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 1–20 of 104 papers
Clear filtersMauro Conti, Lorenzo Perinello, Umberto Salviati
The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at...
Heewon Baek, Alsharif Abuadbba, Kristen Moore +2 more
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other...
Mengxiao Wang, Nitesh Saxena
Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely...
Alex Remedios, Simon Storf, Fabien Roger +1 more
To keep coding agents from going off the rails, production systems now review each proposed action with a blocking monitor that can reject it before...
Guosen Wu, Huizhen Huang, Guoxiong Long +2 more
Privacy evaluations of tool-using LLM agents often inspect a designated action, final response, or attacker report. These local proxies can miss...
Son Ho, Cédric Fournet, Jonathan Protzenko +7 more
We develop a new methodology for verifying cryptographic software. We target production code written in Rust for performance and system integration,...
Divyanshu Kumar, Rohith HN, Nitin Aravind Birur +2 more
AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare...
Victoria Lovelace, Cameron Berryman, Yuhan You +3 more
Large language model (LLM) agents are increasingly applied to penetration testing, but we still know little about what they can do or how they fail....
Divyanshu Kumar, Nitin Aravind Birur, Tanay Baswa +2 more
Agentic systems are rapidly moving to production, where they read untrusted inputs, call tools with real permissions, and act autonomously, expanding...
Md. Wasiul Haque, Sagar Dasgupta, Mizanur Rahman
Automated vehicles rely on millions of lines of safety-critical software, yet general-purpose analyzers do not understand which code can affect...
Sarah Meriem Ourari
Software supply chain security has become increasingly critical due to the widespread reliance on third-party dependencies and the growing attack...
Qikai Wang, Yongzhao Zhang, Zhiwei Chen +3 more
Skill selection is a key stage in LLM-agent workflows, determining which installed skill should handle a user request. Existing attacks on this stage...
Saastha Vasan, Hadjer Benkraouda, Jizhou Chen +8 more
Cyber Threat Intelligence (CTI) is essential for defending mission-critical infrastructure, yet the process of transforming raw attack evidence into...
Ziwei Zhao, Yu Gu, Haojun Liang +2 more
AI coding agents are evolving from solitary tools into collaborative teammates that discover and invoke one another's specialized skills. But the...
Rui Yang, Junjie Xu, Zhengyu Liu +4 more
Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating...
Feitong Qiao, Liren Peng, Shiming Ren +7 more
Frontier language models that refuse harmful single-turn prompts often comply when the same intent is reached gradually over many turns, making...
Xingbang He, Yuanwei Chen, Yi Qian +6 more
Instruction hierarchy is a model-side defense that assigns instructions different levels of privilege according to their sources. These levels...
Jinghan Zhang, Fengran Mo, Zhiyu Chen +3 more
Although Large language models (LLMs) mediate access to knowledge and computational assistance, their capabilities should benefit vulnerable groups...
Jaturong Kongmanee, Smile Thanapattheerakul
This paper proposes a framework for constructing a classifier as a safeguard layer, and for developing a complementary diagnostic that identifies...
Junchen Ding, Jialiang Dong, Yichen Zhu +5 more
The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a...
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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