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 281–300 of 319 papers
Clear filtersSidhant Narula, Javad Rafiei Asl, Mohammad Ghasemigol +2 more
Large Language Models (LLMs) remain vulnerable to multi-turn jailbreak attacks. We introduce HarmNet, a modular framework comprising ThoughtNet, a...
Zijie Xu, Minfeng Qi, Shiqing Wu +4 more
Multi-agent systems powered by large language models are advancing rapidly, yet the tension between mutual trust and security remains underexplored....
Qilin Liao, Anamika Lochab, Ruqi Zhang
Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplored...
Rishi Jha, Harold Triedman, Justin Wagle +1 more
Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system...
Yue Liu, Zhenchang Xing, Shidong Pan +1 more
In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex...
Xiaofan Li, Xing Gao
The Model Context Protocol (MCP) is an emerging open standard that enables AI-powered applications to interact with external tools through structured...
Kate Glazko, Jennifer Mankoff
Generative AI risks such as bias and lack of representation impact people who do not interact directly with GAI systems, but whose content does:...
Shiwen Ou, Yuwei Li, Lu Yu +6 more
Deep learning (DL) frameworks serve as the backbone for a wide range of artificial intelligence applications. However, bugs within DL frameworks can...
ChenYu Wu, Yi Wang, Yang Liao
Large language models (LLMs) are increasingly vulnerable to multi-turn jailbreak attacks, where adversaries iteratively elicit harmful behaviors that...
Zixuan Liu, Yi Zhao, Zhuotao Liu +4 more
Machine Learning (ML)-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by...
Edoardo Allegrini, Ananth Shreekumar, Z. Berkay Celik
Agentic AI systems, which leverage multiple autonomous agents and Large Language Models (LLMs), are increasingly used to address complex, multi-step...
Yisen Wang, Yichuan Mo, Hongjun Wang +2 more
Despite the rapid progress of neural networks, they remain highly vulnerable to adversarial examples, for which adversarial training (AT) is...
Karthik Avinash, Nikhil Pareek, Rishav Hada
The increasing deployment of Large Language Models (LLMs) across enterprise and mission-critical domains has underscored the urgent need for robust...
Caelin Kaplan, Alexander Warnecke, Neil Archibald
AI models are being increasingly integrated into real-world systems, raising significant concerns about their safety and security. Consequently, AI...
Zicheng Liu, Lige Huang, Jie Zhang +3 more
The increasing autonomy of Large Language Models (LLMs) necessitates a rigorous evaluation of their potential to aid in cyber offense. Existing...
Pengyu Zhu, Lijun Li, Yaxing Lyu +3 more
LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks...
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs...
Hyeseon An, Shinwoo Park, Suyeon Woo +1 more
The promise of LLM watermarking rests on a core assumption that a specific watermark proves authorship by a specific model. We demonstrate that this...
Qizhou Peng, Yang Zheng, Yu Wen +2 more
Reinforcement learning (RL) has been an important machine learning paradigm for solving long-horizon sequential decision-making problems under...
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...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert