AI Threat Alert indexes 3,771+ 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 3181–3200 of 3,771 papers
Hanlin Cai, Houtianfu Wang, Haofan Dong +3 more
Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and...
Marcin Podhajski, Jan Dubiński, Franziska Boenisch +3 more
Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in...
Liang Shan, Kaicheng Shen, Wen Wu +7 more
Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address...
Zhisheng Zhang, Derui Wang, Yifan Mi +6 more
Recent advancements in speech synthesis technology have enriched our daily lives, with high-quality and human-like audio widely adopted across...
Hui Lu, Yi Yu, Song Xia +5 more
Large-scale Video Foundation Models (VFMs) has significantly advanced various video-related tasks, either through task-specific models or Multi-modal...
Yuanheng Li, Zhuoyang Chen, Xiaoyun Liu +5 more
As large language models (LLMs) become increasingly capable, concerns over the unauthorized use of copyrighted and licensed content in their training...
Yilin Jiang, Mingzi Zhang, Xuanyu Yin +5 more
Large Language Models for Simulating Professions (SP-LLMs), particularly as teachers, are pivotal for personalized education. However, ensuring their...
Peng Zhang, Peijie Sun
Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal...
Nicy Scaria, Silvester John Joseph Kennedy, Deepak Subramani
Small Language Models (SLMs) are increasingly being deployed in resource-constrained environments, yet their behavioral robustness to data...
Zhi Rui Tam, Yun-Nung Chen
As large language models transition from text-based interfaces to audio interactions in clinical settings, they might introduce new vulnerabilities...
Haonan Shi, Guoli Wang, Tu Ouyang +1 more
Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow...
Dev Patel, Gabrielle Gervacio, Diekola Raimi +5 more
Large Language Models require substantial computational resources for inference, posing deployment challenges. While dynamic pruning offers superior...
Reem Al-Saidi, Erman Ayday, Ziad Kobti
This study investigates embedding reconstruction attacks in large language models (LLMs) applied to genomic sequences, with a specific focus on how...
Shuyuan Liu, Jiawei Chen, Xiao Yang +2 more
With the widespread application of large language models (LLMs) in various fields, the security challenges they face have become increasingly...
Dachuan Lin, Guobin Shen, Zihao Yang +3 more
Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at...
Azanzi Jiomekong, Jean Bikim, Patricia Negoue +1 more
Evaluating semantic tables interpretation (STI) systems, (particularly, those based on Large Language Models- LLMs) especially in domain-specific...
Seif Ikbarieh, Kshitiz Aryal, Maanak Gupta
The rapid expansion of the Internet of Things (IoT) is reshaping communication and operational practices across industries, but it also broadens the...
Dilli Prasad Sharma, Liang Xue, Xiaowei Sun +2 more
The rapid proliferation of Internet of Things (IoT) devices has transformed numerous industries by enabling seamless connectivity and data-driven...
Alina Fastowski, Bardh Prenkaj, Yuxiao Li +1 more
LLMs are now an integral part of information retrieval. As such, their role as question answering chatbots raises significant concerns due to their...
Jiayi Fu, Yuansen Zhang, Yinggui Wang
Large Language Models (LLMs) demonstrate strong capabilities in solving complex tasks when integrated with external tools. The Model Context Protocol...
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 3,771+ 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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