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 21–40 of 152 papers
Clear filtersPadmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim +2 more
The growing complexity of cyber-physical attack surfaces in advanced manufacturing has made cyber threat intelligence analysis increasingly...
Pingyu Wu, Weiming Zhang, Nenghai Yu
Safeguards in deployed LLM services are evaluated by refusal, attack success, and policy violation rates. Those rates characterize how a control...
Panduranga Sai Varma Dantuluri, Jyotirmoy Sundi
Autonomous LLM agents increasingly act on a user's behalf: they hold credentials, call tools and services, and spawn sub-agents that act further on...
Shiqian Zhao, Yangfan Zhou, Xinfeng Li +5 more
Recently, large language model (LLM) agents, such as Codex, Claude Code, and OpenClaw, have become capable of planning and executing long-horizon...
Lifei Liu, Haoran Yu
Multi-agent applications delegate work across independently operated deployers. After an incident, a verifier must answer two questions: which...
Yunseok Lee, Yunji Kim, Woojin Lee
As LLM agents take real-world actions through tools, indirect prompt injection (IPI) has emerged as a serious threat. The standard metric, Attack...
Chuanchao Zang, Jianing Wang, Wenyu Chen +6 more
LLM agents commonly use knowledge-based tools and access their underlying files, databases, and search indexes through tool invocation. This...
Tanzim Ahad, Ismail Hossain, Md Jahangir Alam +3 more
The key limitation of current state-of-the-art influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized...
Jiayi Zhang, Zexin Wang, Degang Sun +4 more
Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable...
Alistair Reid, Simon O'Callaghan, Dustin Venini +2 more
This report presents a framework to help organisations, policymakers and researchers reason about the risks that emerge when AI agents interact with...
Tongyan Hu, Bryan Hooi
Large language models (LLMs) remain vulnerable to jailbreak attacks that exploit techniques such as role-playing, obfuscation, code transformation,...
XiuYu Zhang, Bonan Ruan, Junfeng Fang +3 more
Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them. Interpretability methods...
Dimitri Galli, Andrea Venturi, Dario Stabili +2 more
Graph Neural Networks (GNNs) represent a promising solution for Machine Learning (ML) based Network Intrusion Detection Systems (NIDS), thanks to...
Avital Aviv, Parth A. Gandh, Ron Bitton +1 more
The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments...
Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani +2 more
Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training...
Hanling Tian, Gengyu Zhang, Zeyang Sha +5 more
Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a...
Basavesh Ammanaghatta Shivakumar, Swarn Priya, Peng Gao
LLM agents increasingly read untrusted content, invoke external tools, access private data, and delegate work to other agents. Harm often arises not...
Sibo Liu
A personal language agent learns a fact from one audience and may later place it in the prompt it assembles for another. This memory-to-context step...
Xabier Muruaga
LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents. At session start, the agent's permissions are...
Zixing Chen, Xingyuan Liu, Jie Zhu +6 more
Large language model (LLM) agents combine language-based reasoning with external tools to perform complex tasks. Adversarial inputs can exploit...
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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AI Threat Alert