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 501–520 of 521 papers
Clear filtersZhaoyan Wang, Zheng Gao, Arogya Kharel +1 more
Graph Neural Networks (GNNs) are widely adopted in Web-related applications, serving as a core technique for learning from graph-structured data,...
Luoxi Tang, Yuqiao Meng, Ankita Patra +3 more
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs...
Luca Cotti, Idilio Drago, Anisa Rula +2 more
System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of...
Yicheng Lang, Yihua Zhang, Chongyu Fan +3 more
Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving...
Andrew Gan, Zahra Ghodsi
Machine learning systems increasingly rely on open-source artifacts such as datasets and models that are created or hosted by other parties. The...
Ehsan Aghaei, Sarthak Jain, Prashanth Arun +1 more
Effective analysis of cybersecurity and threat intelligence data demands language models that can interpret specialized terminology, complex document...
Matheus Vinicius da Silva de Oliveira, Jonathan de Andrade Silva, Awdren de Lima Fontao
Large Language Models (LLMs) are widely used across multiple domains but continue to raise concerns regarding security and fairness. Beyond known...
Su Kara, Fazle Faisal, Suman Nath
Recent advances in browser-based LLM agents have shown promise for automating tasks ranging from simple form filling to hotel booking or online...
Yihan Wu, Ruibo Chen, Georgios Milis +1 more
As large language models become increasingly capable and widely deployed, verifying the provenance of machine-generated content is critical to...
Meet Udeshi, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy +4 more
Security of software supply chains is necessary to ensure that software updates do not contain maliciously injected code or introduce vulnerabilities...
Shuyi Lin, Tian Lu, Zikai Wang +3 more
OpenAI's GPT-OSS family provides open-weight language models with explicit chain-of-thought (CoT) reasoning and a Harmony prompt format. We summarize...
Sihan Hu, Xiansheng Cai, Yuan Huang +5 more
Training large language models with Reinforcement Learning with Verifiable Rewards (RLVR) exhibits a set of distinctive and puzzling behaviors that...
Sherif Saad, Kevin Shi, Mohammed Mamun +1 more
Automated machine learning (AutoML) has emerged as a promising paradigm for automating machine learning (ML) pipeline design, broadening AI adoption....
Xiaotian Zou
Multimodal Large Language Models (MLLMs) have transformed text-to-image workflows, allowing designers to create novel visual concepts with...
Antreas Ioannou, Andreas Shiamishis, Nora Hollenstein +1 more
In an era dominated by Large Language Models (LLMs), understanding their capabilities and limitations, especially in high-stakes fields like law, is...
Nakyeong Yang, Dong-Kyum Kim, Jea Kwon +3 more
Large language models trained on web-scale data can memorize private or sensitive knowledge, raising significant privacy risks. Although some...
Haochen Gong, Chenxiao Li, Rui Chang +1 more
Large language model (LLM)-based computer-use agents represent a convergence of AI and OS capabilities, enabling natural language to control system-...
Jiayu Ding, Xinpeng Liu, Zhiyi Pan +2 more
Lifting 2D open-vocabulary understanding into 3D Gaussian Splatting (3DGS) scenes is a critical challenge. However, mainstream methods suffer from...
Wenhan Wu, Zheyuan Liu, Chongyang Gao +2 more
Current LLM unlearning methods face a critical security vulnerability that undermines their fundamental purpose: while they appear to successfully...
Lauren Deason, Adam Bali, Ciprian Bejean +20 more
Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an...
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