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 1–3 of 3 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...
Ali Tajfar, Behnam Tajfar
Interpretability tools increasingly support safety claims, such as that a sparse dictionary has recovered most of a model's features. Such claims...
Jonas Limniatis, Bennet Schaarmann, Georg Reuber +1 more
Taxonomies are structured knowledge representations that support semantic search, recommendation, product catalog organization, and...
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