6 papers
HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models
Zixing Chen, Yifeng Gao, Li Wang +8
Vision-Language-Action (VLA) models inherit rich world knowledge from vision-language backbones and acquire executable skills via action demonstrations. However, existing evaluatio…
Internal Safety Collapse in Frontier Large Language Models
Yutao Wu, Xiao Liu, Yifeng Gao +7
This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a s…
RedTopic: Toward Topic-Diverse Red Teaming of Large Language Models
Jiale Ding, Xiang Zheng, Yutao Wu +5
As large language models (LLMs) are increasingly deployed as black-box components in real-world applications, red teaming has become essential for identifying potential risks. It t…
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…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
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…