AI Threat Alert indexes 3,371+ 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 3201–3220 of 3,371 papers
Maraz Mia, Mir Mehedi A. Pritom
Explainable Artificial Intelligence (XAI) has aided machine learning (ML) researchers with the power of scrutinizing the decisions of the black-box...
Tanqiu Jiang, Min Bai, Nikolaos Pappas +2 more
Vision-language model (VLM)-based web agents increasingly power high-stakes selection tasks like content recommendation or product ranking by...
Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu +2 more
Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models...
Fatmazohra Rezkellah, Ramzi Dakhmouche
With the increasing adoption of Large Language Models (LLMs), more customization is needed to ensure privacy-preserving and safe generation. We...
Kartik Pandit, Sourav Ganguly, Arnesh Banerjee +2 more
Ensuring safety is a foundational requirement for large language models (LLMs). Achieving an appropriate balance between enhancing the utility of...
Javad Rafiei Asl, Sidhant Narula, Mohammad Ghasemigol +2 more
Large Language Models (LLMs) have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn...
Sanket Badhe
We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses...
Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +7 more
Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens...
Xinzhe Huang, Wenjing Hu, Tianhang Zheng +5 more
Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with...
Yu He, Yifei Chen, Yiming Li +5 more
In recent years, RAG has emerged as a key paradigm for enhancing large language models (LLMs). By integrating externally retrieved information, RAG...
Léo Boisvert, Abhay Puri, Chandra Kiran Reddy Evuru +6 more
While finetuning AI agents on interaction data -- such as web browsing or tool use -- improves their capabilities, it also introduces critical...
Nikoo Naghavian, Mostafa Tavassolipour
Vision-language models like CLIP demonstrate impressive zero-shot generalization but remain highly vulnerable to adversarial attacks. In this work,...
Zhixin Xie, Xurui Song, Jun Luo
Despite substantial efforts in safety alignment, recent research indicates that Large Language Models (LLMs) remain highly susceptible to jailbreak...
Abrar Shahid, Ibteeker Mahir Ishum, AKM Tahmidul Haque +2 more
This paper presents a controlled study of adversarial reinforcement learning in network security through a custom OpenAI Gym environment that models...
Lesly Miculicich, Mihir Parmar, Hamid Palangi +4 more
The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These...
Chinthana Wimalasuriya, Spyros Tragoudas
Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect...
Bowei Ning, Xuejun Zong, Kan He
Industrial control systems (ICS) are vital to modern infrastructure but increasingly vulnerable to cybersecurity threats, particularly through...
Zhaorun Chen, Xun Liu, Mintong Kang +4 more
As vision-language models (VLMs) gain prominence, their multimodal interfaces also introduce new safety vulnerabilities, making the safety evaluation...
Chengquan Guo, Chulin Xie, Yu Yang +6 more
Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic...
Chenpei Huang, Lingfeng Yao, Hui Zhong +5 more
Ear canal scanning/sensing (ECS) has emerged as a novel biometric authentication method for mobile devices paired with wireless earbuds. Existing...
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 3,371+ 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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