AI Threat Alert indexes 3,082+ 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 901–920 of 3,082 papers
Yiyang Zhang, Chaojian Yu, Ziming Hong +4 more
Multimodal pretrained models are vulnerable to backdoor attacks, yet most existing methods rely on visual or multimodal triggers, which are...
Jiaren Peng, Zeqin Li, Chang You +17 more
The rapid advancement of Large Language Models (LLMs) has created new opportunities for Automated Penetration Testing (AutoPT), spawning numerous...
Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar
Cyber-physical systems often contend with incomplete architectural documentation or outdated information resulting from legacy technologies,...
Baoshun Tong, Haoran He, Ling Pan +2 more
Vision-Language-Action (VLA) models have achieved remarkable success in robotic manipulation. However, their robustness to linguistic nuances remains...
Yanxu Mao, Peipei Liu, Tiehan Cui +3 more
With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team...
Md Shamimul Islam, Luis G. Jaimes, Ayesha S. Dina
Network Intrusion Detection Systems (NIDS) face important limitations. Signature-based methods are effective for known attack patterns, but they...
Anes Abdennebi, Nadjia Kara, Laaziz Lahlou +1 more
Modern Security Operations Centers struggle with alert fatigue, fragmented tooling, and limited cross-source event correlation. Challenges that...
Wuyang Zhang, Shichao Pei
Tool-use large language model (LLM) agents are increasingly deployed to support sensitive workflows, relying on tool calls for retrieval, external...
Purva Chiniya, Kevin Scaria, Sagar Chaturvedi
Large language models (LLMs) remain susceptible to jailbreak and direct prompt-injection attacks, yet the strongest defensive filters frequently...
Geert Trooskens, Aaron Karlsberg, Anmol Sharma +6 more
We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows...
Jiling Zhou, Aisvarya Adeseye, Seppo Virtanen +2 more
Chain-of-Thought (CoT) prompting has been used to enhance the reasoning capability of LLMs. However, its reliability in security-sensitive analytical...
Qingyang Xu, Yaling Shen, Stephanie Fong +7 more
The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions. A key...
Cheng Xu, Changhong Jin, Yingjie Niu +5 more
The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking tasks from simple classification to...
Houzhe Wang, Xiaojie Zhu, Chi Chen
With the increasing importance of data privacy and security, federated unlearning has emerged as a novel research field dedicated to ensuring that...
Zijun Wang, Haoqin Tu, Letian Zhang +11 more
OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services...
Vinod Vaikuntanathan, Or Zamir
AI agents are increasingly deployed to interact with other agents on behalf of users and organizations. We ask whether two such agents, operated by...
Kanishk Jain, Qian Yang, Shravan Nayak +3 more
Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concepts that...
Zhuohao 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...
Charafeddine Mouzouni
LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt...
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...
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 3,082+ 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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