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 1–7 of 7 papers
Clear filtersTiansheng Huang, Ananth Balashankar, Virat Shejwalkar +2 more
We expose a safety vulnerability on nascent agentic reinforcement-learning-as-a-service. Specifically, with a cost as low as \$20, we show that...
Weimin Fu, Minghao Shao, Zeng Wang +7 more
LLM agents apply approval settings, such as whether to ask before a consequential tool call, through instructions in their system prompt, and...
Vicenç Torra, Maria Bras-Amorós
Multi-agent systems provide a convenient framework for the massive deployment of agents based on Large Language Models (LLMs). It provides a sound...
Mohammed Qaiss, Peter Horvath, Michal Shagam +5 more
Can power-analysis techniques reveal secret weights of machine-learning models? Multiple recent works demonstrate proof-of-concept attacks that can...
Jingfen Qiao, Rui Li, Shuaiqun Pan +3 more
LLM agents increasingly retrieve third-party skills from public registries and follow their procedures to complete user tasks. This reliance allows...
Enhao Huang, Chunshu Xia, Yiming Li +5 more
Agent skills package reusable workflows and task-specific procedures into deployable natural-language artifacts for LLM agents. Their scale and...
Doron Shavit
LLM agents that call tools are exposed to indirect prompt injection: instructions hidden in tool outputs can steer an agent into calls the user never...
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