7 papers
From static to adaptive: immune memory-based jailbreak detection for large language models
Jun Leng, Yu Liu, Litian Zhang +3
Large Language Models (LLMs) serve as the backbone of modern AI systems, yet they remain susceptible to adversarial jailbreak attacks. Consequently, robust detection of such malici…
Jailbreaking LLMs & VLMs: Mechanisms, Evaluation, and Unified Defense
Zejian Chen, Chaozhuo Li, Chao Li +3
This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models (LLMs) and Vision-Language Models (VLMs), emphasizing that jailbreak vulnerabilit…
Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning
Rui Pu, Chaozhuo Li, Rui Ha +3
Defending large language models (LLMs) against jailbreak attacks is essential for their safe and reliable deployment. Existing defenses often rely on shallow pattern matching, whic…
LLMs are Introvert
Litian Zhang, Xiaoming Zhang, Bingyu Yan +5
The exponential growth of social media and generative AI has transformed information dissemination, fostering connectivity but also accelerating the spread of misinformation. Under…
Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Chaozhuo Li, Pengbo Wang, Chenxu Wang +7
Edgar Allan Poe noted, "Truth often lurks in the shadow of error," highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions…
One SPACE to Rule Them All: Jointly Mitigating Factuality and Faithfulness Hallucinations in LLMs
Pengbo Wang, Chaozhuo Li, Chenxu Wang +3
LLMs have demonstrated unprecedented capabilities in natural language processing, yet their practical deployment remains hindered by persistent factuality and faithfulness hallucin…