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 21–34 of 34 papers
Clear filtersYujia Zheng, Tianhao Li, Shangzhi Xu +6 more
Agent Skills allow LLM agents to combine reusable capabilities, but checking each Skill in isolation cannot rule out policy violations when they are...
Pellegrino Casoria, Simon Pietro Romano
In the post-API landscape, social-media profiling has become inexpensive and automated: Large Language Models (LLMs) can extract structured...
Farnoosh Hashemi, Shiyu Ji, Joice Chen +1 more
Large language models (LLMs) increasingly operate in multi-agent systems where they collaborate by responding to others' choices, creating endogenous...
Edward C. Cheng, Jeshua Cheng, Alice Siu
AI agents capable of autonomous decision-making introduce significant challenges related to safety, alignment, accountability, and human oversight....
Hanhui Xu, Jiacheng Ji, Yuhan Wang +3 more
Large language models increasingly address users in the register of care: they remember personal details, adopt a first-person emotional voice, and...
Mohammad Kachuee, Phu Mon Htut, Sheng Li +5 more
Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their...
Diego Miguel Lozano, Erik Derner, Juan Antonio Pérez-Ortiz +1 more
Negation remains a persistent challenge for language models across NLP tasks and modalities. We show that this weakness can undermine LLM safety...
Kamolchanok Saengtong, Chutirat Saengyingyongwattana, Sasin Janpuangtong +1 more
Large language models (LLMs) can improve honeypot realism by generating dynamic, context-aware responses, but powerful cloud models incur substantial...
Romina Omidi, Nima Naderloui, Yuan Hong +1 more
Watermarking has emerged as a key tool for identifying text generated by large language models (LLMs), with current schemes embedding statistical...
Nanjun Zhou, Yiming Li, Yukun Chen +3 more
Tool poisoning has emerged as a critical security threat to LLM agents, where adversaries embed malicious instructions in tool descriptions or...
Kshitij Mishra, Abhijith Sharma, Nils Lukas +1 more
Modern agentic systems extend language models with external tools, enabling them to complete complex tasks through multi-turn interactions. As these...
Huaien Zhang, Zhengjie Zhao, Yu Pei +4 more
Rust has become a popular systems programming language in recent years because of its strong guarantees on memory and concurrency safety. Many types...
Huaien Zhang, Yu Pei, Hao Chen +1 more
Static application security testing (SAST) tools help detect potential software vulnerabilities, but they themselves contain faults undermining...
Ferry Afit Kurniawan, Kazushi Okamoto, Koki Karube +2 more
Cyber threat intelligence (CTI) relies on the timely identification of emerging threats, enabling organizations and security analysts to support...
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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