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 481–500 of 562 papers
Clear filtersKristen Moore, Diksha Goel, Cody James Christopher +5 more
Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing...
Anmol Goel, Cornelius Emde, Sangdoo Yun +2 more
We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle...
Xiaonan Jing, Gongqing Wu, Xingrui Zhuo +2 more
Open-domain Relational Triplet Extraction (ORTE) is the foundation for mining structured knowledge without predefined schemas. Despite the impressive...
Nilanjana Chatterjee, Sidharatha Garg, A V Subramanyam +1 more
Text-Based Person Search (TBPS) has seen significant progress with vision-language models (VLMs), yet it remains constrained by limited training data...
Keita Takeda, Tomoya Sakai
This study investigates the feature representations produced by publicly available open source medical vision-language models (VLMs). While medical...
Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim +6 more
Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments...
Linbo Cao, Lihao Sun, Yang Yue
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of actions with real-world impacts beyond text generation. While...
Yiyang Lu, Jinwen He, Yue Zhao +2 more
Large Language Models (LLMs) are widely integrated into interactive systems such as dialogue agents and task-oriented assistants. This growing...
Fan Huang, Haewoon Kwak, Jisun An
Large Language Models (LLMs) are increasingly employed in various question-answering tasks. However, recent studies showcase that LLMs are...
Jiasen Li, Yanwei Liu, Zhuoyi Shang +2 more
Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and...
Jiayi Yuan, Jonathan Nöther, Natasha Jaques +1 more
While recent automated red-teaming methods show promise for systematically exposing model vulnerabilities, most existing approaches rely on...
Jonatan Rassekhnia
Post-quantum cryptography (PQC) is becoming essential for securing Internet of Things (IoT) and Industrial IoT (IIoT) systems against quantum-enabled...
Yow-Fu Liou, Yu-Chien Tang, Yu-Hsiang Liu +1 more
Benchmarking large language models (LLMs) is critical for understanding their capabilities, limitations, and robustness. In addition to interface...
Johannes Kaiser, Alexander Ziller, Eleni Triantafillou +2 more
Individual Differential Privacy (iDP) promises users control over their privacy, but this promise can be broken in practice. We reveal a previously...
Wenqi Zhang, Yulin Shen, Changyue Jiang +3 more
Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user...
Ke Xie, Xingyi Zhao, Yiwen Hu +2 more
Cellular networks are critical infrastructure supporting billions of worldwide users and safety- and mission-critical services. Vulnerabilities in...
Chutian Huang, Dake Cao, Jiacheng Ji +3 more
Background: While Large Language Models (LLMs) have achieved widespread adoption, malicious prompt engineering specifically "jailbreak attacks" poses...
Arunkumar V, Gangadharan G. R., Rajkumar Buyya
Artificial Intelligence is moving from models that only generate text to Agentic AI, where systems behave as autonomous entities that can perceive,...
Huanyi Ye, Jiale Guo, Ziyao Liu +1 more
RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services...
Yixuan Du, Chenxiao Yu, Haoyan Xu +3 more
Vision-Language Models (VLMs) are rapidly replacing unimodal encoders in modern retrieval and recommendation systems. While their capabilities are...
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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