activity
20172026
most citedGetting pwn'd by AI: Penetration Testing with Large Language Models

126 citations · 179 across the 16 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.SE2025

Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair

Piotr Przymus, Andreas Happe, Jürgen Cito

Large Language Model (LLM) - based Automated Program Repair (APR) systems are increasingly integrated into modern software development workflows, offering automated patches in resp…

cs.CR2025★ 1 cited

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…

cs.CR2025

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,…

cs.CR2025★ 2 cited

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…

cs.CR2025★ 10 cited

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…