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 1–20 of 2,152 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...
Tiansheng 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...
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...
Andreas Sendros, Nikolaos Pavlidis, Vasilios Perifanis +1 more
Federated learning (FL) systems increasingly rely on blockchain based reward mechanisms to incentivize participation while preserving data privacy....
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...
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...
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,...
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...
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...
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...
Julien Delaunay
"System 1" decision models (pioneered by TypeSafe AI's Jev) replace autoregressive text generation with a single forward pass returning typed...
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...
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...
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...
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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