2,583+ academic papers on AI security, attacks, and defenses
Showing 481–500 of 927 papers
Clear filtersShadman 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....
Shayan Ali Hassan, Tao Ni, Zafar Ayyub Qazi +1 more
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. However, these...
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...
Zhiyu Sun, Minrui Luo, Yu Wang +2 more
Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information....
Ruoyao Wen, Hao Li, Chaowei Xiao +1 more
Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft....
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...
Yunbei Zhang, Kai Mei, Ming Liu +5 more
We present the first large-scale empirical study of Moltbook, an AI-only social platform where 27,269 agents produced 137,485 posts and 345,580...
Juefei Pu, Xingyu Li, Zhengchuan Liang +5 more
Autonomous large language model (LLM) based systems have recently shown promising results across a range of cybersecurity tasks. However, there is no...
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...
Saad Hossain, Tom Tseng, Punya Syon Pandey +8 more
As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications,...
Chen Chen, Yuchen Sun, Jiaxin Gao +4 more
Large language models (LLMs) are increasingly deployed in security-sensitive applications, yet remain vulnerable to backdoor attacks. However,...
Fengpeng Li, Kemou Li, Qizhou Wang +2 more
Concept erasure helps stop diffusion models (DMs) from generating harmful content; but current methods face robustness retention trade off....
Yunlong Lyu, Yixuan Tang, Peng Chen +4 more
Modern AI-integrated IDEs are shifting from passive code completion to proactive Next Edit Suggestions (NES). Unlike traditional autocompletion, NES...
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...
Hema Karnam Surendrababu, Nithin Nagaraj
Machine Learning (ML) models, including Large Language Models (LLMs), are characterized by a range of system-level attributes such as security and...
Guowei Guan, Yurong Hao, Jiaming Zhang +6 more
Multimodal large language models (MLLMs) are pushing recommender systems (RecSys) toward content-grounded retrieval and ranking via cross-modal...
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...
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