AI Threat Alert indexes 3,082+ 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 981–1000 of 3,082 papers
Pei Chen, Geng Hong, Xinyi Wu +6 more
The emergence of Large Language Model-enhanced Search Engines (LLMSEs) has revolutionized information retrieval by integrating web-scale search...
Xunguang Wang, Yuguang Zhou, Qingyue Wang +5 more
Large language models (LLMs) increasingly rely on explicit chain-of-thought (CoT) reasoning to solve complex tasks, yet the safety of the reasoning...
Eyal Hadad, Mordechai Guri
On-device Vision-Language Models (VLMs) promise data privacy via local execution. However, we show that the architectural shift toward Dynamic...
Younes Salmi, Hanna Bogucka
Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic...
Younes Salmi, Hanna Bogucka
Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation...
Younes Salmi, Hanna Bogucka
This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for...
Hieu Xuan Le, Benjamin Goh, Quy Anh Tang
Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production,...
Haozhen Wang, Haoyue Liu, Jionghao Zhu +3 more
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is...
Cristian Lupascu, Alexandru Lupascu
Large Language Model based agents increasingly operate in high stakes, multi turn settings where factual grounding is critical, yet their memory...
Zhihui Yao, Hengran Zhang, Keping Bi
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) with external knowledge but remains vulnerable to low-authority sources...
Ron Litvak
System prompt configuration can make the difference between near-total phishing blindness and near-perfect detection in LLM email agents. We present...
Zhenyi Wang, Siyu Luan
As machine learning (ML) systems expand in both scale and functionality, the security landscape has become increasingly complex, with a proliferation...
Ahmed Lekssays
Large Language Models (LLMs) face critical challenges when analyzing security vulnerabilities in real world codebases: token limits prevent loading...
Francesco Gentile, Nicola Dall'Asen, Francesco Tonini +3 more
As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability...
Yuxiao Li, Alina Fastowski, Efstratios Zaradoukas +2 more
Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and...
Alexander Panfilov, Peter Romov, Igor Shilov +3 more
LLM agents like Claude Code can not only write code but also be used for autonomous AI research and engineering \citep{rank2026posttrainbench,...
Joseph G. Zalameda, Megan A. Witherow, Alexander M. Glandon +2 more
Machine learning models trained on small data sets for security applications are especially vulnerable to adversarial attacks. Person identification...
Michael Somma, Markus Großpointner, Paul Zabalegui +2 more
The increasing complexity and interconnectivity of digital infrastructures make scalable and reliable security assessment methods essential. Robotic...
Yulin Shen, Xudong Pan, Geng Hong +1 more
Recent advances in the Model Context Protocol (MCP) have enabled large language models (LLMs) to invoke external tools with unprecedented ease. This...
Oussama Draissi, Mark Günzel, Ahmad-Reza Sadeghi +1 more
WebAssembly's (Wasm) monolithic linear memory model facilitates memory corruption attacks that can escalate to cross-site scripting in browsers or go...
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 3,082+ 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