6 papers
When Benchmarks Leak: Inference-Time Decontamination for LLMs
Jianzhe Chai, Yu Zhe, Jun Sakuma
Benchmark-based evaluation is the de facto standard for comparing large language models (LLMs). However, its reliability is increasingly threatened by test set contamination, where…
Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Models
Haochuan Xu, Yun Sing Koh, Shuhuai Huang +4
Vision-Language-Action (VLA) models have achieved revolutionary progress in robot learning, enabling robots to execute complex physical robot tasks from natural language instructio…
Pattern Enhanced Multi-Turn Jailbreaking: Exploiting Structural Vulnerabilities in Large Language Models
Ragib Amin Nihal, Rui Wen, Kazuhiro Nakadai +1
Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints gradually. These attacks target d…
Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability
Shojiro Yamabe, Jun Sakuma
Diffusion language models (DLMs) generate tokens in parallel through iterative denoising, which can reduce latency and enable bidirectional conditioning. However, the safety risks…
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
Ting-Wei Liao, Chih-Hsun Lin, Yu-Lin Tsai +5
Local Differential Privacy (LDP) has been widely adopted to protect user privacy in decentralized data collection. However, recent studies have revealed that LDP protocols are vuln…
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy
Wei Junhao, Yu Zhe, Sakuma Jun
Model merging is a technique that combines multiple finetuned models into a single model without additional training, allowing a free-rider to cheaply inherit specialized capabilit…