13 papers · 1 filter
SEEK: Agentic Knowledge Graph Retrieval for Secure Code Generation
Varun Gadey, Markus Tohn, Lichao Wu +2
Large Language Models (LLMs) can generate functionally correct code while introducing exploitable security weaknesses, particularly when developers specify functionality without ex…
WoE Wrote It? Watermarking Mixture-of-Experts LLMs for Black-Box Text Provenance
Jona te Lintelo, Lichao Wu, Stjepan Picek
Large Language Model (LLM) watermarks provide a mechanism for text provenance, enabling model owners to identify machine-generated content and attribute it to a specific watermarke…
PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?
Yitian Zhou, Jingyu Zheng, Qiliang Jiang +6
Industrial control systems (ICSs) rely on programmable logic controllers (PLCs) to connect networked computation with physical control. Tool-using large language model (LLM) agents…
NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation
Zhiyuan Xu, Muhammad Firhard Roslan, Joseph Gardiner +2
Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated testing methods, however, large…
From Verdict to Diagnosis: Attributable Security Review of Pull Requests
Zhuo Chen, Boyang Wang, Xiyue Zhang +4
Automated code reviewers are increasingly used as gates on pull requests (PRs), yet evaluations measure whether they block a malicious change. A block may be triggered by an unrela…
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…
Activating Latent Security Knowledge through LLM-Guided Risk Analysis for Secure Code Generation
Xiaoyun Xu, Lichao Wu, Jona te Lintelo +3
Large language models are pretrained on extensive software and security corpora, yet they frequently generate functionally correct code containing well-known vulnerabilities. Exist…
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 Krček +2
Mixture-of-Experts (MoE) architectures in Large Language Models (LLMs) have significantly reduced inference costs through sparse activation. However, this sparse activation paradig…
AegisSat: Securing AI-Enabled SoC FPGA Satellite Platforms
Huimin Li, Vusal Novruzov, Nikhilesh Singh +3
The increasing adoption of System-on-Chip Field-Programmable Gate Arrays (SoC FPGAs) in AI-enabled satellite systems, valued for their reconfigurability and in-orbit update capabil…
Large Language Lobotomy: Jailbreaking Mixture-of-Experts via Expert Silencing
Jona te Lintelo, Lichao Wu, Stjepan Picek
The rapid adoption of Mixture-of-Experts (MoE) architectures marks a major shift in the deployment of Large Language Models (LLMs). MoE LLMs improve scaling efficiency by activatin…
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…