AI Threat Alert indexes 3,406+ 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 381–400 of 981 papers
Clear filtersMohammad Asadi, Jack W. O'Sullivan, Fang Cao +5 more
Multimodal AI systems have achieved remarkable performance across a broad range of real-world tasks, yet the mechanisms underlying visual-language...
Zhongyi Li, Wan Tian, Jingyu Chen +8 more
Multi-agent collaboration has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models, yet it suffers from...
Zongjie Li, Chaozheng Wang, Yuchong Xie +2 more
Large Language Models are increasingly being considered for deployment in safety-critical military applications. However, current benchmarks suffer...
Zihan Guo, Zhiyu Chen, Xiaohang Nie +3 more
With the rapid evolution of Large Language Model (LLM) agent ecosystems, centralized skill marketplaces have emerged as pivotal infrastructure for...
Yandan Zheng, Haoran Luo, Zhenghong Lin +2 more
Benchmarks are the de facto standard for tracking progress in large language models (LLMs), yet static test sets can rapidly saturate, become...
Sen Fang, Weiyuan Ding, Zhezhen Cao +2 more
Large Language Models (LLMs) are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a...
Jiahao Chen, Zhiming Zhao, Yuwen Pu +4 more
Federated learning (FL) has attracted substantial attention in both academia and industry, yet its practical security posture remains poorly...
Hung Yun Tseng, Wuzhen Li, Blerina Gkotse +1 more
The potential of Large Language Models (LLMs) to provide harmful information remains a significant concern due to the vast breadth of illegal queries...
Christopher J. Agostino, Quan Le Thien, Nayan D'Souza +1 more
Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe,...
Fazhong Liu, Zhuoyan Chen, Tu Lan +6 more
Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to...
Dong Yan, Jian Liang, Yanbo Wang +3 more
Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving...
Zou Qiang
Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under...
Zikang Ding, Junhao Li, Suling Wu +3 more
Model watermarking utilizes internal representations to protect the ownership of large language models (LLMs). However, these features inevitably...
Alvin Rajkomar, Pavan Sudarshan, Angela Lai +1 more
Background: Clinical trials rely on transparent inclusion criteria to ensure generalizability. In contrast, benchmarks validating health-related...
Iakovos-Christos Zarkadis, Christos Douligeris
Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time...
Haocheng Li, Juepeng Zheng, Shuangxi Miao +4 more
Multimodal remote sensing semantic segmentation enhances scene interpretation by exploiting complementary physical cues from heterogeneous data....
Wanjun Du, Zifeng Yuan, Tingting Chen +3 more
Existing vision-language models (VLMs) have demonstrated impressive performance in reasoning-based segmentation. However, current benchmarks are...
Yuntong Zhang, Sungmin Kang, Ruijie Meng +2 more
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end. In the front...
Chengwei Wei, Jung-jae Kim, Longyin Zhang +2 more
Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary...
Min Zeng, Shuang Zhou, Zaifu Zhan +1 more
Medical language models must be updated as evidence and terminology evolve, yet sequential updating can trigger catastrophic forgetting. Although...
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 3,406+ 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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