4 papers
AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models
Jiayu Li, Yunhan Zhao, Xiang Zheng +4
Vision-Language-Action (VLA) models enable robots to interpret natural-language instructions and perform diverse tasks, yet their integration of perception, language, and control i…
Defense-to-Attack: Bypassing Weak Defenses Enables Stronger Jailbreaks in Vision-Language Models
Yunhan Zhao, Xiang Zheng, Xingjun Ma
Despite their superb capabilities, Vision-Language Models (VLMs) have been shown to be vulnerable to jailbreak attacks. While recent jailbreaks have achieved notable progress, thei…
RedRFT: A Light-Weight Benchmark for Reinforcement Fine-Tuning-Based Red Teaming
Xiang Zheng, Xingjun Ma, Wei-Bin Lee +1
Red teaming has proven to be an effective method for identifying and mitigating vulnerabilities in Large Language Models (LLMs). Reinforcement Fine-Tuning (RFT) has emerged as a pr…
CALM: Curiosity-Driven Auditing for Large Language Models
Xiang Zheng, Longxiang Wang, Yi Liu +3
Auditing Large Language Models (LLMs) is a crucial and challenging task. In this study, we focus on auditing black-box LLMs without access to their parameters, only to the provided…