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 1–6 of 6 papers
Clear filtersZhezhen Cao, Weiyuan Ding, Bowen Xu
Static application security testing (SAST) tools such as CodeQL serve as security gates in continuous integration and code review, and a gate is also...
Zhuyi Zhao, Shengjie Yuan, Shengyu Wang +4 more
Single-pass scoring models such as Jev give each code function a vulnerability probability, so that security teams can rank many functions for review...
Zhuyi Zhao, Shengjie Yuan, Shengyu Wang +4 more
Single-pass scoring models such as Jev give each code function a vulnerability probability, so that security teams can rank many functions for review...
Frank Breitinger, Jan Gruber, Christopher Hargreaves +3 more
Large language models (LLMs) are entering digital forensics as examination assistants and autonomous agents. Digital forensics inverts the usual...
Gadi Evron
LLM-based vulnerability discovery harnesses can miss real, reachable vulnerabilities because of how the harness around the model is built. Steps that...
Nishant Abhishek, Ping Charoenwet, Kla Tantithamthavorn
Finding vulnerabilities early, before they can affect the production system, is important for reducing the risk of any software attacks. However,...
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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AI Threat Alert