2,589+ academic papers on AI security, attacks, and defenses
Showing 1461–1480 of 2,589 papers
Ji Guo, Wenbo Jiang, Yansong Lin +7 more
Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics. However, their complex...
Quy-Anh Dang, Chris Ngo, Truong-Son Hy
As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount....
Hang Fu, Wanli Peng, Yinghan Zhou +3 more
The widespread adoption of Large Language Model (LLM) in commercial and research settings has intensified the need for robust intellectual property...
Binh Nguyen, Thai Le
Audio Language Models (ALMs) offer a promising shift towards explainable audio deepfake detections (ADDs), moving beyond \textit{black-box}...
Xiao Lin, Philip Li, Zhichen Zeng +6 more
Despite rich safety alignment strategies, large language models (LLMs) remain highly susceptible to jailbreak attacks, which compromise safety...
Zejian Chen, Chaozhuo Li, Chao Li +3 more
This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models (LLMs) and Vision-Language Models (VLMs),...
Shidong Cao, Hongzhan Lin, Yuxuan Gu +2 more
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias...
Di Wu, Yanyan Zhao, Xin Lu +2 more
Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve...
Rasmus Erlemann, Charles Colyer Morris, Sanjyot Sathe
The emergence of large-scale quantum computing threatens widely deployed public-key cryptographic systems, creating an urgent need for...
Zhakshylyk Nurlanov, Frank R. Schmidt, Florian Bernard
As Large Language Models (LLMs) are increasingly deployed in safety-critical domains, rigorously evaluating their robustness against adversarial...
Xiangdong Hu, Yangyang Jiang, Qin Hu +1 more
Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous...
Sofie Goethals, Foster Provost, João Sedoc
As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their...
Xiangzhe Yuan, Zhenhao Zhang, Haoming Tang +1 more
As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that...
Nick Pepper, Adam Keane, Amy Hodgkin +11 more
This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital...
Bocheng Chen, Xi Chen, Han Zi +5 more
Identifying specific moral errors in an input and generating appropriate corrections require moral sensitivity in large language models (LLMs), which...
Xi Wang, Songlei Jian, Shasha Li +5 more
Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a...
Chengcheng Feng, Haojie Yin, Yucheng Jin +1 more
Comic-based visual question answering (CVQA) poses distinct challenges to multimodal large language models (MLLMs) due to its reliance on symbolic...
Yuetian Chen, Yuntao Du, Kaiyuan Zhang +4 more
Most membership inference attacks (MIAs) against Large Language Models (LLMs) rely on global signals, like average loss, to identify training data....
Gaurav Sarraf, Vibhor Pal
Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious...
Dinghong Song, Zhiwei Xu, Hai Wan +3 more
Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe...
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