1 citations · 1 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Backdoor Attacks on Decentralised Post-Training
Oğuzhan Ersoy, Nikolay Blagoev, Jona te Lintelo +3
Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training c…
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…
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…
BadPatches: Routing-Aware Backdoor Attacks on Vision Mixture-of-Experts
Jona te Lintelo, Cedric Chan, Stjepan Picek
Mixture-of-Experts (MoE) architectures have gained significant traction for reducing computational costs in deep neural networks by activating only a sparse subset of parameters du…
The SkipSponge Attack: Sponge Weight Poisoning of Deep Neural Networks
Jona te Lintelo, Stefanos Koffas, Stjepan Picek
Sponge attacks aim to increase the energy consumption and computation time of neural networks. In this work, we present a novel sponge attack called SkipSponge. SkipSponge is the f…