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Treat AI systems as chains of prompt, retrieval, memory, tools, and permissions.
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Based on SOC occupation classification
Four-phase autonomous bug bounty orchestration. Phase 0 maps external assets with deterministic Python helpers, Phase 1 lets AI prioritize attack surfaces, Phase 2 gives AI autonomous attack control, and Phase 3 produces verifier-only reports.
Four-phase autonomous bug bounty workflow. Python handles deterministic collection and verifier support; AI owns prioritization, attack reasoning, and autonomous direction changes.
Report generation agent. Convert verifier-confirmed findings into a fixed evidence-based report without adding speculative impact.
Rank reconnaissance-derived attack surfaces and design evidence-driven tests without active exploitation.
Test ranked attack surfaces autonomously, preserve evidence, and turn new access into additional attack-chain hypotheses.
A compact routing skill for choosing vulnerability hypotheses; detailed payloads live in references, not here.
| name | ai-security |
| description | Treat AI systems as chains of prompt, retrieval, memory, tools, and permissions. |
| metadata | {"tags":"ai,llm,rag,agentic"} |
判断用户或外部内容能否影响 AI 的数据访问、工具调用、记忆、检索结果或后端动作。
references/INDEX.md；候选资源：ai-security-testing.md、ai-security-vulnforge.md