ai-ml-attacks
This skill should be used when the user asks about "AI security", "ML pipeline attacks", "prompt injection", "model deserialization", "unsafe model loading",…
Guided first-run security audit: doctor, scope, threats, scan, verify, report.
$ npx -y skills add allsmog/vuln-scout --skill start-audit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/start-auditContext preview
The summary Claude sees to decide when to auto-load this skill.
Guided first-run security audit: doctor, scope, threats, scan, verify, report.
name: start-audit description: Guided first-run security audit: doctor, scope, threats, scan, verify, report.
Use this task skill when the user asks to start a security audit, audit this repo, perform a security review of a codebase, or review this codebase for vulnerabilities.
1. Run `python3 vuln-scout/scripts/doctor.py --strict` or ask the user to address missing quick-profile dependencies. 2. Trigger `session-init` and `large-codebase-check` hooks when the target is a repo or monorepo. 3. Run `/vuln-scout:scope` to establish boundaries and write `.claude/audit-plan.md`. 4. Call `app-mapper`, then `/vuln-scout:threats`, then `threat-modeler`. 5. Run `/vuln-scout:scan --profile quick` first; use `deep` only when optional analyzers are installed or requested. 6. Call `code-reviewer` on prioritized findings. 7. Run `/vuln-scout:verify` per finding that needs confirmation. 8. Finish with `/vuln-scout:report --format bundle --output evidence-bundle`.
Do not trigger for knowledge-only questions about STRIDE, OWASP, dangerous functions, framework patterns, compliance mapping, exploit techniques, or vulnerability classes. Let those knowledge skills answer directly unless the user asks to audit a target.
AI-powered whitebox penetration testing plugin for Claude Code. 9 languages, 22 skills, 7 autonomous agents. STRIDE threat modeling, OWASP 2025 coverage, polyglot monorepo support.
Repo: allsmog/vuln-scout
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