6 papers
Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Ruiqing Yue, Yu Cui, Zhuoyu Sun +11
Self-evolving runtime harnesses can substantially improve the capabilities of large language model (LLM) agents and provide a promising paradigm for optimizing agent execution. Exi…
Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination
Shuo Liang, Yixing Ma, Pengfei Zhou +33
Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation. Existi…
Towards Provably Secure Generative AI: Reliable Consensus Sampling
Yu Cui, Hang Fu, Sicheng Pan +9
Existing research on generative AI security is primarily driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. This dynamic frequently g…
Can LLMs Threaten Human Survival? Benchmarking Potential Existential Threats from LLMs via Prefix Completion
Yu Cui, Yifei Liu, Hang Fu +4
Research on the safety evaluation of large language models (LLMs) has become extensive, driven by jailbreak studies that elicit unsafe responses. Such response involves information…
VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy
Yu Cui, Sicheng Pan, Yifei Liu +2
Large language models (LLMs) have been widely deployed in Conversational AIs (CAIs), while exposing privacy and security threats. Recent research shows that LLM-based CAIs can be m…
RRTL: Red Teaming Reasoning Large Language Models in Tool Learning
Yifei Liu, Yu Cui, Haibin Zhang
While tool learning significantly enhances the capabilities of large language models (LLMs), it also introduces substantial security risks. Prior research has revealed various vuln…