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 161–180 of 521 papers
Clear filtersYicheng Cai, Mitchell John DeStefano, Guodong Dong +5 more
As Large Language Models (LLMs) and multi-agent AI systems are demonstrating increasing potential in cybersecurity operations, organizations,...
Quan Zhang, Lianhang Fu, Lvsi Lian +5 more
Equipping LLM agents with real-world tools can substantially improve productivity. However, granting agents autonomy over tool use also transfers the...
Vishal Narnaware, Animesh Gupta, Kevin Zhai +2 more
Multimodal Diffusion Large Language Models (MDLLMs) achieve high-concurrency generation through parallel masked decoding, yet the architectures...
Pei Chen, Geng Hong, Xinyi Wu +6 more
The emergence of Large Language Model-enhanced Search Engines (LLMSEs) has revolutionized information retrieval by integrating web-scale search...
Michael Somma, Markus Großpointner, Paul Zabalegui +2 more
The increasing complexity and interconnectivity of digital infrastructures make scalable and reliable security assessment methods essential. Robotic...
Oussama Draissi, Mark Günzel, Ahmad-Reza Sadeghi +1 more
WebAssembly's (Wasm) monolithic linear memory model facilitates memory corruption attacks that can escalate to cross-site scripting in browsers or go...
Zhanguang Zhang, Zhiyuan Li, Behnam Rahmati +10 more
Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predicting...
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera
Large language models are becoming pervasive core components in many real-world applications. As a consequence, security alignment represents 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...
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...
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...
Caglar Yildirim
Large language models (LLMs) are increasingly deployed as tool-using agents, shifting safety concerns from harmful text generation to harmful task...
Gengxin Sun, Ruihao Yu, Liangyi Yin +3 more
Ensuring robust and fair interview assessment remains a key challenge in AI-driven evaluation. This paper presents CoMAI, a general-purpose...
Yu Pan, Wenlong Yu, Tiejun Wu +4 more
Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. However, they remain highly susceptible to...
Ye Wang, Jing Liu, Toshiaki Koike-Akino
The safety and reliability of vision-language models (VLMs) are a crucial part of deploying trustworthy agentic AI systems. However, VLMs remain...
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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