AI Threat Alert indexes 3,023+ 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 2741–2760 of 3,023 papers
Pengyu Zhu, Lijun Li, Yaxing Lyu +3 more
LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks...
Michael Schlichtkrull
When AI agents retrieve and reason over external documents, adversaries can manipulate the data they receive to subvert their behaviour. Previous...
Jiahao Liu, Bonan Ruan, Xianglin Yang +5 more
LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of...
Vasilije Stambolic, Aritra Dhar, Lukas Cavigelli
Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminating the...
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs...
Zhuochen Yang, Kar Wai Fok, Vrizlynn L. L. Thing
Large language models have gained widespread attention recently, but their potential security vulnerabilities, especially privacy leakage, are also...
Jian Wang, Xiaofei Xie, Qiang Hu +4 more
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has...
Hyeseon An, Shinwoo Park, Suyeon Woo +1 more
The promise of LLM watermarking rests on a core assumption that a specific watermark proves authorship by a specific model. We demonstrate that this...
Jidong Li, Lingyong Fang, Haodong Zhao +2 more
Multimodal large language models (MLLMs) have witnessed astonishing advancements in recent years. Despite these successes, MLLMs remain vulnerable to...
Qizhou Peng, Yang Zheng, Yu Wen +2 more
Reinforcement learning (RL) has been an important machine learning paradigm for solving long-horizon sequential decision-making problems under...
Zonghuan Xu, Jiayu Li, Yunhan Zhao +3 more
Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly...
Zaixi Zhang, Souradip Chakraborty, Amrit Singh Bedi +16 more
The rapid adoption of generative artificial intelligence (GenAI) in the biosciences is transforming biotechnology, medicine, and synthetic biology....
Tiarnaigh Downey-Webb, Olamide Jogunola, Oluwaseun Ajao
This paper presents a systematic security assessment of four prominent Large Language Models (LLMs) against diverse adversarial attack vectors. We...
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky +2 more
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which...
Mohan Zhang, Yihua Zhang, Jinghan Jia +3 more
Modern large reasoning models (LRMs) exhibit impressive multi-step problem-solving via chain-of-thought (CoT) reasoning. However, this iterative...
Ming Tan, Wei Li, Hu Tao +4 more
Open-source large language models (LLMs) have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks...
Shaolun Liu, Sina Marefat, Omar Tsai +4 more
GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover...
Shingo Kodama, Haya Diwan, Lucas Rosenblatt +2 more
The rapid spread of text generated by large language models (LLMs) makes it increasingly difficult to distinguish authentic human writing from...
Guan-Yan Yang, Tzu-Yu Cheng, Ya-Wen Teng +2 more
The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security...
Wentian Zhu, Zhen Xiang, Wei Niu +1 more
Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the...
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 3,023+ 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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