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 81–100 of 161 papers
Clear filtersSaad Hossain, Tom Tseng, Punya Syon Pandey +8 more
As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications,...
Guowei Guan, Yurong Hao, Jiaming Zhang +6 more
Multimodal large language models (MLLMs) are pushing recommender systems (RecSys) toward content-grounded retrieval and ranking via cross-modal...
Guangwei Zhang, Jianing Zhu, Cheng Qian +12 more
We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM...
Gautam Savaliya, Robert Aufschläger, Abhishek Subedi +2 more
Artificial intelligence systems introduce complex privacy risks throughout their lifecycle, especially when processing sensitive or high-dimensional...
Alsharif Abuadbba, Nazatul Sultan, Surya Nepal +1 more
AI is moving from domain-specific autonomy in closed, predictable settings to large-language-model-driven agents that plan and act in open,...
Naen Xu, Hengyu An, Shuo Shi +7 more
Recent advancements in large language models (LLMs) have significantly enhanced the capabilities of collaborative multi-agent systems, enabling them...
Waleed Khan Mohammed, Zahirul Arief Irfan Bin Shahrul Anuar, Mousa Sufian Mousa Mitani +2 more
Advanced Persistent Threats (APTs) are among the most challenging cyberattacks to detect. They are carried out by highly skilled attackers who...
Xiang Zheng, Yutao Wu, Hanxun Huang +5 more
Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and...
Lige Huang, Zicheng Liu, Jie Zhang +3 more
The dual offensive and defensive utility of Large Language Models (LLMs) highlights a critical gap in AI security: the lack of unified frameworks for...
Satyapriya Krishna, Matteo Memelli, Tong Wang +5 more
Amazon published its Frontier Model Safety Framework (FMSF) as part of the Paris AI summit, following which we presented a report on Amazon's Premier...
Inderjeet Singh, Eleonore Vissol-Gaudin, Andikan Otung +1 more
Fine-tuning Large Language Models (LLMs) for specialized domains is constrained by a fundamental challenge: the need for diverse,...
Wenbo Guo, Shiwen Song, Jiaxun Guo +5 more
Open-source ecosystems such as NPM and PyPI are increasingly targeted by supply chain attacks, yet existing detection methods either depend on...
Jiazhu Xie, Bowen Li, Heyu Fu +3 more
Large Language Model (LLM)-based question-answering systems offer significant potential for automating customer support and internal knowledge access...
Yijin Zhou, Xiaoya Lu, Dongrui Liu +2 more
The rapid advancement of Large Language Model (LLM)-based Multi-Agent Systems (MAS) has introduced significant security vulnerabilities, where...
Daisuke Miyamoto, Takuji Iimura, Narushige Michishita
With the spread of generative AI in recent years, attacks known as Whaling have become a serious threat. Whaling is a form of social engineering that...
Roy Betser, Shamik Bose, Amit Giloni +3 more
AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper tool...
Zimo Ji, Daoyuan Wu, Wenyuan Jiang +5 more
Large Language Model (LLM)-based agent systems are increasingly deployed for complex real-world tasks but remain vulnerable to natural language-based...
Kaiyu Zhou, Yongsen Zheng, Yicheng He +5 more
The agent--tool interaction loop is a critical attack surface for modern Large Language Model (LLM) agents. Existing denial-of-service (DoS) attacks...
Xinrui Zhang, Pincan Zhao, Jason Jaskolka +2 more
Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as...
Yutao Mou, Zhangchi Xue, Lijun Li +4 more
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks....
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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