AI Threat Alert indexes 3,055+ 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 2161–2180 of 3,055 papers
Reachal Wang, Yuqi Jia, Neil Zhenqiang Gong
Prompt injection attacks aim to contaminate the input data of an LLM to mislead it into completing an attacker-chosen task instead of the intended...
Bethel Hall, Owen Ungaro, William Eiers
Access control policies are vital for securing modern cloud computing, where organizations must manage access to sensitive data across thousands of...
Miranda Christ, Noah Golowich, Sam Gunn +2 more
Watermarks are an essential tool for identifying AI-generated content. Recently, Christ and Gunn (CRYPTO '24) introduced pseudorandom...
Joshua Ward, Bochao Gu, Chi-Hua Wang +1 more
Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice, two...
Anantaa Kotal, Anupam Joshi
The widespread use of big data across sectors has raised major privacy concerns, especially when sensitive information is shared or analyzed....
Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Underground mining operations depend on sensor networks to monitor critical parameters such as temperature, gas concentration, and miner movement,...
Shuyue Hu, Haoyang Yan, Yiqun Zhang +3 more
Foundation models (FMs) are increasingly assuming the role of the ''brain'' of AI agents. While recent efforts have begun to equip FMs with native...
Botao 'Amber' Hu, Bangdao Chen
The emerging "agentic web" envisions large populations of autonomous agents coordinating, transacting, and delegating across open networks. Yet many...
Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros +1 more
Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across...
Dyna Soumhane Ouchebara, Stéphane Dupont
The significant increase in software production, driven by the acceleration of development cycles over the past two decades, has led to a steady rise...
Gary Ackerman, Zachary Kallenborn, Anna Wetzel +7 more
The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism...
Yinan Zhong, Qianhao Miao, Yanjiao Chen +3 more
Large Language Models (LLMs) have been integrated into many applications (e.g., web agents) to perform more sophisticated tasks. However,...
Xiaoqi Li, Lei Xie, Wenkai Li +1 more
In the case of upgrading smart contracts on blockchain systems, it is essential to consider the continuity of upgrades and subsequent maintenance. In...
Shiva Gaire, Srijan Gyawali, Saroj Mishra +3 more
The Model Context Protocol (MCP) has emerged as the de facto standard for connecting Large Language Models (LLMs) to external data and tools,...
Tailun Chen, Yu He, Yan Wang +9 more
Retrieval-Augmented Generation (RAG) systems enhance LLMs with external knowledge but introduce a critical attack surface: corpus poisoning. While...
Zafaryab Haider, Md Hafizur Rahman, Shane Moeykens +2 more
Hard-to-detect hardware bit flips, from either malicious circuitry or bugs, have already been shown to make transformers vulnerable in non-generative...
Md Nazmul Haque, Elizabeth Lin, Lawrence Arkoh +2 more
Large Language Models for code (LLMs4Code) are increasingly used to generate software artifacts, including library and package recommendations in...
Jinghao Wang, Ping Zhang, Carter Yagemann
Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of...
Sampriti Soor, Suklav Ghosh, Arijit Sur
Language models are vulnerable to short adversarial suffixes that can reliably alter predictions. Previous works usually find such suffixes with...
Stephan Carney, Soham Hans, Sofia Hirschmann +4 more
Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the...
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 3,055+ 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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