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 241–260 of 562 papers
Clear filtersZhuohao Yu, Zhiwei Steven Wu, Adam Block
Inference-time compute scaling has emerged as a powerful paradigm for improving language model performance on a wide range of tasks, but the question...
Jia Chengyu, AprilPyone MaungMaung, Huy H. Nguyen +2 more
Recent advances in vision-language models (VLMs) trained on web-scale image-text pairs have enabled impressive zero-shot transfer across a diverse...
Shuyao Gao, Minghao Huang
The deployment of Large Language Models (LLMs) has ignited concerns about technological unemployment. Existing task-based evaluations predominantly...
Abinitha Gourabathina, Inkit Padhi, Manish Nagireddy +2 more
For Large Language Models (LLMs) to be reliably deployed, models must effectively know when not to answer: abstain. Reasoning models, in particular,...
Jaemin Kim, Jae O Lee, Sumyeong Ahn +1 more
Retrieval-Augmented Language Models (RALMs) have demonstrated significant potential in knowledge-intensive tasks; however, they remain vulnerable to...
Matteo Migliarini, Joaquin Pereira Pizzini, Luca Moresca +3 more
Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk...
O. Clerc, R. Abdelghani, C. Desvaux +3 more
The rapid adoption of generative artificial intelligence (GenAI) in schools raises concerns about students' uncritical reliance on its outputs....
Yiheng Huang, Zhijia Zhao, Bihuan Chen +5 more
The model context protocol (MCP) standardizes how LLMs connect to external tools and data sources, enabling faster integration but introducing new...
Yukai Ma, Honglin He, Selina Song +2 more
Long-horizon navigation in complex urban environments relies heavily on continuous human operation, which leads to fatigue, reduced efficiency, and...
Weidi Luo, Xiaofei Wen, Tenghao Huang +5 more
Large language models (LLMs) are increasingly deployed for everyday tasks, including food preparation and health-related guidance. However, food...
Kıvanç Kuzey Dikici, Serdar Kara, Semih Çağlar +2 more
As Large Language Models (LLMs) for code increasingly utilize massive, often non-permissively licensed datasets, evaluating data contamination...
Yao Qin, Yangyang Yan, Jinhua Pang +1 more
The integration of Large Language Models (LLMs) into life sciences has catalyzed the development of "AI Scientists." However, translating these...
Yanting Wang, Jinyuan Jia
Random subspace method has wide security applications such as providing certified defenses against adversarial and backdoor attacks, and building...
Yubo Li, Lu Zhang, Tianchong Jiang +2 more
Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a...
Yicheng Cai, Mitchell John DeStefano, Guodong Dong +5 more
As Large Language Models (LLMs) and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations,...
Quan Zhang, Lianhang Fu, Lvsi Lian +5 more
Equipping LLM agents with real-world tools can substantially improve productivity. However, granting agents autonomy over tool use also transfers the...
Kesheng Chen, Yamin Hu, Qi Zhou +2 more
Vision-language models (VLMs) achieve strong performance on many benchmarks, yet a basic reliability question remains underexplored: when visual...
Vishal Narnaware, Animesh Gupta, Kevin Zhai +2 more
Multimodal Diffusion Large Language Models (MDLLMs) achieve high-concurrency generation through parallel masked decoding, yet the architectures...
Pei Chen, Geng Hong, Xinyi Wu +6 more
The emergence of Large Language Model-enhanced Search Engines (LLMSEs) has revolutionized information retrieval by integrating web-scale search...
Zhihui Yao, Hengran Zhang, Keping Bi
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with external knowledge but remains vulnerable to low-authority sources...
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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