2,583+ academic papers on AI security, attacks, and defenses
Showing 281–300 of 740 papers
Clear filtersIgor Santos-Grueiro
Safety evaluation for advanced AI systems assumes that behavior observed under evaluation predicts behavior in deployment. This assumption weakens...
Pouria Arefijamal, Mahdi Ahmadlou, Bardia Safaei +1 more
Federated learning (FL) is a decentralized learning paradigm widely adopted in resource-constrained Internet of Things (IoT) environments. These...
Yuhang Wang, Feiming Xu, Zheng Lin +6 more
Although large language model (LLM)-based agents, exemplified by OpenClaw, are increasingly evolving from task-oriented systems into personalized AI...
Liwen Wang, Zongjie Li, Yuchong Xie +4 more
The evolution of Large Language Models (LLMs) into agentic systems that perform autonomous reasoning and tool use has created significant...
Shadman Rabby, Md. Hefzul Hossain Papon, Sabbir Ahmed +3 more
Sycophancy in Vision-Language Models (VLMs) refers to their tendency to align with user opinions, often at the expense of moral or factual accuracy....
Nanda Rani, Kimberly Milner, Minghao Shao +9 more
Real-world offensive security operations are inherently open-ended: attackers explore unknown attack surfaces, revise hypotheses under uncertainty,...
Jiangnan Fang, Cheng-Tse Liu, Hanieh Deilamsalehy +5 more
Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization because they...
Sai Puppala, Ismail Hossain, Md Jahangir Alam +5 more
Large language models are increasingly deployed as *deep agents* that plan, maintain persistent state, and invoke external tools, shifting safety...
Tianyi Wu, Mingzhe Du, Yue Liu +4 more
Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to...
Kunal Pai, Parth Shah, Harshil Patel
AI agents are increasingly deployed in production, yet their security evaluations remain bottlenecked by manual red-teaming or static benchmarks that...
Xiang Li, Pin-Yu Chen, Wenqi Wei
With the rapid advancement and adoption of Audio Large Language Models (ALLMs), voice agents are now being deployed in high-stakes domains such as...
Qi Sun, Ahmed Abdo, Luis Burbano +4 more
Autonomous Vehicles (AVs), especially vision-based AVs, are rapidly being deployed without human operators. As AVs operate in safety-critical...
Li Lu, Yanjie Zhao, Hongzhou Rao +2 more
Large Language Models (LLMs) have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists:...
Haoyang Hu, Zhejun Jiang, Yueming Lyu +3 more
Retrieval-augmented generation (RAG) is increasingly deployed in real-world applications, where its reference-grounded design makes outputs appear...
Yi Liu, Zhihao Chen, Yanjun Zhang +5 more
Third-party agent skills extend LLM-based agents with instruction files and executable code that run on users' machines. Skills execute with user...
Junhyeok Lee, Han Jang, Kyu Sung Choi
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows; however, prompt...
Navita Goyal, Hal Daumé
Model steering, which involves intervening on hidden representations at inference time, has emerged as a lightweight alternative to finetuning for...
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1 more
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer...
Rui Jia, Ruiyi Lan, Fengrui Liu +7 more
Large language models (LLMs) have advanced the development of personalized learning in education. However, their inherent generation mechanisms often...
Nelu D. Radpour
Contemporary benchmarks for agentic artificial intelligence (AI) frequently evaluate safety through isolated task-level accuracy thresholds,...
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