AI Threat Alert indexes 3,771+ 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 21–40 of 57 papers
Clear filtersDavood Wadi, Yu Ma
Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab," an adaptation...
Asmah Muallem, Firdous Kausar, Sajid Hussain +1 more
Flipping 60\% of training labels from a single Byzantine client using label-flipping model poisoning self-degrades an attacker's own federated...
Jinqian Zhang, Haojun Xia, Shujiang Wu +4 more
Multi-step tool-calling LLM agents rely on host runtimes to preserve state across turns. When a runtime carries an external tool return into later...
Mingyuan Li, Yanna Jiang, Guangsheng Yu +4 more
Privacy-sensitive organizations may run large language models (LLMs) in restricted or air-gapped environments while exporting selected diagnostic...
Saeedeh Lohrasbi, Mohammad Mamun, Ahmed Yehia +2 more
Multi-stage LLM-based cyber agents may complete attack workflows while remaining brittle, costly, or reliant on incorrect interpretations of...
Jian Xu
Agentic video-generation systems close a loop between a generator and a verifier: an LLM plans shots, calls a text-to-video model, and a multimodal...
Xuwei Tan, Yao Ma, Xueru Zhang
Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized...
Jasem Khelifi, Issam Oukhay, Ali Ouni +2 more
Agentic workflows shift software development from prompting AI agents for individual tasks to defining recurring work that agents execute...
Yue Xing, Pengfei He, Zitao Li
With recent advancements in large language models (LLMs) and LLM-based agents, these agents are becoming increasingly autonomous and gaining broader...
Djamel Eddine Hakim Ghorab, Farid Mokhati, Mostafa Anouar Ghorab
The growing adoption of blockchain technologies, particularly the Ethereum platform, has amplified the critical role of smart contracts in...
Tianhao Chen, Yuhan Wei, Weifei Jin +3 more
Multi-passage corpus poisoning often repeats one target claim across similar documents, creating correlated lexical and semantic patterns that...
Reshabh K Sharma, Linxi Jiang, Shuo Chen +1 more
A language model agent acts through the tools it is given. The data it reads while working on a task can redirect what it does with those tools. A...
Om Nepal, Sushant Aryal, Oluseyi Olukola +1 more
Large language models (LLMs) are increasingly applied to the automated repair of C/C++ security vulnerabilities, and compile rate is a commonly...
Christopher Burger, Christina Trotter, Joseph Carlisle +1 more
The rapidly advancing capabilities of vision-language models (VLMs) present a systemic challenge to academic integrity. VLMs now allow students to...
Brandon Bauer, Matthew Whalen, Kenji Watanabe +4 more
Artificial-intelligence (AI) agents are beginning to enter experimental laboratories, automating experiments and accelerating scientific discovery....
Arthur Cordeiro, Alberto Maria Mongardini, Emmanouil Vasilomanolakis
Honeypots are designed to deceive attackers, and recent work shows they can also derail autonomous LLM-based pentesters. These evaluations, however,...
Xin Shen, San-Zhuo Xi, Yali Du +1 more
Recent advances in large language models (LLMs) have made them widely used for code-related tasks. Identifier names are statistically informative in...
Han Chen, Hanchen Wang, Hongmei Chen +3 more
Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation...
Mauro Conti, Lorenzo Perinello, Umberto Salviati
The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at...
Francesca Medda, Hui Gong
Dementia care increasingly involves connected sensors, medication devices, electronic records, and assistive technologies. Interoperability can...
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 3,771+ 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