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 341–360 of 521 papers
Clear filtersWei Qian, Chenxu Zhao, Yangyi Li +1 more
The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledgeable learning...
Wang Bin, Ao Yang, Kedan Li +5 more
In the domain of software security testing, Directed Grey-Box Fuzzing (DGF) has garnered widespread attention for its efficient target localization...
Baolei Zhang, Minghong Fang, Zhuqing Liu +5 more
Federated Learning (FL) allows multiple clients to collaboratively train a model without sharing their private data. However, FL is vulnerable to...
Saksham Sahai Srivastava, Haoyu He
Large Language Model (LLM) agents increasingly rely on long-term memory and Retrieval-Augmented Generation (RAG) to persist experiences and refine...
Adam Kaufman, James Lucassen, Tyler Tracy +2 more
Future AI agents might run autonomously with elevated privileges. If these agents are misaligned, they might abuse these privileges to cause serious...
Xuanjun Zong, Zhiqi Shen, Lei Wang +2 more
Large language models (LLMs) are evolving into agentic systems that reason, plan, and operate external tools. The Model Context Protocol (MCP) is a...
Yihan Liao, Jacky Keung, Xiaoxue Ma +2 more
The rapid advancement of Large Language Models (LLMs) has been driven by extensive datasets that may contain sensitive information, raising serious...
Ruozhao Yang, Mingfei Cheng, Gelei Deng +3 more
Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly...
Akhil Sharma, Shaikh Yaser Arafat, Jai Kumar Sharma +1 more
The increasing operational reliance on complex Multi-Agent Systems (MAS) across safety-critical domains necessitates rigorous adversarial robustness...
Ali Al Sahili, Ali Chehab, Razane Tajeddine
Large Language Models (LLMs) are prone to memorizing training data, which poses serious privacy risks. Two of the most prominent concerns are...
Md Nahid Hasan Shuvo, Moinul Hossain
Connected autonomous vehicles (CAVs) rely on vision-based deep neural networks (DNNs) and low-latency (Vehicle-to-Everything) V2X communication to...
Sanjay Das, Swastik Bhattacharya, Shamik Kundu +3 more
State-space models (SSMs), exemplified by the Mamba architecture, have recently emerged as state-of-the-art sequence-modeling frameworks, offering...
Luoxi Meng, Henry Feng, Ilia Shumailov +1 more
Browser-using agents (BUAs) are an emerging class of AI agents that interact with web browsers in human-like ways, including clicking, scrolling,...
Arastoo Zibaeirad, Marco Vieira
Large Language Models (LLMs) are increasingly being studied for Software Vulnerability Detection (SVD) and Repair (SVR). Individual LLMs have...
Xin Yang, Omid Ardakanian
Data obfuscation is a promising technique for mitigating attribute inference attacks by semi-trusted parties with access to time-series data emitted...
Edward Lue Chee Lip, Anthony Channg, Diana Kim +2 more
As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\unicode{x2013}$ but what if the decomposer itself is...
Han Yang, Shaofeng Li, Tian Dong +3 more
Deep Neural Networks (DNNs), as valuable intellectual property, face unauthorized use. Existing protections, such as digital watermarking, are...
N Mangala, Murtaza Rangwala, S Aishwarya +5 more
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices are revolutionising remote patient monitoring,...
Jan Betley, Jorio Cocola, Dylan Feng +4 more
LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow...
Aink Acrie Soe Thein, Nikolaos Pitropakis, Pavlos Papadopoulos +2 more
With the adoption of multiple digital devices in everyday life, the cyber-attack surface has increased. Adversaries are continuously exploring new...
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