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 61–80 of 125 papers
Clear filtersZhenyi Wang, Siyu Luan
As machine learning (ML) systems expand in both scale and functionality, the security landscape has become increasingly complex, with a proliferation...
Shenghan Zheng, Qifan Zhang
AI agent protocols -- including MCP, A2A, ANP, and ACP -- enable autonomous agents to discover capabilities, delegate tasks, and compose services...
Oleksandr Yarotskyi, José D'Abruzzo Pereira, João R. Campos
The widespread adoption of web applications has made their security a critical concern and has increased the need for systematic ways to assess...
Yanming Mu, Hao Hu, Feiyang Li +7 more
Retrieval-Augmented Generation (RAG) significantly mitigates the hallucinations and domain knowledge deficiency in large language models by...
Shouqiao Wang, Marcello Politi, Samuele Marro +1 more
As agentic systems move into real-world deployments, their decisions increasingly depend on external inputs such as retrieved content, tool outputs,...
Dimitris Mitropoulos, Nikolaos Alexopoulos, Georgios Alexopoulos +1 more
Security code reviews increasingly rely on systems integrating Large Language Models (LLMs), ranging from interactive assistants to autonomous agents...
Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick
As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities,...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software...
Abhijeet Sahu, Shuva Paul, Richard Macwan
Cyber deception assists in increasing the attacker's budget in reconnaissance or any early phases of threat intrusions. In the past, numerous methods...
Kai Wang, Biaojie Zeng, Zeming Wei +7 more
With the rapid development of LLM-based multi-agent systems (MAS), their significant safety and security concerns have emerged, which introduce novel...
Zakia Zaman, Praveen Gauravaram, Mahbub Hassan +2 more
The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard...
Brecht Verbeken, Joke Van den Broeck, Inge De Cleyn +4 more
Purpose: Higher education institutions face increasing pressure to audit course designs for generative AI (GenAI) integration. This paper presents an...
Kele Xu, Yifan Wang, Ming Feng +5 more
Human-computer interaction has traditionally relied on the acoustic channel, a dependency that introduces systemic vulnerabilities to environmental...
Jiongchi Yu, Xiaolin Wen, Sizhe Cheng +3 more
Fuzz testing is one of the most effective techniques for detecting bugs and vulnerabilities in software. However, as the basis of fuzz testing,...
Fabrizio Dimino, Bhaskarjit Sarmah, Stefano Pasquali
The rapid adoption of large language models (LLMs) in financial services introduces new operational, regulatory, and security risks. Yet most...
Xiaolei Zhang, Lu Zhou, Xiaogang Xu +5 more
Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and...
Xiaolei Zhang, Lu Zhou, Xiaogang Xu +5 more
Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and...
Saroj Mishra, Suman Niroula, Umesh Yadav +3 more
Retrieval-Augmented Generation (RAG) systems are increasingly evolving into agentic architectures where large language models autonomously coordinate...
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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