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 81–100 of 172 papers
Clear filtersYijun Yu
Agentic AI systems exhibit numerous crosscutting concerns -- security, observability, cost management, fault tolerance -- that are poorly modularized...
Reva Schwartz, Carina Westling, Morgan Briggs +12 more
This paper proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI's...
Chuanming Tang, Ling Qing, Shifeng Chen
The rapid evolution of sophisticated cyberattacks has strained modern Security Operations Centers (SOC), which traditionally rely on rule-based or...
Quanjun Zhang, Chengyu Gao, Yu Han +4 more
The rapid advancement of Large Language Models (LLMs) has led to the emergence of intelligent agents capable of autonomously interacting with...
Kimberly T. Mai, Anna Gausen, Magda Dubois +5 more
AI is increasingly being used to assist fraud and cybercrime. However, it is unclear the extent to which current large language models can provide...
Yedi Zhang, Haoyu Wang, Xianglin Yang +2 more
LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task...
Florin Adrian Chitan
The proliferation of autonomous AI agents capable of executing real-world actions - filesystem operations, API calls, database modifications,...
Emmanuel Bamidele
Long-running LLM agents require persistent memory to preserve state across interactions, yet most deployed systems manage memory with age-based...
Arnold Cartagena, Ariane Teixeira
Large language models deployed as agents increasingly interact with external systems through tool calls--actions with real-world consequences that...
Herman Errico
As artificial intelligence systems evolve from passive assistants into autonomous agents capable of executing consequential actions, the security...
Juefei Pu, Xingyu Li, Zhengchuan Liang +5 more
Autonomous large language model (LLM) based systems have recently shown promising results across a range of cybersecurity tasks. However, there is no...
Saad 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...
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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