activity
20232026
most citedMadvex: Instrumentation-based Adversarial Attacks on Machine Learning Malware Detection

1 citations · 1 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CR2026

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…

cs.CL2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…