12 papers
SPARK: Security Knowledge Priming and Representation-Guided Knowledge Activation for LLM-based Secure Code Generation
Xiaoyun Xu, Lichao Wu, Jona te Lintelo +2
Large language models routinely generate code with exploitable security flaws. Prior literature attributes this limitation to a lack of security expertise, steering current defense…
AttackonCTF: Defending Hardware Security Competition Benchmarks in the Age of LLMs
Mohamadreza Rostami, Nikhilesh Singh, Stephen Muttathil +5
Hardware security competitions such as HackTheSilicon serve as benchmarking platforms for evaluating vulnerability detection methods and for training humans and AI. However, our st…
NeST: Neuron Selective Tuning for LLM Safety
Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami +1
Safety alignment is essential for the responsible deployment of Large Language Models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to updat…
GoodVibe: Security-by-Vibe for LLM-Based Code Generation
Maximilian Thang, Lichao Wu, Sasha Behrouzi +4
Large language models (LLMs) are increasingly used for code generation in fast, informal development workflows, often referred to as vibe coding, where speed and convenience are pr…
RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
Zhiyuan Xu, Joseph Gardiner, Sana Belguith +1
Safety alignment is critical for the responsible deployment of large language models (LLMs). As Mixture-of-Experts (MoE) architectures are increasingly adopted to scale model capac…
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
Jona te Lintelo, Lichao Wu, Marina KrÄek +2
Mixture-of-Experts (MoE) architectures in Large Language Models (LLMs) have significantly reduced inference costs through sparse activation. However, this sparse activation paradig…