2,077+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 161 papers
Clear filtersZhanguang Zhang, Zhiyuan Li, Behnam Rahmati +10 more
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting...
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera
Large language models are becoming pervasive core components in many real-world applications. As a consequence, security alignment represents a...
Rui Yang Tan, Yujia Hu, Roy Ka-Wei Lee
Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visually...
Yanming Mu, Hao Hu, Feiyang Li +7 more
Retrieval-Augmented Generation (RAG) significantly mitigates the hallucinations and domain knowledge deficiency in large language models by...
Huamin Chen, Xunzhuo Liu, Bowei He +5 more
Over the past year, the vLLM Semantic Router project has released a series of work spanning: (1) core routing mechanisms -- signal-driven routing,...
Kwanyoung Kim, Byeongsu Sim
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models with human preferences, inspiring the...
Abed K. Musaffar, Ambuj Singh, Francesco Bullo
Large language models (LLMs) are increasingly deployed in human-AI teams as support agents for complex tasks such as information retrieval,...
Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg +1 more
Frontier LLM companies have repeatedly assured courts and regulators that their models do not store copies of training data. They further rely on...
Uchi Uchibeke
AI agents today have passwords but no permission slips. They execute tool calls (fund transfers, database queries, shell commands, sub-agent...
Jiahao Chen, Zhiming Zhao, Yuwen Pu +4 more
Federated learning (FL) has attracted substantial attention in both academia and industry, yet its practical security posture remains poorly...
Hung Yun Tseng, Wuzhen Li, Blerina Gkotse +1 more
The potential of Large Language Models (LLMs) to provide harmful information remains a significant concern due to the vast breadth of illegal queries...
Christopher J. Agostino, Quan Le Thien, Nayan D'Souza +1 more
Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe,...
Vicenç Torra, Maria Bras-Amorós
Memory poisoning attacks for Agentic AI and multi-agent systems (MAS) have recently caught attention. It is partially due to the fact that Large...
Fazhong Liu, Zhuoyan Chen, Tu Lan +6 more
Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to...
Qi Luo, Minghui Xu, Dongxiao Yu +1 more
Many learning systems now use graph data in which each node also contains text, such as papers with abstracts or users with posts. Because these...
Dong-Xiao Zhang, Hu Lou, Jun-Jie Zhang +2 more
Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems, each addressed with...
Vincent Siu, Jingxuan He, Kyle Montgomery +4 more
Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security...
Shawn Li, Yue Zhao
Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete...
Carlos Hinojosa, Clemens Grange, Bernard Ghanem
Vision-language models (VLMs) are increasingly deployed in real-world and embodied settings where safety decisions depend on visual context. However,...
Zikang Ding, Junhao Li, Suling Wu +3 more
Model watermarking utilizes internal representations to protect the ownership of large language models (LLMs). However, these features inevitably...
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