AI Threat Alert indexes 3,371+ 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 21–40 of 117 papers
Clear filtersMohammadreza Hallajiyan, Xueren Ge, Athish Pranav Dharmalingam +4 more
The growing integration of artificial intelligence (AI) and machine learning (ML) in medical systems requires effective measures to address emerging...
Faruk Alpay, Taylan Alpay
LLM agents process trusted instructions, retrieved records, and tool observations through a common generative channel. This conflates data flow with...
Bingyu Yan, Xiaoming Zhang, Jinyu Hou +4 more
While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating...
Ze Sheng, Zhicheng Chen, Qingxiao Xu +2 more
Software vulnerabilities pose critical security threats, with nearly 50,000 CVEs reported in 2025. While Large Language Models (LLMs) show promise...
Tim Van hamme, Thomas Vissers, Javier Carnerero-Cano +4 more
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different...
Sultan Zavrak
The Model Context Protocol (MCP) has become a widely adopted interface for LLM agents to invoke external tools, yet learned monitoring of MCP...
Zhaorun Chen, Xun Liu, Haibo Tong +14 more
AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due...
Haoyu Zhang, Mohammad Zandsalimy, Shanu Sushmita
Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We...
Weiyi Kong, Ahmad Mohammad Saber, Amr Youssef +1 more
In modern energy systems, industrial control systems (ICS) and power-system SCADA require intrusion detection that is not only accurate but also...
Yuchuan Zhao, Tong Chen, Junliang Yu +3 more
Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance, enabling recommendations...
Run Hao, Zhuoran Tan
Model Context Protocol (MCP) is increasingly adopted for tool-integrated LLM agents, but its multi-layer design and third-party server ecosystem...
Jiamin Chang, Minhui Xue, Ruoxi Sun +3 more
Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive...
Jiacheng Liang, Yao Ma, Tharindu Kumarage +5 more
Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an...
Wei Zhao, Zhe Li, Peixin Zhang +1 more
Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet...
Yihao Zhang, Kai Wang, Jiangrong Wu +7 more
Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security...
Vu Tuan Truong, Long Bao Le
Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks. Prior...
Zhuowen Yuan, Zhaorun Chen, Zhen Xiang +5 more
Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on...
Anubhab Sahu, Diptisha Samanta, Reza Soosahabi
System Instructions in Large Language Models (LLMs) are commonly used to enforce safety policies, define agent behavior, and protect sensitive...
Jingning Xu, Haochen Luo, Chen Liu
Vision-language models (VLMs) are vulnerable to adversarial image perturbations. Existing works based on adversarial training against task-specific...
Aengus Lynch
Autonomous AI agents are being deployed with filesystem access, email control, and multi-step planning. This thesis contributes to four open problems...
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 3,371+ 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