13 papers
Walma: Learning to See Memory Corruption in WebAssembly
Oussama Draissi, Mark Günzel, Ahmad-Reza Sadeghi +1
WebAssembly's (Wasm) monolithic linear memory turns a single memory-corruption bug into a bidirectional threat: a compromised module can attack its embedding host, and a malicious…
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…
Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard
Sahar Abdelnabi, Chris Hicks, Konrad Rieck +1
The benchmarks used to evaluate AI agents in security-critical roles suffer from crucial weaknesses. Building on recent empirical evidence, we characterize three core challenges th…
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections
Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2
Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server, but the cut-layer interface introduces a key challenge: hig…