5 papers
Dissecting the Black Box: Circuit-Level Analysis of LLM Vulnerability Detection
Syafiq Al Atiiq, Chun Zhou, Christian Gehrmann
Large language models (LLMs) can detect software vulnerabilities, but how do they actually identify vulnerable code? We address this question using mechanistic interpretability; an…
SAMSEM -- A Generic and Scalable Approach for IC Metal Line Segmentation
Christian Gehrmann, Jonas Ricker, Simon Damm +5
In light of globalized hardware supply chains, the assurance of hardware components has gained significant interest, particularly in cryptographic applications and high-stakes scen…
Lost in the Pages: WebAssembly Code Recovery through SEV-SNP's Exposed Address Space
Markus Berthilsson, Christian Gehrmann
WebAssembly (Wasm) has risen as a widely used technology to distribute computing workloads on different platforms. The platform independence offered through Wasm makes it an attrac…
Vulnerability Detection in Popular Programming Languages with Language Models
Syafiq Al Atiiq, Christian Gehrmann, Kevin Dahlén
Vulnerability detection is crucial for maintaining software security, and recent research has explored the use of Language Models (LMs) for this task. While LMs have shown promisin…
From Generalist to Specialist: Exploring CWE-Specific Vulnerability Detection
Syafiq Al Atiiq, Christian Gehrmann, Kevin Dahlén +1
Vulnerability Detection (VD) using machine learning faces a significant challenge: the vast diversity of vulnerability types. Each Common Weakness Enumeration (CWE) represents a un…