AI Threat Alert indexes 4,319+ 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 221–240 of 4,319 papers
Zhiyue Xu, Xukai Wang, Zhiyuan Huang +3 more
Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled...
Mohammad Kachuee, Phu Mon Htut, Sheng Li +5 more
Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their...
Abhishek Rai
Local Retrieval-Augmented Generation (RAG) systems are vulnerable to indirect prompt injection and retrieval poisoning, where malicious instructions...
Masahiro Kaneko, Hiromi Arai, Timothy Baldwin
Large language model (LLM) agents increasingly use persistent memory to personalize responses, and personal attributes in this memory pose a privacy...
Jinhao Hu, Bardia Mohammadi, Mofasshara Binte Rafique +4 more
AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not...
Diego Miguel Lozano, Erik Derner, Juan Antonio Pérez-Ortiz +1 more
Negation remains a persistent challenge for language models across NLP tasks and modalities. We show that this weakness can undermine LLM safety...
Siyuan Li, Jiayi Zhou, Zhiyuan Xia +6 more
Code reuse in modern software ecosystems facilitates the spread of 1-day vulnerabilities, posing significant security risks. Determining if patches...
Kamolchanok Saengtong, Chutirat Saengyingyongwattana, Sasin Janpuangtong +1 more
Large language models (LLMs) can improve honeypot realism by generating dynamic, context-aware responses, but powerful cloud models incur substantial...
Romina Omidi, Nima Naderloui, Yuan Hong +1 more
Watermarking has emerged as a key tool for identifying text generated by large language models (LLMs), with current schemes embedding statistical...
Liping Yang, Yongqi Leng, Jingting Zheng +3 more
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing...
Tongrun Xing, Yongjun Dai, Xuan Liao +4 more
As cognitive radars evolve towards the end-to-end processing of one-dimensional complex baseband sequences, deep networks face severe adversarial...
Maosen Zhang, Jianshuo Dong, Yutong Zhang +3 more
Multi-agent systems are increasingly deployed to coordinate multiple large language model (LLM) agents. However, this also creates new attack...
Sadia Asif, Israel Antonio Rosales Laguan, Shumaila Asif +3 more
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative...
Vasisht Duddu, Adam Caulfield, Xi He
Regulations require companies to run AI models in a regulatory sandbox, an isolated environment that enables monitoring model behavior by regulators,...
Yixin Wu, Rachel Hong, Jamie Morgenstern +1 more
Machine learning benchmarks act as guideposts for model development and spur the creation of training datasets intended to improve benchmark scores....
Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea
Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions,...
Nishant Abhishek, Ping Charoenwet, Kla Tantithamthavorn
Finding vulnerabilities early, before they can affect the production system, is important for reducing the risk of any software attacks. However,...
Leo Elmecker-Plakolm, Lukasz Szpruch, Avishek Joey Bose +1 more
Beneath the apparent diversity of foundation models lies an increasingly homogeneous technical stack of common training pipelines, distillation...
Qi Pang, Hanshen Xiao, Andre Manoel +4 more
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently...
Rui Melo, Andre Catarino, Claudia Mamede +2 more
Large language models can detect vulnerable code, yet the form of vulnerability encoding in their representations remains unclear. We analyze 8 code...
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 4,319+ 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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert