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 2761–2780 of 3,023 papers
Yutao Wu, Xiao Liu, Yinghui Li +5 more
Knowledge poisoning poses a critical threat to Retrieval-Augmented Generation (RAG) systems by injecting adversarial content into knowledge bases,...
Mengyao Zhao, Kaixuan Li, Lyuye Zhang +4 more
Recent advances in Large Language Models (LLMs) have brought remarkable progress in code understanding and reasoning, creating new opportunities and...
Yue Deng, Francisco Santos, Pang-Ning Tan +1 more
Deep learning based weather forecasting (DLWF) models leverage past weather observations to generate future forecasts, supporting a wide range of...
Zonghao Ying, Yangguang Shao, Jianle Gan +9 more
Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed in...
Yuyi Huang, Runzhe Zhan, Lidia S. Chao +2 more
As large language models (LLMs) are increasingly deployed for complex reasoning tasks, Long Chain-of-Thought (Long-CoT) prompting has emerged as a...
Ruizhe Zhu
The widespread application of large vision language models has significantly raised safety concerns. In this project, we investigate text prompt...
Ines Altemir Marinas, Anastasiia Kucherenko, Alexander Sternfeld +1 more
The performance of Large Language Models (LLMs) is determined by their training data. Despite the proliferation of open-weight LLMs, access to LLM...
Mikhail Terekhov, Alexander Panfilov, Daniil Dzenhaliou +4 more
AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a...
Yongding Tao, Tian Wang, Yihong Dong +4 more
Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples...
MingSheng Li, Guangze Zhao, Sichen Liu
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal perception and generation, yet their safety alignment remains a...
Yifan Zhu, Lijia Yu, Xiao-Shan Gao
In recent years, data poisoning attacks have been increasingly designed to appear harmless and even beneficial, often with the intention of verifying...
Dennis Rall, Bernhard Bauer, Mohit Mittal +1 more
Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflows like...
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity...
Milad Nasr, Nicholas Carlini, Chawin Sitawarin +11 more
How should we evaluate the robustness of language model defenses? Current defenses against jailbreaks and prompt injections (which aim to prevent an...
Xiaonan Si, Meilin Zhu, Simeng Qin +7 more
Retrieval-augmented generation (RAG) systems enhance large language models (LLMs) with external knowledge but are vulnerable to corpus poisoning and...
Brandon Lit, Edward Crowder, Daniel Vogel +1 more
AI chatbots are an emerging security attack vector, vulnerable to threats such as prompt injection, and rogue chatbot creation. When deployed in...
Ragib Amin Nihal, Rui Wen, Kazuhiro Nakadai +1 more
Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints...
Vinay Bamil
Recent advances in large language models have enabled developers to generate software by conversing with artificial intelligence systems rather than...
Debeshee Das, Luca Beurer-Kellner, Marc Fischer +1 more
The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt...
Abhishek K. Mishra, Antoine Boutet, Lucas Magnana
Large Language Models (LLMs) are increasingly deployed across multilingual applications that handle sensitive data, yet their scale and linguistic...
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