candidate findings, launch N parallel Haiku agents (one per finding) for independent verification.

Each Haiku agent receives:

• The finding description, severity, confidence, category, and CWE
• The relevant diff section around the flagged code
• The framework/sanitizer context discovered in Phase 1

Each agent verifies:

1. Data flow reachability — Can user input actually reach the vulnerable sink?
2. Sanitizer presence — Are there sanitizers/validators in the path that the main analysis missed?
3. Framework handling — Does the framework's security model prevent this exploit?
4. False positive patterns — Does this match any hard exclusion or precedent from WORKFLOW.md?

Each agent returns a verdict:

• KEEP — Finding confirmed. Data flow verified, no sanitizers found, exploit path is valid.
• DISMISS — False positive. Reason: {specific evidence — framework handling, sanitizer found, etc.}
• DOWNGRADE — Valid but lower severity or confidence. Reason: {what changed — e.g., only reachable by admins, mitigating control exists.}

Apply verdicts: remove DISMISSED findings, adjust DOWNGRADED findings' severity/confidence. Remaining findings proceed to report.

If a Haiku verification agent fails for a finding, default to KEEP (conservative — finding stays).

See WORKFLOW.md for the Haiku verification prompt template and verdict criteria.

Output Format

For each vulnerability found:

### [Triage] Vuln N: {Category Code}: `{file}:{line}`

* **Severity**: {CRITICAL|HIGH|MEDIUM}
* **Confidence**: {HIGH|MEDIUM}
* **Category**: {OWASP A0X:2025 or API/LLM Top 10}
* **CWE**: CWE-XXX
* **Description**: {What the vulnerability is and how it can be exploited}
* **Exploit Scenario**: {Specific attack path with example payload}
* **Recommendation**: {Concrete fix with code example}

Phase 5 — Report Generation

1. Create output directory using Bash: mkdir -p {output-directory}
2. Save the report to: {output-directory}/{YYYY-MM-DD}_{HH-MM-SS}_security-review.md

Include this header:

# Security Review Report

**Date**: {ISO 8601 date}
**Branch**: {current branch name}
**Commit**: {short commit hash}
**Reviewer**: Claude Code (security-review)
**Framework**: OWASP Top 10 2025 + API Top 10 + LLM Top 10

## Summary
- **Blocker**: {count} findings
- **Improvement**: {count} findings
- **Question**: {count} findings
- **Total**: {count} actionable findings
- **Automated Scan**: {Passed | X issues found | Skipped — tools not installed}
- **Haiku Verification**: {N} findings verified — {kept} kept, {dismissed} dismissed, {downgraded} downgraded

---

3. Display the full report to the user in the chat
4. Confirm the save: Security report saved to: {output-directory}/{filename}

Phase 6 — Optional GitHub PR Posting

Only execute if --post-to-pr flag was passed.

1. Check if an open PR exists for the current branch via gh pr view
2. If no PR exists, inform the user: "No open PR found for this branch. Skipping GitHub posting."
3. If a PR exists, check eligibility via a Haiku agent:
   • Is the PR closed? → skip
   • Is the PR a draft? → skip
   • Has Claude already commented on this PR? → skip
4. If eligible, format findings as a concise PR comment and post via gh pr comment
5. Use the security-specific GitHub comment format from WORKFLOW.md

Phase 7 — Automatic Verification

After saving the report and confirming the save to the user, invoke the false-positive verifier:

1. Use the Skill tool to invoke review:verify-findings with the saved report path as the argument
2. The verifier runs in an isolated forked context and produces a .verified.md report
3. After verification completes, inform the user of both report locations

If the Skill tool is not available (e.g., running inside a subagent), inform the user:

Run verification manually: /review:verify-findings {report-path}

False Positive Filtering

Apply the false positive filtering rules from WORKFLOW.md before finalizing. Each finding must pass the signal quality matrix above.

Resources

• WORKFLOW.md - Detailed category taxonomy, analysis methodology, false positive filtering, sub-task orchestration
• EXAMPLES.md - Sample security review reports
• TROUBLESHOOTING.md - Common issues and calibration guidance