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 2841–2860 of 3,023 papers
Peichao Lai, Jinhui Zhuang, Kexuan Zhang +6 more
Automating the conversion of UI images into web code is a critical task for front-end development and rapid prototyping. Advances in multimodal large...
Rijha Safdar, Danyail Mateen, Syed Taha Ali +1 more
Artificial Intelligence (AI) and more specifically Large Language Models (LLMs) have demonstrated exceptional progress in multiple areas including...
Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam +5 more
Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by...
Guangyu Shen, Siyuan Cheng, Xiangzhe Xu +4 more
Large Language Models (LLMs) can acquire deceptive behaviors through backdoor attacks, where the model executes prohibited actions whenever secret...
Jehyeok Yeon, Isha Chaudhary, Gagandeep Singh
Large language models (LLMs) are increasingly deployed in agentic systems where they map user intents to relevant external tools to fulfill a task. A...
Chengxiao Wang, Isha Chaudhary, Qian Hu +3 more
Large Language Models (LLMs) can produce catastrophic responses in conversational settings that pose serious risks to public safety and security....
Ardalan Aryashad, Parsa Razmara, Amin Mahjoub +3 more
Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine...
Hangting Ye, Jinmeng Li, He Zhao +4 more
Existing anomaly detection (AD) methods for tabular data usually rely on some assumptions about anomaly patterns, leading to inconsistent performance...
Arina Kharlamova, Bowei He, Chen Ma +1 more
Online services rely on CAPTCHAs as a first line of defense against automated abuse, yet recent advances in multi-modal large language models (MLLMs)...
Yulin Chen, Haoran Li, Yuan Sui +2 more
With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However,...
Bumjun Kim, Dongjae Jeon, Dueun Kim +2 more
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and...
Rabeya Amin Jhuma, Mostafa Mohaimen Akand Faisal
This study explored how in-context learning (ICL) in large language models can be disrupted by data poisoning attacks in the setting of public health...
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...
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