5 papers
Open Models, Open Risks: Measuring Unsafe Generation in Text-to-Image Models In the Wild
Peilin Han, Yang Liu, Yilong Yang +4
Existing safety studies on text-to-image (T2I) jailbreaks are largely conducted in controlled in-the-lab settings, typically on a small number of canonical models. As a result, the…
A Cross-Modal Prompt Injection Attack against Large Vision-Language Models with Image-Only Perturbation
Hao Yang, Zhuo Ma, Yang Liu +3
Large vision-language models (LVLMs) have emerged as a powerful paradigm for multimodal intelligence, but their growing deployment also expands the attack surface of prompt injecti…
The Shawshank Redemption of Embodied AI: Understanding and Benchmarking Indirect Environmental Jailbreaks
Chunyang Li, Zifeng Kang, Junwei Zhang +4
The adoption of Vision-Language Models (VLMs) in embodied AI agents, while being effective, brings safety concerns such as jailbreaking. Prior work have explored the possibility of…
ProvX: Generating Counterfactual-Driven Attack Explanations for Provenance-Based Detection
Weiheng Wu, Wei Qiao, Teng Li +4
Provenance graph-based intrusion detection systems are deployed on hosts to defend against increasingly severe Advanced Persistent Threat. Using Graph Neural Networks to detect the…
CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models
Chunyang Li, Junwei Zhang, Anda Cheng +3
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, but its openness introduces vulnerabilities that can be exploited by…