home / skills / phrazzld / claude-config / security-scan
This skill performs end-to-end security analysis of a full codebase, leveraging deep context to reveal cross-file vulnerabilities and data-flow risks.
npx playbooks add skill phrazzld/claude-config --skill security-scanReview the files below or copy the command above to add this skill to your agents.
---
name: security-scan
description: |
Whole-codebase vulnerability analysis leveraging 1M context window.
Loads entire project source, runs deep security analysis in a single pass.
Opus 4.6 found 500 zero-day vulnerabilities in pre-release testing — this skill weaponizes that capability.
argument-hint: "[optional: specific focus area, e.g. 'auth' or 'api routes']"
effort: max
---
# /security-scan
Deep security analysis of an entire codebase in a single pass.
## Philosophy
Traditional security scanning is file-by-file. It misses cross-file vulnerabilities: data flows from user input through multiple modules to a dangerous sink. With Opus 4.6's 1M token context, we load the entire project and trace attack surfaces end-to-end.
**This is NOT a replacement for dedicated SAST/DAST tools.** It's a complementary analysis that catches what those tools miss: logic flaws, auth bypasses, business logic vulnerabilities, and cross-module data flow issues.
## Process
### 1. Load Full Codebase
```bash
# Estimate token count
find . -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.jsx" -o -name "*.py" -o -name "*.go" -o -name "*.rs" -o -name "*.rb" | \
grep -v node_modules | grep -v .next | grep -v dist | grep -v build | \
xargs wc -l 2>/dev/null | tail -1
```
If under ~200K lines: load everything into context via full file reads.
If over: focus on the attack surface (auth, API routes, data access, user input handlers).
### 2. Map Attack Surface
Read ALL of these file categories:
- **Entry points**: API routes, webhooks, form handlers, CLI commands
- **Auth/authz**: Middleware, guards, session management, JWT handling
- **Data access**: Database queries, ORM models, raw SQL
- **User input**: Form validation, request parsing, file uploads
- **External integrations**: API clients, webhook handlers, OAuth flows
- **Secrets management**: env var usage, config files, .env patterns
- **Infrastructure**: Dockerfile, CI/CD workflows, deployment configs
### 3. Vulnerability Analysis
Spawn `security-sentinel` agent with the full diff/codebase context. Analyze for:
**OWASP Top 10:**
- A01: Broken Access Control — missing auth checks, IDOR, privilege escalation
- A02: Cryptographic Failures — weak hashing, plaintext secrets, insecure transport
- A03: Injection — SQL, NoSQL, OS command, LDAP, XSS
- A04: Insecure Design — business logic flaws, missing rate limiting
- A05: Security Misconfiguration — default creds, verbose errors, open CORS
- A06: Vulnerable Components — outdated deps with known CVEs
- A07: Auth Failures — weak passwords, missing MFA, session issues
- A08: Software/Data Integrity — unsigned updates, insecure deserialization
- A09: Logging Failures — missing audit trails, sensitive data in logs
- A10: SSRF — unvalidated URLs, internal network access
**Cross-Module Analysis (unique to 1M context):**
- Trace user input from entry point through all transformations to sink
- Identify auth bypass paths across middleware chains
- Find data exposure through API response serialization
- Detect race conditions in concurrent operations
- Map trust boundary violations across service calls
### 4. Dependency Audit
```bash
# npm/pnpm
pnpm audit 2>/dev/null || npm audit 2>/dev/null
# Python
pip-audit 2>/dev/null || safety check 2>/dev/null
# Go
govulncheck ./... 2>/dev/null
# Rust
cargo audit 2>/dev/null
```
### 5. Secrets Scan
```bash
# Check for hardcoded secrets
grep -rn "sk_live\|sk_test\|AKIA\|password\s*=\s*['\"]" --include="*.ts" --include="*.js" --include="*.py" --include="*.go" . | grep -v node_modules | grep -v ".env.example"
# Check .env files not in .gitignore
git ls-files --cached | grep -i "\.env$" | head -5
```
## Output Format
```markdown
## Security Scan: [project-name]
**Scope:** [X files, Y lines analyzed]
**Effort:** max
**Date:** [timestamp]
---
### Critical (Immediate Fix Required)
- [ ] `file:line` — [Vulnerability type] — [Description] — [Exploit path]
### High (Fix Before Deploy)
- [ ] `file:line` — [Vulnerability type] — [Description]
### Medium (Fix in Sprint)
- [ ] `file:line` — [Vulnerability type] — [Description]
### Low (Track and Fix)
- [ ] `file:line` — [Vulnerability type] — [Description]
### Dependency Vulnerabilities
| Package | Version | CVE | Severity | Fix Version |
|---------|---------|-----|----------|-------------|
### Cross-Module Findings
- [Data flow from X through Y to Z creates injection risk]
- [Auth middleware skipped on route A when accessed via B]
### Positive Observations
- [Good security patterns found]
- [Well-implemented auth flows]
```
## When to Use
- Before any production deployment
- After adding new API routes or auth logic
- When integrating new external services
- During quarterly security reviews
- After dependency updates
## Related
- `/check-quality` — Includes lightweight security scan
- `/review-branch` — security-sentinel is mandatory reviewer
- `/billing-security` — Payment-specific security patterns
This skill performs whole-codebase vulnerability analysis using a 1M-token context window to load and inspect an entire project in a single pass. I combine deep cross-file dataflow tracing with dependency and secrets auditing to surface logic flaws, auth bypasses, and end-to-end attack paths that file-by-file scanners often miss. It is a complementary check, not a replacement for dedicated SAST/DAST tools.
I load the full codebase (or focus on the attack surface if the project is extremely large) and map entry points, auth, data access, user input handlers, integrations, secrets, and infrastructure. The analysis traces user input across modules to dangerous sinks, inspects middleware chains for auth bypasses, enumerates OWASP Top 10 issues, and runs dependency and secrets scans. The output is a prioritized checklist with concrete file/line findings, cross-module narratives, and remediation guidance.
Is this a replacement for SAST/DAST tools?
No. Use this skill as a complementary analysis to catch cross-file logic and dataflow issues that automated SAST/DAST may miss.
How does it handle very large repositories?
If the repo exceeds practical context limits, I focus on the attack surface: entry points, auth, data access, input handlers, integrations, and secrets to maximize signal.