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 661–680 of 748 papers
Clear filtersBaogang Song, Dongdong Zhao, Jianwen Xiang +2 more
Backdoor attacks pose a persistent security risk to deep neural networks (DNNs) due to their stealth and durability. While recent research has...
Tuan T. Nguyen, John Le, Thai T. Vu +2 more
Large language models (LLMs) achieve impressive performance across diverse tasks yet remain vulnerable to jailbreak attacks that bypass safety...
Yuqi Jia, Yupei Liu, Zedian Shao +2 more
Prompt injection attacks deceive a large language model into completing an attacker-specified task instead of its intended task by contaminating its...
Bowen Fan, Zhilin Guo, Xunkai Li +5 more
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social...
Xiaoxue Ren, Penghao Jiang, Kaixin Li +6 more
Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly...
Harsh Kasyap, Minghong Fang, Zhuqing Liu +2 more
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL...
Ting Li, Yang Yang, Yipeng Yu +3 more
Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A...
Michael Schlichtkrull
When AI agents retrieve and reason over external documents, adversaries can manipulate the data they receive to subvert their behaviour. Previous...
Vasilije Stambolic, Aritra Dhar, Lukas Cavigelli
Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminating the...
Zonghuan Xu, Jiayu Li, Yunhan Zhao +3 more
Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly...
Ming Tan, Wei Li, Hu Tao +4 more
Open-source large language models (LLMs) have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks...
Guan-Yan Yang, Tzu-Yu Cheng, Ya-Wen Teng +2 more
The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security...
Wentian Zhu, Zhen Xiang, Wei Niu +1 more
Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the...
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...
Ruizhe Zhu
The widespread application of large vision language models has significantly raised safety concerns. In this project, we investigate text prompt...
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...
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...
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...
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