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 461–480 of 562 papers
Clear filtersNourin Shahin, Izzat Alsmadi
As large language models (LLMs) move from research prototypes to enterprise systems, their security vulnerabilities pose serious risks to data...
Xiangyang Zhu, Yuan Tian, Zicheng Zhang +6 more
Large vision-language models (LVLMs) exhibit remarkable capabilities in cross-modal tasks but face significant safety challenges, which undermine...
Quy-Anh Dang, Chris Ngo
Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors....
Yuxiang Wang, Hongyu Liu, Dekun Chen +2 more
As Speech Language Models (SLMs) transition from personal devices to shared, multi-user environments such as smart homes, a new challenge emerges:...
Chi Zhang, Wenxuan Ding, Jiale Liu +3 more
Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities on Visual-Question-Answering (VQA) benchmarks. However, their...
Zahra Hashemi, Zhiqiang Zhong, Jun Pang +1 more
The rapid evolution of large language models (LLMs) has fuelled enthusiasm about their role in advancing scientific discovery, with studies exploring...
Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah
Autonomous unmanned aerial vehicle (UAV) systems are increasingly deployed in safety-critical, networked environments where they must operate...
Geunsik Lim
As climate-related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective...
Krittin Pachtrachai, Petmongkon Pornpichitsuwan, Wachiravit Modecrua +1 more
Building reliable conversational AI assistants for customer-facing industries remains challenging due to noisy conversational data, fragmented...
Dezhang Kong, Zhuxi Wu, Shiqi Liu +8 more
LLM-based web agents have become increasingly popular for their utility in daily life and work. However, they exhibit critical vulnerabilities when...
Thomas Heverin
Prompt injection evaluations typically treat refusal as a stable, binary indicator of safety. This study challenges that paradigm by modeling refusal...
Xiaohui Hu, Wun Yu Chan, Yuejie Shi +5 more
Smart contract security is paramount, but identifying intricate business logic vulnerabilities remains a persistent challenge because existing...
Zelong Zheng, Jiayuan Zhou, Xing Hu +2 more
Software vulnerability management has become increasingly critical as modern systems scale in size and complexity. However, existing automated...
Alireza Salemi, Hamed Zamani
Personalization is crucial for aligning Large Language Model (LLM) outputs with individual user preferences and background knowledge....
Marton Szep, Jorge Marin Ruiz, Georgios Kaissis +4 more
Fine-tuning Large Language Models (LLMs) on sensitive datasets carries a substantial risk of unintended memorization and leakage of Personally...
Massimiliano Pronesti, Anya Belz, Yufang Hou
Recent work on reinforcement learning with verifiable rewards (RLVR) has shown that large language models (LLMs) can be substantially improved using...
Dongshen Peng, Yi Wang, Austin Schoeffler +2 more
Large language models (LLMs) show promise in clinical decision support yet risk acquiescing to patient pressure for inappropriate care. We introduce...
Khoa Nguyen, Khiem Ton, NhatHai Phan +6 more
Although boosting software development performance, large language model (LLM)-powered code generation introduces intellectual property and data...
Andres Karjus, Kais Allkivi, Silvia Maine +3 more
Large language models (LLMs) enable rapid and consistent automated evaluation of open-ended exam responses, including dimensions of content and...
Akriti Vij, Benjamin Chua, Darshini Ramiah +43 more
As frontier AI models are deployed globally, it is essential that their behaviour remains safe and reliable across diverse linguistic and cultural...
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