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 101–109 of 109 papers
Clear filtersPengyu Zhu, Lijun Li, Yaxing Lyu +3 more
LLM-based multi-agent systems (MAS) demonstrate increasing integration into next-generation applications, but their safety in backdoor attacks...
Hyeseon An, Shinwoo Park, Suyeon Woo +1 more
The promise of LLM watermarking rests on a core assumption that a specific watermark proves authorship by a specific model. We demonstrate that this...
Dennis Rall, Bernhard Bauer, Mohit Mittal +1 more
Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflows like...
Jonathan Sneh, Ruomei Yan, Jialin Yu +6 more
As LLMs increasingly power agents that interact with external tools, tool use has become an essential mechanism for extending their capabilities....
Shoumik Saha, Jifan Chen, Sam Mayers +3 more
Code-capable large language model (LLM) agents are increasingly embedded into software engineering workflows where they can read, write, and execute...
Jing-Jing Li, Jianfeng He, Chao Shang +6 more
As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based...
Petar Radanliev
This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key...
Ping He, Changjiang Li, Binbin Zhao +2 more
The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize...
Adam Swanda, Amy Chang, Alexander Chen +3 more
The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across...
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