126 citations · 174 across the 16 of their papers we have counts for
8 papers · 1 filter
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…
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…
On the Surprising Efficacy of LLMs for Penetration-Testing
Andreas Happe, Jürgen Cito
This paper presents a critical examination of the surprising efficacy of Large Language Models (LLMs) in penetration testing. The paper thoroughly reviews the evolution of LLMs and…
Recognition Without Mitigation: Ethical Frameworks in Autonomous Offensive-LLM Agent Research
Andreas Happe, Jürgen Cito
Large language models have moved from advising on offensive security to autonomously conducting it. A growing literature presents agents that execute reconnaissance, exploitation,…
Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design
Andreas Happe, Jürgen Cito
Large Language Models (LLMs) have emerged as a powerful approach for driving offensive penetration-testing tooling. Due to the opaque nature of LLMs, empirical methods are typicall…
Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks
Andreas Happe, Jürgen Cito
Enterprise penetration-testing is often limited by high operational costs and the scarcity of human expertise. This paper investigates the feasibility and effectiveness of using La…