AI Threat Alert indexes 3,371+ 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 61–80 of 876 papers
Clear filtersPushkal Kumar, Tucker Nielson, Tanish Kolhe +2 more
Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus...
Ads Dawson, Adrian Wood
Stealth, the discipline of achieving an objective without revealing your presence, capabilities, or collected intelligence, is what separates...
Conor McCauley, Zeliang Kan, Jason Martin
When a language model receives conflicting instructions from different priority levels, which one does it actually follow? This question lies at the...
Conor McCauley, Zeliang Kan, Jason Martin
When a language model receives conflicting instructions from different priority levels, which one does it actually follow? This question lies at the...
Pau Baguer, J. Xavier Salvat Lozano, Gines Garcia-Aviles +1 more
The shift to O-RAN architectures marks a turning point in cellular security, where increased openness and modularity directly translate into a...
Anwar Alajmi, Ayed Salman, Imtiaz Ahmad
The emergence of the recent outstanding capabilities of Arabic Language Models has opened doors for exposing their vulnerabilities. One of the major...
Rui Yang, Michael Fu, Kla Tantithamthavorn +2 more
Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing...
Qi Zhao, Christian Wressnegger
Recent training-time defenses against neural backdoors isolate a benign subset from poisoned training data, to learn a backdoor-free model from it....
Maria Rosaria Briglia, Igor Maljkovic, Antonio Emanuele Cinà +3 more
Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text...
Parmida Geranmayeh, Onur Günlü
In next-generation wireless networks, communication systems are expected to go beyond simple data transmission and simultaneously provide high data...
Abu Bakar Siddik
Cyber-capable AI agents combine language models with tools, memory, and execution en- vironments to perform multi-step offensive-security tasks....
Abhishek Kumar Singh, Shrey Nag, Sachita +2 more
India is a vast nation of over 1.4 billion people, varied by hundreds of diverse and locally specific traditions and cultures and 22 officially...
Genliang Zhu, Chu Wang
Tool-using agents expose structured calls but commonly attach free-form rationales. Such rationales are neither authorization nor reliable...
Haowen Dai, Zonghao Ying, Wenfeng Li +10 more
Multi-agent systems improve capability through task decomposition and role specialization, but these same mechanisms introduce an important safety...
Arseny Kravchenko, Vadim Liventsev, Innokentii Konstantinov +2 more
Autonomous LLM agents processing mixed-confidentiality data face severe security risks from prompt injection attacks and reasoning errors. While...
Diego Fernandez Arias, Dev Prashant Mistry, Ren Wang +1 more
Multi-agent LLM systems can be attacked by a payload that no single agent ever holds in full: a poisoned tool hides encrypted fragments in its...
Tong Zhang, Zexin Li, Simin Chen +1 more
Jailbreak defenses are essential for protecting large language models (LLMs), but they can also introduce secondary costs that weaken model utility....
Andrea Ponte, Daniel Gibert, Matous Kozak +5 more
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since...
Mohan Manivannan, Dalal Alharthi
Cloud telemetry arrives at a scale that, paradoxically, makes intrusion understanding harder rather than easier. Attackers operate through legitimate...
Mohammed Aldeen, Muhammad Sami Irfan, Sagar Dasgupta +3 more
Autonomous vehicles (AVs) depend on Global Navigation Satellite Systems (GNSS) for localization and navigation, making them vulnerable to spoofing...
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 3,371+ 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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