AI Threat Alert indexes 3,023+ 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 41–60 of 218 papers
Clear filtersRohith Uppala
Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when...
Lecheng Yan, Ruizhe Li, Xicheng Han +5 more
Tool-using LLM agents increasingly rely on external tools to make consequential decisions, yet most existing agent-security benchmarks and defenses...
Chenning Li, Pan Hu, Justin Xu +9 more
We present the Agentic AI Detection and Response (ADR) system, the first large-scale, production-proven enterprise framework for securing AI agents...
Lukas Pirch, Micha Horlboge, Patrick Großmann +4 more
Autonomous agents based on large language models (LLMs) are rapidly emerging as a general-purpose technology, with recent systems such as OpenClaw...
Fanxiao Li, Jiaying Wu, Tingchao Fu +3 more
Multi-agent systems (MAS) powered by large language models (LLMs) increasingly adopt planner--executor architectures, where planners convert prompts...
Khondaker Tasnia Hoque, Toukir Ahammed
Flaky tests, which exhibit non-deterministic pass/fail behavior for the same version of code, pose significant challenges to reliable regression...
Joel Rorseth, Parke Godfrey, Lukasz Golab +2 more
This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models...
Tim Van hamme, Thomas Vissers, Javier Carnerero-Cano +4 more
LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different...
Sultan Zavrak
The Model Context Protocol (MCP) has become a widely adopted interface for LLM agents to invoke external tools, yet learned monitoring of MCP...
Yu-Hsiang Liu, Yu-Chien Tang, An-Zi Yen
Training AI agents to proactively assist humans in daily activities, from routine household tasks to urgent safety situations, requires large-scale...
Michael A. Riegler, Inga Strümke
We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory,...
Chengjie Wang, Jingzheng Wu, Xiang Ling +2 more
Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third-party...
Zhaorun Chen, Xun Liu, Haibo Tong +14 more
AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due...
Haoyu Zhang, Mohammad Zandsalimy, Shanu Sushmita
Large language models (LLMs) employ safety mechanisms to prevent harmful outputs, yet these defenses primarily rely on semantic pattern matching. We...
Kerri Prinos, Lilianne Brush, Cameron Denton +5 more
Agentic systems involved in high-stake decision-making under adversarial pressure need formal guarantees not offered by existing approaches....
Mingming Zha, Xiaofeng Wang
Autonomous LLM agents operate as long-running processes with persistent workspaces, memory files, scheduled task state, and messaging integrations....
Neha Nagaraja, Hayretdin Bahsi, Carlo R. da Cunha
As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can...
Weiyi Kong, Ahmad Mohammad Saber, Amr Youssef +1 more
In modern energy systems, industrial control systems (ICS) and power-system SCADA require intrusion detection that is not only accurate but also...
Zheng Wu, Yi Hua, Zhaoyuan Huang +8 more
The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via...
Jeffrey Wong, Antoine Creux
Create an idea, prototype it, evaluate if users like it, then learn. It is the circle of business. If AI can operate in all parts of the circle, it...
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 3,023+ 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