126 citations · 174 across the 16 of their papers we have counts for
4 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…
Reducing Token Usage of State-in-Context Agents using Minification
Nicolas Hrubec, Jürgen Cito
This paper presents a replication and extension of the recently introduced state-in-context agent framework. We independently re-implement the DirectSolve variant and evaluate it o…
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…