126 citations · 179 across the 16 of their papers we have counts for
6 papers · 1 filter
ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning
Ignacio D. Lopez-Miguel, Andreas Happe, Jürgen Cito +3
Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment. While these agents demonstrate impressive cap…
The Ethics of Autonomous AI Agents for Offensive Security
Andreas Happe, Jürgen Cito, Jasmin Wachter
LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling - deterministic, narrowly scoped, and operated by trained practitioner…
Cochise: A Reference Harness for Autonomous Penetration Testing
Andreas Happe, Jürgen Cito
Recent work on LLM-driven autonomous penetration testing reports promising results, but existing systems often bundle architectural, prompting, and tool-integration choices togethe…
Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents
Benjamin Probst, Andreas Happe, Jürgen Cito
Cloud-based Large Language Models (LLMs) can perform autonomous penetration-testing sub-tasks such as Linux privilege escalation, but raise security, privacy, and sovereignty conce…
Can LLMs Hack Enterprise Networks? -- Replicated Computational Results (RCR) Report
Andreas Happe, Jürgen Cito
This is the Replicated Computational Results (RCR) Report for the paper ``Can LLMs Hack Enterprise Networks?" The paper empirically investigates the efficacy and effectiveness of d…
Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation
Philipp Normann, Andreas Happe, Jürgen Cito +1
LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to reproduce or use with sensitive code. This…