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

collaborators

16 papers

cs.SE2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026★ 1 cited

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…

cs.CR2026

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…

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…

cs.CR2024

Got Root? A Linux Priv-Esc Benchmark

Andreas Happe, Jürgen Cito

Linux systems are integral to the infrastructure of modern computing environments, necessitating robust security measures to prevent unauthorized access. Privilege escalation attac…

cs.CR2023★ 15 cited

LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks

Andreas Happe, Aaron Kaplan, Juergen Cito

Penetration-testing is crucial for identifying system vulnerabilities, with privilege-escalation being a critical subtask to gain elevated access to protected resources. Language M…

cs.SE2023★ 20 cited

Understanding Hackers' Work: An Empirical Study of Offensive Security Practitioners

Andreas Happe, Jürgen Cito

Offensive security-tests are a common way to pro-actively discover potential vulnerabilities. They are performed by specialists, often called penetration-testers or white-hat hacke…

cs.CL2023★ 126 cited

Getting pwn'd by AI: Penetration Testing with Large Language Models

Andreas Happe, Jürgen Cito

The field of software security testing, more specifically penetration testing, is an activity that requires high levels of expertise and involves many manual testing and analysis s…

cs.CR2017★ 4 cited

Advances in Security in Computing and Communications

Jaydip Sen, Javier Franco-Contreras, Gouenou Coatrieux +15

In the era of Internet of Things (IoT) and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous…