AI Threat Alert indexes 4,319+ 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 101–120 of 927 papers
Clear filtersMohammad Kachuee, Phu Mon Htut, Sheng Li +5 more
Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their...
Jinhao Hu, Bardia Mohammadi, Mofasshara Binte Rafique +4 more
AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not...
Diego Miguel Lozano, Erik Derner, Juan Antonio Pérez-Ortiz +1 more
Negation remains a persistent challenge for language models across NLP tasks and modalities. We show that this weakness can undermine LLM safety...
Kamolchanok Saengtong, Chutirat Saengyingyongwattana, Sasin Janpuangtong +1 more
Large language models (LLMs) can improve honeypot realism by generating dynamic, context-aware responses, but powerful cloud models incur substantial...
Liping Yang, Yongqi Leng, Jingting Zheng +3 more
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing...
Sadia Asif, Israel Antonio Rosales Laguan, Shumaila Asif +3 more
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative...
Vasisht Duddu, Adam Caulfield, Xi He
Regulations require companies to run AI models in a regulatory sandbox, an isolated environment that enables monitoring model behavior by regulators,...
Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea
Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions,...
Nataša Krčo, Euodia Dodd, Peter Romov +1 more
Speech models are increasingly used as an interface to AI systems, from transcription tools to voice agents. Existing audio backdoors require an...
Walid El Maouaki, Mohammad Izen, Nouhaila Innan +2 more
Quantum machine-learning (QML) models are increasingly considered for security applications, yet their adversarial robustness in malware analysis...
Anastasiia Kucherenko, Francois Brouchoud, Dimitri Percia David +1 more
Confidentiality requirements increasingly push legal professionals toward small, on-device LLMs that keep case data in-house, used for everyday tasks...
Manohar Kaul, Sadbhavana Babar, Aditya Ramesh +4 more
Adversarial vulnerability persists across modern vision architectures from CNNs to vision language models (VLMs), yet existing detection methods rely...
Yinxiang Wang, Zehua Ma, Kejiang Chen +2 more
LLM-based guardrails are effective against indirect prompt injection (IPI), but their performance depends heavily on model capability: as the...
Yuchen Chen, Wei Cheng, Yuan Xiao +7 more
Retrieval-Augmented Generation (RAG) enables large language models to access external knowledge during inference, but its reliance on external...
Chang Zhou, Jiarun Zhou, Xingtong Yu +3 more
Generator--critic trading systems pair an LLM-based trader that makes trading decisions with a red team agent that evaluates these decisions and...
Dixi Yao, Tahseen Rabbani, Tian Li
Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it...
Kshitij Mishra, Abhijith Sharma, Nils Lukas +1 more
Modern agentic systems extend language models with external tools, enabling them to complete complex tasks through multi-turn interactions. As these...
Jiaming Zhang, Zherui Li, Siqi Guo +3 more
As vision-language models (VLMs) transition from frozen-encoder pipelines to native multimodal architectures, the visual channel increasingly serves...
Zhiyuan Wang, Yuezhi Zhou, Ziqi Zhang +3 more
On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models...
Vasileios Perifanis, Nikolaos Pavlidis
Federated learning (FL) is increasingly used to adapt a pretrained, centrally fine-tuned model to data that remain on user devices, beyond training a...
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 4,319+ 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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