1 citations · 1 across the 10 of their papers we have counts for
12 papers
Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents
Kristoffer Hempel, Nils Loose, Lorenzo Cavallaro +1
Large language model (LLM) agents are becoming an integral part of the software development lifecycle, and the capabilities granted to them allow an agent to act with the user's au…
U-Space: Uncovering When and Why Uncertainty Arises in Language Models
Tobias Braun, Nils Loose, Alexander Herzog +4
Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ignore: how much can we trust…
FloatDoor: Platform-Triggered Backdoors in LLMs
Nils Loose, Jonas Sander, Felix Mächtle +1
Large language models (LLMs) are increasingly deployed in sensitive settings such as software engineering, where their outputs directly shape downstream artifacts. Recent work has…
Steganography Without Modification: Hidden Communication via LLM Seeds
Felix Mächtle, Jonas Sander, Sebastian Berndt +3
We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or ou…
A Comprehensive Evaluation of Code Language Models for Security Patch Detection
Nils Loose, Joseph Bienhüls, Kristoffer Hempel +2
Automated detection of vulnerability-fixing commits (\vfcs) is critical for timely security patch deployment, as advisory databases lag patch releases by a median of 25 days and ma…
Coverage-Guided Multi-Agent Harness Generation for Java Library Fuzzing
Nils Loose, Nico Winkel, Kristoffer Hempel +3
Coverage-guided fuzzing has proven effective for software testing, but targeting library code requires specialized fuzz harnesses that translate fuzzer-generated inputs into valid…
Beyond Accuracy: Characterizing Code Comprehension Capabilities in (Large) Language Models
Felix Mächtle, Jan-Niclas Serr, Nils Loose +1
Large Language Models (LLMs) are increasingly integrated into software engineering workflows, yet current benchmarks provide only coarse performance summaries that obscure the dive…
Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts
Felix Mächtle, Ashwath Shetty, Jonas Sander +3
Diffusion models have significantly advanced text-to-image generation, enabling the creation of highly realistic images conditioned on textual prompts and seeds. Given the consider…
AutoStub: Genetic Programming-Based Stub Creation for Symbolic Execution
Felix Mächtle, Nils Loose, Jan-Niclas Serr +2
Symbolic execution is a powerful technique for software testing, but suffers from limitations when encountering external functions, such as native methods or third-party libraries.…
Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction
Felix Mächtle, Nils Loose, Tim Schulz +4
As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerab…
OCEAN: Open-World Contrastive Authorship Identification
Felix Mächtle, Jan-Niclas Serr, Nils Loose +2
In an era where cyberattacks increasingly target the software supply chain, the ability to accurately attribute code authorship in binary files is critical to improving cybersecuri…
Madvex: Instrumentation-based Adversarial Attacks on Machine Learning Malware Detection
Nils Loose, Felix Mächtle, Claudius Pott +2
WebAssembly (Wasm) is a low-level binary format for web applications, which has found widespread adoption due to its improved performance and compatibility with existing software.…