142 citations · 144 across the 14 of their papers we have counts for
15 papers · 1 filter
SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs
Wenyu Chen, Li Wang, Chuanchao Zang +5
Video Multimodal Large Language Models (Video-MLLMs) support reasoning over video inputs, yet remain vulnerable to jailbreak attacks that elicit policy-violating responses. Existin…
Transferable End-to-End Optimization for Indirect Long-Term Memory Poisoning in LLM Agents
Chuanchao Zang, Jianing Wang, Wenyu Chen +5
Long-term memory can turn untrusted external content into persistent influence over an LLM agent's future decisions, creating the threat of indirect memory poisoning. A successful…
Extracting Knowledge from Tools in LLM Agents
Chuanchao Zang, Jianing Wang, Wenyu Chen +6
LLM agents commonly use knowledge-based tools and access their underlying files, databases, and search indexes through tool invocation. This integration improves agents' ability to…
Understanding Stage-Wise Utility-Risk Trade-offs in LLM Agent Memory
Chuanchao Zang, Zijian Cao, Xiangtao Meng +6
Long-term memory is becoming a core capability of LLM agents, enabling personalization and long-horizon interaction. However, memory mechanisms that retain, transform, or expose mo…
Isolated but Exposed: Persistence-Based Memory Extraction Attack on LLM Agents
Xinyu Gao, Wenyu Chen, Xiangtao Meng +5
LLM-based agents extend large language models with long-term memory (LTM) that persists privacy-sensitive user data across sessions. Production systems mitigate extraction risks th…
Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models
Xiangtao Meng, Wenyu Chen, Chuanchao Zang +5
Large language models (LLMs) may require additional defenses after deployment as risks and governance requirements evolve. Subsequent defenses can interact with earlier defenses, r…
Not All Tokens Are Created Equal: Query-Efficient Jailbreak Fuzzing for LLMs
Wenyu Chen, Xiangtao Meng, Chuanchao Zang +6
Large Language Models(LLMs) are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, th…
VidLeaks: Membership Inference Attacks Against Text-to-Video Models
Li Wang, Wenyu Chen, Ning Yu +2
The proliferation of powerful Text-to-Video (T2V) models, trained on massive web-scale datasets, raises urgent concerns about copyright and privacy violations. Membership inference…
One Risk Down, Another Up: Cross-Risk Interactions Induced by LLM Defenses
Xiangtao Meng, Tianshuo Cong, Li Wang +4
Large Language Models (LLMs) are increasingly deployed in high-stakes settings, where they face diverse risks. Numerous defense strategies have been proposed to mitigate these risk…
DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
Xinyu Gao, Xiangtao Meng, Yingkai Dong +2
While Retrieval-Augmented Generation (RAG) effectively reduces hallucinations by integrating external knowledge bases, it introduces vulnerabilities to membership inference attacks…
FaceSwapGuard: Safeguarding Facial Privacy from DeepFake Threats through Identity Obfuscation
Li Wang, Zheng Li, Xuhong Zhang +2
DeepFakes pose a significant threat to our society. One representative DeepFake application is face-swapping, which replaces the identity in a facial image with that of a victim. A…
Beyond Known Fakes: Generalized Detection of AI-Generated Images via Post-hoc Distribution Alignment
Li Wang, Wenyu Chen, Xiangtao Meng +2
The rapid proliferation of highly realistic AI-generated images poses serious security threats such as misinformation and identity fraud. Detecting generated images in open-world s…
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models
Yingkai Dong, Xiangtao Meng, Ning Yu +2
Text-to-image (T2I) generative models have revolutionized content creation by transforming textual descriptions into high-quality images. However, these models are vulnerable to ja…
Learning Symmetric and Asymmetric Steganography via Adversarial Training
Zheng Li, Ge Han, Yunqing Wei +1
Steganography refers to the art of concealing secret messages within multiple media carriers so that an eavesdropper is unable to detect the presence and content of the hidden mess…
How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN
Zheng Li, Chengyu Hu, Yang Zhang +1
Deep learning techniques have made tremendous progress in a variety of challenging tasks, such as image recognition and machine translation, during the past decade. Training deep n…