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 281–300 of 407 papers
Clear filtersYueqiao Jin, Roberto Martinez-Maldonado, Dragan Gašević +1 more
Generative AI is increasingly embedded in collaborative learning, yet little is known about how AI personas shape learner agency when AI teammates...
Nenad Tomašev, Matija Franklin, Julian Jacobs +2 more
AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an...
Himanshu Gharat, Himanshi Agrawal, Gourab K. Patro
Large Language Models (LLMs) have empowered AI agents with advanced capabilities for understanding, reasoning, and interacting across diverse tasks....
Hao Li, Yubing Ren, Yanan Cao +3 more
Benefiting from the superior capabilities of large language models in natural language understanding and generation, Embeddings-as-a-Service (EaaS)...
Vahideh Zolfaghari
Large language models (LLMs) are increasingly consulted by parents for pediatric guidance, yet their safety under real-world adversarial pressures is...
Nnamdi Philip Okonkwo, Lubna Luxmi Dhirani
Cloud Security Operations Center (SOC) enable cloud governance, risk and compliance by providing insights visibility and control. Cloud SOC triages...
Teodor Poncu, Ioana Pintilie, Marius Dragoi +2 more
Large Language Models (LLMs) typically excel at coding tasks involving high-level programming languages, as opposed to lower-level programming...
Perry Abdulkadir
Large language models (LLMs) are increasingly deployed behind safety guardrails such as system prompts and content filters, especially in settings...
Dang-Khoa Nguyen, Gia-Thang Ho, Quang-Minh Pham +5 more
Software supply chain attacks targeting the npm ecosystem have become increasingly sophisticated, leveraging obfuscation and complex logic to evade...
Andrew Adiletta, Kathryn Adiletta, Kemal Derya +1 more
The rapid deployment of Large Language Models (LLMs) has created an urgent need for enhanced security and privacy measures in Machine Learning (ML)....
Manon Kempermann, Sai Suresh Macharla Vasu, Mahalakshmi Raveenthiran +2 more
Safety evaluations of large language models (LLMs) typically focus on universal risks like dangerous capabilities or undesirable propensities....
Najmul Hasan, Prashanth BusiReddyGari, Haitao Zhao +3 more
Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language...
Sohely Jahan, Ruimin Sun
As medical large language models (LLMs) become increasingly integrated into clinical workflows, concerns around alignment robustness, and safety are...
Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Underground mining operations depend on sensor networks to monitor critical parameters such as temperature, gas concentration, and miner movement,...
Dyna Soumhane Ouchebara, Stéphane Dupont
The significant increase in software production, driven by the acceleration of development cycles over the past two decades, has led to a steady rise...
Zikai Mao, Lingchen Zhao, Lei Xu +4 more
On-device machine learning (ML) introduces new security concerns about model privacy. Storing valuable trained ML models on user devices exposes them...
Wenjie Zhang, Yun Lin, Chun Fung Amos Kwok +5 more
Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing...
Xiaoqi Li, Hailu Kuang, Wenkai Li +2 more
Traditional approaches for smart contract analysis often rely on intermediate representations such as abstract syntax trees, control-flow graphs, or...
Jehyeok Yeon, Federico Cinus, Yifan Wu +1 more
Large language models (LLMs) face critical safety challenges, as they can be manipulated to generate harmful content through adversarial prompts and...
Sheng Liu, Panos Papadimitratos
Federated Learning (FL) has drawn the attention of the Intelligent Transportation Systems (ITS) community. FL can train various models for ITS tasks,...
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