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VRSA: Jailbreaking Multimodal Large Language Models through Visual Reasoning Sequential Attack
Shiji Zhao, Shukun Xiong, Yao Huang +7
Multimodal Large Language Models (MLLMs) are widely used in various fields due to their powerful cross-modal comprehension and generation capabilities. However, more modalities bri…
DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios
Yao Huang, Yitong Sun, Yichi Zhang +3
Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also introduces emergent deceptive behav…
NDM: A Noise-driven Detection and Mitigation Framework against Implicit Sexual Intentions in Text-to-Image Generation
Yitong Sun, Yao Huang, Ruochen Zhang +4
Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with…
Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation
Yichi Zhang, Yao Huang, Yifan Wang +10
The trustworthiness of Multimodal Large Language Models (MLLMs) remains an intense concern despite the significant progress in their capabilities. Existing evaluation and mitigatio…
Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation
Yifu Yuan, Haiqin Cui, Yaoting Huang +7
Generalization in embodied AI is hindered by the "seeing-to-doing gap," which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer "pointing" as a uni…
Never compromise with vulnerabilities: a comprehensive survey on AI governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan +62
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, t…
Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space
Yao Huang, Yitong Sun, Shouwei Ruan +3
Large Language Models (LLMs), despite advanced general capabilities, still suffer from numerous safety risks, especially jailbreak attacks that bypass safety protocols. Understandi…
Mitigating Overthinking in Large Reasoning Models via Manifold Steering
Yao Huang, Huanran Chen, Shouwei Ruan +3
Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex tasks such as mathematics and coding. However, these models frequently…
Unveiling the Basin-Like Loss Landscape in Large Language Models
Huanran Chen, Yinpeng Dong, Zeming Wei +4
We discover the emergence of \textit{basins} in the loss landscape of large language models. As model scale increases, LLMs become progressively more resilient to random perturbati…
RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability
Yichi Zhang, Zihao Zeng, Dongbai Li +3
Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have been rapidly progressing and achieving breakthrough performance on complex reasoning tasks such as mathematic…
The Path to Reconciling Quality and Safety in Text-to-Image Generation: Dataset, Method, and Evaluation
Shouwei Ruan, Zhenyu Wu, Yao Huang +5
Content safety is a fundamental challenge for text-to-image (T2I) models, yet prevailing methods enforce a debilitating trade-off between safety and generation quality. We argue th…
When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack
Hanqing Liu, Shouwei Ruan, Yao Huang +2
Vision-Language Models (VLMs) have achieved remarkable success in various tasks, yet their robustness to real-world illumination variations remains largely unexplored. To bridge th…
STAIR: Improving Safety Alignment with Introspective Reasoning
Yichi Zhang, Siyuan Zhang, Yao Huang +7
Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications. However, existing safety alignment methods ty…