AI Threat Alert indexes 4,319+ 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 41–60 of 107 papers
Clear filtersDivyanshu 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...
Miseon Yu, Jaehoon Choi, Younghan Lee +1 more
Despite their strong ability to generate code, large language models often fail to produce secure code, as their outputs frequently contain security...
Minh Tran, Cuong Dang, Tuc Nguyen +9 more
Retrieval-Augmented Generation (RAG) enhances large language models by grounding outputs in external knowledge, improving factuality and reducing...
Isaac Kofi Nti
Machine-learning malware detectors often achieve high clean-data accuracy, but operational triage also requires evidence about uncertainty, novelty,...
Rabimba Karanjai, Yang Lu, Nour Diallo +4 more
AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify...
Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari
Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning,...
Jiale Han, Xiang Li, Jing Qian +7 more
Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their...
Johann Knechtel, Ozgur Sinanoglu, Paul V. Gratz +1 more
The semiconductor industry is undergoing a dual revolution: the shift toward heterogeneous 2.5D chiplet systems and the integration of Large Language...
Liehuang Zhu, Yuhang Li, Tianxing Wang +7 more
AI agents are evolving from isolated task executors into networked autonomous entities that can communicate, delegate tasks, invoke tools, access...
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 4,319+ 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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