2,104+ academic papers on AI security, attacks, and defenses
Showing 661–680 of 2,104 papers
Shaowei Shen, Xiaohong Yang, Jie Yang +4 more
Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for...
Shaowei Shen, Xiaohong Yang, Jie Yang +4 more
Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for...
Terry Yue Zhuo, Yangruibo Ding, Wenbo Guo +1 more
For over a decade, cybersecurity has relied on human labor scarcity to limit attackers to high-value targets manually or generic automated attacks at...
Chengran Yang, Zichao Wei, Heminghao Deng +6 more
Large Language Model (LLM) based code generation is predominantly formulated as a strictly monotonic process, appending tokens linearly to an...
Abhilekh Borah, Shubhra Ghosh, Kedar Joshi +2 more
Tasks such as solving arithmetic equations, evaluating truth tables, and completing syllogisms are handled well by large language models (LLMs) in...
Xinyi Hou, Shenao Wang, Yifan Zhang +4 more
Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that...
Kaiyuan Cui, Yige Li, Yutao Wu +4 more
Vision-language models (VLMs) extend large language models (LLMs) with vision encoders, enabling text generation conditioned on both images and text....
Weizhe Tang, Junwei You, Jiaxi Liu +5 more
End-to-end autonomous driving models increasingly benefit from large vision--language models for semantic understanding, yet ensuring safe and...
Xueyi Li, Zhuoneng Zhou, Zitao Liu +2 more
Large language models (LLMs) have demonstrated remarkable potential for automatic short answer grading (ASAG), significantly boosting student...
Srinivas Rao Marri
The proliferation of AI-assisted "vibe coding" enables rapid software development but introduces significant security risks, as Large Language Models...
Manveer Singh Tamber, Hosna Oyarhoseini, Jimmy Lin
Research on adversarial robustness in language models is currently fragmented across applications and attacks, obscuring shared vulnerabilities. In...
Licheng Pan, Yunsheng Lu, Jiexi Liu +5 more
Uncovering the mechanisms behind "jailbreaks" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these...
Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém +2 more
The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated...
Md Jahedur Rahman, Ihsen Alouani
Large language models (LLMs) are increasingly used in interactive and retrieval-augmented systems, but they remain vulnerable to task drift;...
Hao Gu, Mao-Lin Luo, Zi-Hao Zhou +3 more
Parameter-efficient continual learning aims to adapt pre-trained models to sequential tasks without forgetting previously acquired knowledge. Most...
Ranjith Krishnamurthy, Oshando Johnson, Goran Piskachev +1 more
Security vulnerabilities often arise unintentionally during development due to a lack of security expertise and code complexity. Traditional tools,...
Yuxuan Lu, Yongkang Guo, Yuqing Kong
Safety alignment in Large Language Models (LLMs) often creates a systematic discrepancy between a model's aligned output and the underlying...
Haoran Ou, Kangjie Chen, Gelei Deng +4 more
Fact-checking systems with search-enabled large language models (LLMs) have shown strong potential for verifying claims by dynamically retrieving...
Naen Xu, Hengyu An, Shuo Shi +7 more
Recent advancements in large language models (LLMs) have significantly enhanced the capabilities of collaborative multi-agent systems, enabling them...
Yihang Chen, Zhao Xu, Youyuan Jiang +2 more
Large Vision-Language Models (LVLMs) are increasingly equipped with robust safety safeguards to prevent responses to harmful or disallowed prompts....
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