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 1–8 of 8 papers
Clear filtersYang Zhou, Krish Agarwal, Zhuoming Chen +16 more
Frontier AI agents increasingly succeed on realistic, long-horizon tasks and are beginning to improve the systems through which they operate. Yet...
Sujan Maharjan, Lu Liu
Large Language Models (LLMs) have emerged as primary interfaces for factual retrieval, yet current evaluation paradigms rely almost exclusively on...
Hesham Elbakoury, Deep Shah, Ankur Sharma
Frontier large language models (LLMs) deliver the strongest response quality but carry per-token prices often more than an order of magnitude above...
Howard Zhang, Chuck Arvin, Logan Brown +3 more
Deploying LLM-based analytics agents in enterprise settings requires evaluation frameworks that can reliably detect failures across complex,...
Weifeng Yuan, Zifan Chen, Yucheng Zhang +3 more
Artificial intelligence agents increasingly automate cybersecurity tasks, including offensive operations. Frequent attacks across the web make...
Haoyi Wu, Yunkai Dang, Wanyu Zhang +2 more
Large language models (LLMs) have shown strong capabilities across diverse applications, making both safety and utility essential for reliable...
Xinyi Wu, Jiagui Chen, JiaYi Dong +5 more
Web agents extend LLMs from text generation to real-world web interaction by relying on execution harnesses that interpret user goals, perceive...
Marco Arazzi, Mert Cihangiroglu, Antonino Nocera
Swarm Learning improves resilience and removes the need for a single trusted coordinator by distributing model aggregation and validation across...
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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