1 citations · 1 across the 6 of their papers we have counts for
6 papers
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…
Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
Zhiyu Xu, Lean Wang, Yuanxin Liu +5
Vision-Language Models (VLMs) have demonstrated remarkable proficiency in general multi-modal understanding; yet they struggle to efficiently acquire continually evolving domain-sp…
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…
Scalable Text-Embedding-informed Cognitive Diagnosis of Large Language Models
Jia Liu, Zhiyu Xu, Yuqi Gu
Large language models (LLMs) have achieved remarkable performance on diverse benchmarks, yet existing evaluation practices largely rely on coarse summary metrics that obscure under…
Progressive Behavioral Drift through Compression Valleys in Large Language Models
Zhiyuan Xu, Stanislav Abaimov, Joseph Gardiner +1
We show that attention sinks and compression valleys create a vulnerable region in decoder-only Transformers, where small activation perturbations can be amplified through the auto…
The dark deep side of DeepSeek: Fine-tuning attacks against the safety alignment of CoT-enabled models
Zhiyuan Xu, Joseph Gardiner, Sana Belguith
Large language models are typically trained on vast amounts of data during the pre-training phase, which may include some potentially harmful information. Fine-tuning attacks can e…