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hunt-llm-ai

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Hunt LLM/AI feature bugs — prompt injection, indirect injection, exfiltration via tool-use/markdown, ASCII smuggling, agentic AI security (OWASP Agentic Apps 2026, ASI01-ASI10). Patterns: direct injection ('ignore previous instructions'), indirect injection via documents/web pages/email the model reads, ASCII smuggling (Unicode Tags block U+E0000-U+E007F, invisible to humans, decoded by the model), tool-use exfiltration (model has fetch/browse tool, attacker injects OOB URL, model exfils chat history/secrets), markdown-image zero-click exfil, system-prompt extraction, IDOR-via-AI (cross-tenant data). Targets: chatbots, RAG, summarizers, agentic copilots, MCP tools. Detection: any LLM-backed endpoint, doc upload triggering AI processing, autonomous agent with tools. Validate: OOB/Collaborator callback for exfil, verbatim-reproducible system-prompt leak (run twice), verifiable cross-tenant leak or RCE. Confabulation is NOT a finding. Use when hunting AI features, chatbots, RAG, agentic systems, MCP.

knowledge/Claude-BugHunter/skills/hunt-llm-ai/SKILL.md

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## 11. LLM / AI FEATURES

LLM bugs are only worth reporting when they cross a trust boundary you can **prove** — an OOB callback, a verbatim-reproducible secret, a cross-tenant record, or code execution. A model "saying something bad once" is confabulation, not a vulnerability. Read the False-Positive Gate before claiming anything.

> **Naming note (was wrong in v1):** the model-level list is **OWASP Top 10 for LLM Applications 2025** (LLM01 Prompt Injection, LLM07 System Prompt Leakage, LLM08 Vector/Embedding Weaknesses). The agent-level list is **OWASP Top 10 for Agentic Applications (2026)** from the **Agentic Security Initiative (ASI)**, codes ASI01–ASI10. Do not write "OWASP ASI 2026" as if it were one document — cite the correct list per finding.

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## False-Positive Gate (Read First)

LLMs are non-deterministic. The single biggest source of bogus LLM reports is **confabulation** — the model inventing a plausible "system prompt" or "other user's data" that is not real. Apply every check below before writing a word.

1. **Run-twice rule (verbatim reproducibility).** Send the identical extraction prompt in two fresh sessions (clear cookies/conversation). A real system-prompt leak reproduces **token-for-token**. If the two outputs differ in wording, structure, or detail, it is confabulation — discard it.
2. **Anchor to a known-secret.** Don't ask "what is your system prompt"; ask the model to echo a string only the real prompt would contain (a tool name, an internal URL, a tenant ID format, a guardrail phrase you already saw leak in an error). Reproducible echo of a *non-guessable* anchor = real leak.