AI Threat Alert indexes 3,082+ 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 1921–1940 of 3,082 papers
Alessandro Bellina, Giordano De Marzo, David Garcia
As AI agents increasingly operate in multi-agent environments, understanding their collective behavior becomes critical for predicting the dynamics...
Badhan Chandra Das, Md Tasnim Jawad, Joaquin Molto +2 more
In recent years, the security vulnerabilities of Multi-modal Large Language Models (MLLMs) have become a serious concern in the Generative Artificial...
Keerthi Kumar. M, Swarun Kumar Joginpelly, Sunil Khemka +2 more
Background: Cyber-attacks have evolved rapidly in recent years, many individuals and business owners have been affected by cyber-attacks in various...
Ilmo Sung
Large language models suffer from "hallucinations"-logical inconsistencies induced by semantic noise. We propose that current architectures operate...
Suyash Mishra, Qiang Li, Srikanth Patil +1 more
Vision Language Models (VLMs) are poised to revolutionize the digital transformation of pharmacyceutical industry by enabling intelligent, scalable,...
Konstantinos E. Kampourakis, Vyron Kampourakis, Efstratios Chatzoglou +2 more
Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems (IDS). However,...
Mizuki Sakai, Mizuki Yokoyama, Wakaba Tateishi +1 more
Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that...
Qiang Yu, Xinran Cheng, Chuanyi Liu
As LLM agents transition from digital assistants to physical controllers in autonomous systems and robotics, they face an escalating threat from...
Seyeon Jeong, Yeonjun Choi, JongWook Kim +1 more
Large Language Models (LLMs) suffer from hallucinations and factual inaccuracies, especially in complex reasoning and fact verification tasks....
Huawei Zheng, Xinqi Jiang, Sen Yang +3 more
Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare, where they introduce unique safety...
Han Zhu, Jiale Chen, Chengkun Cai +8 more
Multi-modal Large Language Models (MLLMs) are increasingly deployed in interactive applications. However, their safety vulnerabilities become...
Hongming Fei, Zilong Hu, Prosanta Gope +1 more
Physical Unclonable Functions (PUFs) serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However,...
Zhilun Zhou, Zihan Liu, Jiahe Liu +5 more
Large Language Model-based Multi-Agent Systems (LLM-based MAS), where multiple LLM agents collaborate to solve complex tasks, have shown impressive...
Zhiyuan Chang, Mingyang Li, Yuekai Huang +6 more
Large language model (LLM)-integrated applications have become increasingly prevalent, yet face critical security vulnerabilities from prompt...
Hoagy Cunningham, Jerry Wei, Zihan Wang +26 more
We introduce enhanced Constitutional Classifiers that deliver production-grade jailbreak robustness with dramatically reduced computational costs and...
Saad Alqithami
Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step...
Yunhao Feng, Yige Li, Yutao Wu +6 more
Large language model (LLM) agents execute tasks through multi-step workflows that combine planning, memory, and tool use. While this design enables...
Mohamed Nabeel, Oleksii Starov
According to Gartner, more than 70% of organizations will have integrated AI models into their workflows by the end of 2025. In order to reduce cost...
Sahaya Jestus Lazer, Kshitiz Aryal, Maanak Gupta +1 more
Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over...
Jacob Ede Levine, Yun Lyan Luo, Sai Chandra Kosaraju
The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient...
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 3,082+ 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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