AI Threat Alert indexes 3,771+ 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 161–180 of 3,771 papers
Jiahao Shi, Edward Tsien, Yifeng Di +10 more
The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing...
Yage Zhang, Xinyue Shen, Yukun Jiang +2 more
Otome games, a romance simulation genre primarily targeting female, have emerged as a major force in the global gaming market, attracting hundreds of...
Boyang Zhang, Qingxin Xiao, Lingwei Dang +1 more
Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions...
Feifei Liu, Jintao Cheng, Chi Man Vong +1 more
Training-free collaborative pipelines that integrate Vision Foundation Models such as CLIP, SAM, and DINO achieve strong open-vocabulary dense...
Weizhe Wang, Yitong Zhang, Yao Zhang +4 more
Large language model (LLM) based agents are increasingly applied to cybersecurity tasks such as vulnerability discovery and automated penetration...
Xiao Ma, Hong Shen, Hui Tian +2 more
This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine...
Branka Stojanović, Andreas Flatscher, Michael Somma
Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required...
Uday Vallabhaneni, Cassie L. Cagwin, David J. Wild
Large language model (LLM) agents are increasingly proposed as autonomous SOC analysts, but two limitations make them unreliable at enterprise scale:...
Lingyu Li, Yan Teng, Yingchun Wang +1 more
Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet...
Nivedita Singh, Alsharif Abuadbba, Yansong Gao +2 more
Large language models (LLMs) are becoming integral to web applications and browser agents, transforming online interactions while introducing new...
Alessandro Pesare, Tommaso Dolci, Katja Hose +1 more
The emergence of agentic AI and LLM-based multi-agent systems (MAS) presents unprecedented opportunities for automating complex tasks, while...
Pengxun Li, Litian Zhang, Jianwei Hou +4 more
Modern AI agent harnesses expose lifecycle hooks that bind shell commands to runtime events such as session start, tool calls, and file edits. These...
Haoyang Li, Yaxin Xiao, Qingqing Ye +2 more
Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call...
Saikat Mondal, Mamta, Deeksha Varshney +2 more
Large language models (LLMs) are increasingly used in multilingual settings, yet their safety is still evaluated primarily in English. This limits...
Jakub Reš, Petr Kaška, Martin Perešíni +2 more
Large language models (LLMs) are vulnerable to jailbreak attacks that bypass safety alignment through carefully crafted prompts. Many existing...
Xuanfa Jin, Zhijian Ma, Yongcheng Zeng +3 more
Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses...
Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek +1 more
Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its...
Syed Ghazanfar Abbas, Dongyan Xu
Large language model (LLM) security has largely focused on role-playing jailbreaks, with less attention to what happens when a user asks an LLM to...
Shrenik Jadhav, Nickalsa LaPlaca, Caleb Stone +3 more
Large language models (LLMs) are increasingly used in software engineering pipelines for code generation, where production prompts often combine...
Ahmed Abdelnaby, Mohamed Elmahallawy
Deep neural networks (DNNs) are increasingly deployed in real-world vision systems, yet their predictions can be covertly manipulated by backdoor...
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 3,771+ 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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