Intent-Aware Protection for AI Agents
Fady Copty, Neta Haiby, Max Braitmaiere +1 more
AI agents increasingly access enterprise data, invoke powerful tools, and act with limited human oversight. Existing security defenses primarily...
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 filtersFady Copty, Neta Haiby, Max Braitmaiere +1 more
AI agents increasingly access enterprise data, invoke powerful tools, and act with limited human oversight. Existing security defenses primarily...
Jacy Reese Anthis, Ken Burke, David Madras +5 more
AI is being deployed across a vast range of application domains, cultural environments, and human stakeholders. Safe and responsible AI development...
Qianxue Xia
Physics-based safety gates for large language model (LLM) agents can approve unsafe actions when the agent and gate rely on the same corrupted...
Oshando Johnson, Ranjith Krishnamurthy, Rohith Kumar Shanmuganathan +2 more
Static application security testing (SAST) tools surface vulnerabilities early and their verdicts are rule-based and auditable. Yet developers...
Kiavash Fathi, Hans Wernher van de Venn
One of the fundamental needs for reaching resilient production infrastructure is flexible manufacturing, which requires translating changing...
Yiming Sun, Mi Zhang
Advances in spatial understanding have enabled frontier models to power coding agents that control a real robot arm zero-shot and end-to-end, without...
Riya Bharathwaj, Akshara Prabhakar, Roshan Ram +4 more
As enterprises delegate consequential work to multi-agent LLM systems, benign-looking requests can escalate into unsafe actions as they propagate...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial