The Ethics of Autonomous AI Agents for Offensive Security
LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling -- deterministic, narrowly scoped, and operated by trained practitioners -- agentic security tools exhibit \textit{indeterminacy} along three independent dimensions. First, their actions are drawn from a non-deterministic policy whose outputs resist both ex-ante and ex-post explanation, frustrating incident attribution and pre-deployment safety review. Second, their impact is open-ended due to the non-deterministic actions, agency of utilized models, and opaque LLM supply-chains. Third
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- PossiblePossibly related (embedding) · 65%Devs shipping AI agents what does your security testing look like ? →
- PossiblePossibly related (embedding) · 61%Prompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routers →
- LinkedLinked via arxiv author · 85%Andreas Happe →
“The Ethics of Autonomous AI Agents for Offensive Security”
- LinkedLinked via arxiv author · 85%Jürgen Cito →
“The Ethics of Autonomous AI Agents for Offensive Security”
- LinkedLinked via arxiv author · 85%Jasmin Wachter →
“The Ethics of Autonomous AI Agents for Offensive Security”
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “The Ethics of Autonomous AI Agents for Offensive Security” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “The Ethics of Autonomous AI Agents for Offensive Security” ≈ “Unity-Technologies/ml-agents””
- FuzzySimilar title/name (fuzzy) · 59%datawhalechina/hello-agents →
“Fuzzy title match (0.73): “The Ethics of Autonomous AI Agents for Offensive Security” ≈ “datawhalechina/hello-agents””