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 21–40 of 161 papers
Clear filtersMihai Christodorescu, Earlence Fernandes, Ashish Hooda +11 more
We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted...
Rohith Uppala
Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when...
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...
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...
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...
Kato Mivule
This paper extends the Classification Error Gauge (x-CEG) framework, originally developed for measuring the privacy-utility trade-off in tabular...
Mikko Lempinen, Joni Kemppainen, Niklas Raesalmi
As artificial intelligence (AI) systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of...
Yuan Fang, Yiming Luo, Aimin Zhou +1 more
Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains...
Shangkun Che, Silin Du, Ge Gao
The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes....
Hengkai Ye, Zhechang Zhang, Jinyuan Jia +1 more
Large language models (LLMs) increasingly rely on external tools to perform time-sensitive tasks and real-world actions. While tool integration...
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang +1 more
As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to...
Yinghan Hou, Zongyou Yang
OpenClaw's ClawHub marketplace hosts over 13,000 community-contributed agent skills, and between 13% and 26% of them contain security vulnerabilities...
Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar
Cyber-physical systems often contend with incomplete architectural documentation or outdated information resulting from legacy technologies,...
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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