Individual skill

performing-ai-assisted-vulnerability-discovery

Using LLMs to accelerate vulnerability research and pentest workflows — generating syntax-valid fuzzing seeds and evolving grammars, fine-tuned mutation dictionaries, parallel agent-based proof-of-vulnerability generation, and evidence-driven passive analysis of real HTTP traffic via the Burp MCP server. Covers concrete prompts, AFL++/ libFuzzer wiring, and Burp+Codex/Gemini/Ollama MCP setup.

internal/tools/skills/data/ai-security/performing-ai-assisted-vulnerability-discovery/SKILL.md

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Nov 8, 2026
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# Performing AI-Assisted Vulnerability Discovery

## When to Use

- During authorized vulnerability research where complex input formats (SQL, URLs, custom/binary protocols) stall a blind fuzzer
- When bootstrapping a coverage-guided fuzzer (AFL++, libFuzzer, Honggfuzz) that needs syntax-valid, security-relevant seeds
- When you have crash candidates and need to scale proof-of-vulnerability (PoV) generation across many agents/models
- When triaging large volumes of real Burp HTTP traffic and want evidence-driven passive analysis + report drafting
- When working under cost/time budgets (bug-bounty, CTF, AIxCC-style cyber reasoning systems)

## Critical: Techniques Most Often Missed

Teams either ignore LLMs entirely or paste code and hope. The high-value patterns are about
**feeding the model coverage feedback** and **keeping the human/Burp as the source of truth**.
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