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 21–40 of 586 papers
Clear filtersAnwar 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...
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...
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...
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...
Ankur Singh, Jinqiu Yang, Tse-Hsun Chen
AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute code with...
Paolo Modesti, Syed Ahmed, Ioannis Sfyrakis +1 more
Security protocol verification relies on formal tools such as ProVerif and OFMC. This study evaluates whether Large Language Models (LLMs) can...
Yanyu Chen, Yue Li, Yongyi Cui +2 more
Retrieval-augmented large language models frequently face contexts that interleave useful evidence with misleading statements or instruction-like...
Or Zion Eliav, Eyal Lenga, Shir Bernstien +1 more
Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue...
Zhanhao Hu, Dennis Jacob, Xiao Huang +3 more
Large language model (LLM) agents are vulnerable to security risks, such as prompt injection attacks from untrusted context that manipulate...
Jihyun Lee, Cheol-Ho Cho, Woojin Jun +2 more
Zero-shot video moment retrieval aims to overcome the limitations of traditional approaches that require large-scale datasets annotated with text and...
Hao Wu, Haijun Wang, Shangwang Li +4 more
With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After...
Akansha Shukla, Emily Bellov, Parth Atulbhai Gandhi +2 more
Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data...
Lukas Fluri, Avital Shafran, Nicholas Carlini +5 more
Cryptanalysis - the task of finding attacks against cryptographic schemes - sits at the intersection of mathematical reasoning and cybersecurity, two...
Jie Li
Large language model (LLM) agents are starting to take on routine work in high-performance computing (HPC), including monitoring Slurm jobs,...
Devina Jain, David Hartmann, Chuan Li
LLM-based agents process external content, exposing them to prompt injection and multi-turn manipulation. Most safety benchmarks evaluate defenders...
Zhida He, Xia Hu, Baichen Le +20 more
Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace...
Yuyang Xue, Feng Chen, Zhihua Liu +4 more
Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment....
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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