AI Threat Alert indexes 3,397+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 501–520 of 975 papers
Clear filtersAashish Kolluri, Rishi Sharma, Manuel Costa +5 more
Indirect prompt injection attacks threaten AI agents that execute consequential actions, motivating deterministic system-level defenses. Such...
Yang Liu, Armstrong Foundjem, Xingfang Wu +2 more
Context: In the fast-paced evolution of software development, Large Language Models (LLMs) have become indispensable tools for tasks such as code...
Arpit Singh Gautam, Kailash Talreja, Saurabh Jha
Large Language Models (LLMs) frequently hallucinate plausible but incorrect assertions, a vulnerability often missed by uncertainty metrics when...
Zhenhua Zou, Sheng Guo, Qiuyang Zhan +6 more
The evolution of Large Language Models (LLMs) has shifted mobile computing from App-centric interactions to system-level autonomous agents. Current...
Xinguo Feng, Zhongkui Ma, Zihan Wang +2 more
Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to...
Matteo Migliarini, Berat Ercevik, Oluwagbemike Olowe +5 more
Large Language Models (LLMs) are increasingly deployed as active participants on public social media platforms, yet their behavior in these...
Yuxin Cao, Wei Song, Shangzhi Xu +2 more
Video Large Language Models (VideoLLMs) have recently achieved strong performance in video understanding tasks. However, we identify a previously...
Mohan Rajagopalan, Vinay Rao
Large Language Model (LLM) applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot...
Yilin Yang, Zhenghui Guo, Yuke Wang +3 more
Large Vision-Language Models (VLMs) have achieved remarkable success across diverse multimodal tasks but remain vulnerable to hallucinations rooted...
Adriana Alvarado Garcia, Ruyuan Wan, Ozioma C. Oguine +1 more
Recently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial...
Pei-Chi Pan, Yingbin Liang, Sen Lin
Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning...
Chaeyun Kim, YongTaek Lim, Kihyun Kim +2 more
Existing red-teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio-technical vulnerabilities rooted in...
Ashwin Sreevatsa, Sebastian Prasanna, Cody Rushing
The AI Control research agenda aims to develop control protocols: safety techniques that prevent untrusted AI systems from taking harmful actions...
Yuting Ning, Jaylen Jones, Zhehao Zhang +5 more
Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the...
Ahmed Salem, Andrew Paverd, Sahar Abdelnabi
Large language models (LLMs) are commonly treated as stateless: once an interaction ends, no information is assumed to persist unless it is...
Igor 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....
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 3,397+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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AI Threat Alert