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 41–60 of 125 papers
Clear filtersJunjie Yu, Pengrui Lu, Weiye Si +75 more
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of...
Mohd Ruhul Ameen, Md Takrim Ul Alam, Akif Islam
Static Application Security Testing tools help developers find security vulnerabilities before release, but they often produce many false positives....
Luyao Xu, Xiang Chen
Autonomous agent frameworks built upon large language models (LLMs) are evolving into complex, tool-integrated, and continuously operating systems,...
Yuan Xin, Yixuan Weng, Minjun Zhu +6 more
As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial...
Harry Collins, Hartmut Grote, Paul Newbury +2 more
This paper is under review in AI and Ethics This study examines whether large language models (LLMs) can reliably answer scientific questions and...
Xiaohang Yu, Hejia Geng, William Knottenbelt
Agentic systems increasingly act with user secrets for APIs, messaging platforms, and cloud services. Today's bearer-secret interfaces implement...
Zihan Liu, Yizhen Wang, Rui Wang +2 more
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for...
Jiaqi Li, Yang Zhao, Bin Sun +3 more
Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering,...
Jialiang Wang, Yuchen Liu, Hang Xu +7 more
The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At...
Patrick Vossler, Jean Feng, Venkat Sivaraman +9 more
Hospital Quality Improvement (QI) plays a critical role in optimizing healthcare delivery by translating high-level hospital goals into actionable...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing...
Yuming Xu, Mingtao Zhang, Zhuohan Ge +5 more
Retrieval-augmented generation (RAG) significantly enhances large language models (LLMs) but introduces novel security risks through external...
Mehrdad Rostamzadeh, Sidhant Narula, Nahom Birhan +2 more
The Model Context Protocol (MCP) enables large language models (LLMs) to dynamically discover and invoke third-party tools, significantly expanding...
Nirajan Acharya, Gaurav Kumar Gupta
The Model Context Protocol (MCP), introduced by Anthropic in November 2024 and now governed by the Linux Foundation's Agentic AI Foundation, has...
Jiaren Peng, Zeqin Li, Chang You +17 more
The rapid advancement of Large Language Models (LLMs) has created new opportunities for Automated Penetration Testing (AutoPT), spawning numerous...
Charafeddine Mouzouni
LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt...
Xiao Qian, Shangjia Dong
Accurate prediction of evacuation behavior is critical for disaster preparedness, yet models trained in one region often fail elsewhere. Using a...
Aiman Almasoud, Antony Anju, Marco Arazzi +6 more
LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated...
Zihao Xu, Xiao Cheng, Ruijie Meng +1 more
LLM API calls are becoming a ubiquitous program construct, yet they create a boundary that no existing program analysis can cross: runtime values...
Bhavuk Jain, Sercan Ö. Arık, Hardeo K. Thakur
Multimodal large language models (MLLMs) integrate information from multiple modalities such as text, images, audio, and video, enabling complex...
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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