The Ethics Of Autonomous Ai Agents For Offensive S

---
name: the-ethics-of-autonomous-ai-agents-for-offensive-s
description: 'Skill generated from arXiv paper 2607.20255: The Ethics of Autonomous AI Agents for Offensive Security'
metadata:
  {
    "arxiv": {
      "id": "2607.20255",
      "title": "The Ethics of Autonomous AI Agents for Offensive Security",
      "authors": ['Andreas Happe', 'Jürgen Cito', 'Jasmin Wachter'],
      "published": "2026-07-22",
      "categories": ['cs.CR', 'cs.AI'],
      "url": "https://arxiv.org/abs/2607.20255",
      "utility": 1.0
    }
  }
---
# The Ethics of Autonomous AI Agents for Offensive Security

**arXiv:** 2607.20255  
**Published:** 2026-07-22  
**Authors:** Andreas Happe, Jürgen Cito, Jasmin Wachter  
**Categories:** cs.CR, cs.AI  
**Utility:** 1.00

## Key Innovation
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 ...

## Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.CR, cs.AI.

## References
- arXiv: https://arxiv.org/abs/2607.20255