briiirussell

Research a specific CVE or vulnerability disclosure end-to-end — what version is affected, is your code reachable, is there a public PoC, is there a patch, what's the exposure window, what's the mitigation if you can't patch immediately. Use when the user mentions 'CVE,' 'vulnerability research,' 'is this CVE relevant,' 'zero-day,' 'CISA KEV,' 'GitHub Security Advisory,' 'reachability analysis,' 'patch analysis,' 'exploit availability,' 'EPSS,' 'CVSS,' or 'should we drop everything and patch this.'

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Perform black-box / grey-box web application penetration testing on an authorized target — auth bypass, IDOR, session handling, business-logic flaws, parameter tampering, Burp Suite / OWASP ZAP workflows. Use when the user mentions 'web pentest,' 'web application penetration test,' 'pentesting,' 'bug bounty,' 'Burp Suite,' 'ZAP,' 'OWASP testing,' 'authentication testing,' 'session testing,' 'authorization testing,' 'business logic testing,' 'web vulnerability testing,' or has explicit authorization to test a live web application.

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Audit cryptography implementation — algorithm choice, key sizes, KDF parameters, IV/nonce handling, signature verification, randomness, TLS configuration, and key rotation. Deeper than owasp-audit A02. Use when the user mentions 'crypto review,' 'cryptography audit,' 'encryption review,' 'KDF,' 'PBKDF2,' 'Argon2,' 'bcrypt cost,' 'IV reuse,' 'nonce reuse,' 'AES mode,' 'AES-GCM,' 'AES-ECB,' 'signature verification,' 'TLS configuration,' 'cipher suites,' 'key rotation,' 'libsodium,' 'BoringSSL,' or 'is this crypto right.'

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Run a structured threat-modeling session for a new feature, system, or architecture — STRIDE, attack trees, data flow diagrams, abuse cases. Use when the user mentions 'threat model,' 'threat modeling,' 'STRIDE,' 'attack tree,' 'abuse case,' 'data flow diagram,' 'DFD,' 'security architecture review,' 'security review,' 'design review,' 'pre-implementation security,' 'shift left,' 'what could go wrong,' or needs strategic security thinking before code is written.

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Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.

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