126 citations · 174 across the 16 of their papers we have counts for
9 papers · 1 filter
Lost in Code Generation: Reimagining the Role of Software Models in AI-driven Software Engineering
Jürgen Cito, Dominik Bork
Generative AI enables rapid "vibe coding" and agentic software engineering, where natural-language prompts yield working software systems. This lowers barriers to software creation…
Online and Interactive Bayesian Inference Debugging
Nathanael Nussbaumer, Markus Böck, Jürgen Cito
Probabilistic programming is a rapidly developing programming paradigm which enables the formulation of Bayesian models as programs and the automation of posterior inference. It fa…
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…
Static Factorisation of Probabilistic Programs With User-Labelled Sample Statements and While Loops
Markus Böck, Jürgen Cito
It is commonly known that any Bayesian network can be implemented as a probabilistic program, but the reverse direction is not so clear. In this work, we address the open question…
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,…
The Road to Hybrid Quantum Programs: Characterizing the Evolution from Classical to Hybrid Quantum Software
Vincenzo De Maio, Ivona Brandic, Ewa Deelman +1
Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. Howeve…
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…