AI Threat Alert indexes 3,771+ 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 2901–2920 of 3,771 papers
Alisha Ukani, Hamed Haddadi, Ali Shahin Shamsabadi +1 more
This paper presents a systematic evaluation of the privacy behaviors and attributes of eight recent, popular browser agents. Browser agents are...
JV Roig
We investigate how large language models (LLMs) fail when operating as autonomous agents with tool-use capabilities. Using the Kamiwaza Agentic Merit...
Zikai Mao, Lingchen Zhao, Lei Xu +4 more
On-device machine learning (ML) introduces new security concerns about model privacy. Storing valuable trained ML models on user devices exposes them...
Ziming Hong, Tianyu Huang, Runnan Chen +4 more
Recent studies have extended diffusion-based instruction-driven 2D image editing pipelines to 3D Gaussian Splatting (3DGS), enabling faithful...
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +1 more
To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against...
Max Zhang, Derek Liu, Kai Zhang +2 more
Large language models (LLMs) are increasingly deployed worldwide, yet their safety alignment remains predominantly English-centric. This allows for...
Fenghua Weng, Chaochao Lu, Xia Hu +2 more
As multimodal reasoning improves the overall capabilities of Large Vision Language Models (LVLMs), recent studies have begun to explore...
Yunzhe Li, Jianan Wang, Hongzi Zhu +3 more
Large Language Models (LLMs) have become foundational components in a wide range of applications, including natural language understanding and...
Richard Young
Despite substantial investment in safety alignment, the vulnerability of large language models to sophisticated multi-turn adversarial attacks...
George Mikros
Large language models (LLMs) present a dual challenge for forensic linguistics. They serve as powerful analytical tools enabling scalable corpus...
Guanquan Shi, Haohua Du, Zhiqiang Wang +4 more
Large Language Models (LLMs) are evolving into autonomous agents capable of executing complex workflows via standardized protocols (e.g., MCP)....
Wenjie Zhang, Yun Lin, Chun Fung Amos Kwok +5 more
Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing...
Xiaoqi Li, Hailu Kuang, Wenkai Li +2 more
Traditional approaches for smart contract analysis often rely on intermediate representations such as abstract syntax trees, control-flow graphs, or...
Sima Jafarikhah, Daniel Thompson, Eva Deans +2 more
Manual vulnerability scoring, such as assigning Common Vulnerability Scoring System (CVSS) scores, is a resource-intensive process that is often...
Zhibo Liang, Tianze Hu, Zaiye Chen +1 more
Autonomous Large Language Model (LLM) agents exhibit significant vulnerability to Indirect Prompt Injection (IPI) attacks. These attacks hijack agent...
Donghang Duan, Xu Zheng, Yuefeng He +3 more
Current LLM-based text anonymization frameworks usually rely on remote API services from powerful LLMs, which creates an inherent privacy paradox:...
Songping Wang, Rufan Qian, Yueming Lyu +5 more
Image-to-Video (I2V) generation synthesizes dynamic visual content from image and text inputs, providing significant creative control. However, the...
Jehyeok Yeon, Federico Cinus, Yifan Wu +1 more
Large language models (LLMs) face critical safety challenges, as they can be manipulated to generate harmful content through adversarial prompts and...
Xiaojun Jia, Jie Liao, Qi Guo +11 more
Recent advances in multi-modal large language models (MLLMs) have enabled unified perception-reasoning capabilities, yet these systems remain highly...
Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +3 more
The Model Context Protocol (MCP) enables Large Language Models to integrate external tools through structured descriptors, increasing autonomy in...
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 3,771+ 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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