AI Threat Alert indexes 3,371+ 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 141–160 of 172 papers
Clear filtersBui Duc Son, Trinh Van Chien, Dong In Kim
The integration of generative artificial intelligence (GenAI) into 6G networks promises substantial performance gains while simultaneously exposing...
Rathin Chandra Shit, Sharmila Subudhi
The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands,...
Dennis Wei, Ronny Luss, Xiaomeng Hu +6 more
Large Language Models (LLMs) have become ubiquitous in everyday life and are entering higher-stakes applications ranging from summarizing meeting...
Liang Shan, Kaicheng Shen, Wen Wu +7 more
Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address...
Peng Zhang, Peijie Sun
Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal...
Jiayi Fu, Yuansen Zhang, Yinggui Wang
Large Language Models (LLMs) demonstrate strong capabilities in solving complex tasks when integrated with external tools. The Model Context Protocol...
Tim Beyer, Jonas Dornbusch, Jakob Steimle +3 more
The rapid expansion of research on Large Language Model (LLM) safety and robustness has produced a fragmented and oftentimes buggy ecosystem of...
Ken Huang, Kyriakos Rock Lambros, Jerry Huang +8 more
This paper introduces the Agentic AI Governance Assurance & Trust Engine (AAGATE), a Kubernetes-native control plane designed to address the unique...
Adetayo Adebimpe, Helmut Neukirchen, Thomas Welsh
Honeypots are decoy systems used for gathering valuable threat intelligence or diverting attackers away from production systems. Maximising attacker...
Li An, Yujian Liu, Yepeng Liu +3 more
Watermarking has emerged as a promising solution for tracing and authenticating text generated by large language models (LLMs). A common approach to...
Alyssa Gerhart, Balaji Iyangar
Adversarial attacks pose a severe risk to AI systems used in healthcare, capable of misleading models into dangerous misclassifications that can...
Zhonghao Zhan, Amir Al Sadi, Krinos Li +1 more
In this work, we study security of Model Context Protocol (MCP) agent toolchains and their applications in smart homes. We introduce AegisMCP, a...
Thomas Wang, Haowen Li
As large language models (LLMs) are increasingly integrated into real-world applications, ensuring their safety, robustness, and privacy compliance...
Rishi Jha, Harold Triedman, Justin Wagle +1 more
Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system...
Yue Liu, Zhenchang Xing, Shidong Pan +1 more
In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex...
Xiaofan Li, Xing Gao
The Model Context Protocol (MCP) is an emerging open standard that enables AI-powered applications to interact with external tools through structured...
Edoardo Allegrini, Ananth Shreekumar, Z. Berkay Celik
Agentic AI systems, which leverage multiple autonomous agents and Large Language Models (LLMs), are increasingly used to address complex, multi-step...
Yisen Wang, Yichuan Mo, Hongjun Wang +2 more
Despite the rapid progress of neural networks, they remain highly vulnerable to adversarial examples, for which adversarial training (AT) is...
Karthik Avinash, Nikhil Pareek, Rishav Hada
The increasing deployment of Large Language Models (LLMs) across enterprise and mission-critical domains has underscored the urgent need for robust...
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs...
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 3,371+ 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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