8 papers
SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents
Hao Cheng, Changtao Miao, Tianle Song +21
Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabilities enable complex real-world…
Reliable and Responsible Foundation Models: A Comprehensive Survey
Xinyu Yang, Junlin Han, Rishi Bommasani +49
Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), a…
Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Models
Hao Cheng, Erjia Xiao, Yichi Wang +8
Current Cross-Modality Generation Models (GMs) demonstrate remarkable capabilities in various generative tasks. Given the ubiquity and information richness of vision modality input…
Jailbreak-AudioBench: In-Depth Evaluation and Analysis of Jailbreak Threats for Large Audio Language Models
Hao Cheng, Erjia Xiao, Jing Shao +6
Large Language Models (LLMs) demonstrate impressive zero-shot performance across a wide range of natural language processing tasks. Integrating various modality encoders further ex…
Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models
Hao Cheng, Erjia Xiao, Jiayan Yang +6
Current image generation models can effortlessly produce high-quality, highly realistic images, but this also increases the risk of misuse. In various Text-to-Image or Image-to-Ima…
Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models
Hao Cheng, Erjia Xiao, Yichi Wang +12
Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabul…
Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models
Hao Cheng, Erjia Xiao, Jiayan Yang +8
Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities. In particular, the tra…
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model
Hao Cheng, Erjia Xiao, Jindong Gu +6
Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of…