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 241–260 of 272 papers
Clear filtersLuis Lazo, Hamed Jelodar, Roozbeh Razavi-Far
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large...
Renmiao Chen, Yida Lu, Shiyao Cui +6 more
As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may...
William Pan, Guiran Liu, Binrong Zhu +4 more
The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service (DDoS) attacks, characterized by...
Anudeex Shetty, Aditya Joshi, Salil S. Kanhere
Humans are susceptible to undesirable behaviours and privacy leaks under the influence of alcohol. This paper investigates drunk language, i.e., text...
Xiaofeng Luo, Jiayi He, Jiawen Kang +4 more
The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through...
Jonah Ghebremichael, Saastha Vasan, Saad Ullah +6 more
Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results...
Hao Wang, Yanting Wang, Hao Li +2 more
Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial ``jailbreak'' attacks designed to bypass...
Xingjun Ma, Yixu Wang, Hengyuan Xu +18 more
The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and...
Jiawen Zhang, Yangfan Hu, Kejia Chen +7 more
Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential...
Caitlin A. Stamatis, Jonah Meyerhoff, Richard Zhang +3 more
Large language models (LLMs) are increasingly used for mental health support, yet existing safety evaluations rely primarily on small,...
Zhenhua Xu, Yiran Zhao, Mengting Zhong +4 more
The rapid growth of large language models raises pressing concerns about intellectual property protection under black-box deployment. Existing...
Zhichen Zeng, Wenxuan Bao, Xiao Lin +8 more
Vision-language models (VLMs), despite their extraordinary zero-shot capabilities, are vulnerable to distribution shifts. Test-time adaptation (TTA)...
Mingxiang Tao, Yu Tian, Wenxuan Tu +3 more
Federated learning (FL) addresses data privacy and silo issues in large language models (LLMs). Most prior work focuses on improving the training...
Kaiwen Zhou, Shreedhar Jangam, Ashwin Nagarajan +7 more
Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex,...
Imtiaz Ali Soomro, Hamood Ur Rehman, S. Jawad Hussain ID +3 more
The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks...
Qingyuan Li, Chenchen Yu, Chuanyi Li +4 more
Vulnerabilities severely threaten software systems, making the timely application of security patches crucial for mitigating attacks. However,...
G M Shahariar, Zabir Al Nazi, Md Olid Hasan Bhuiyan +1 more
Vision Language Models (VLMs) are increasingly integrated into privacy-critical domains, yet existing evaluations of personally identifiable...
Jua Han, Jaeyoon Seo, Jungbin Min +2 more
One mistake by an AI system in a safety-critical setting can cost lives. As Large Language Models (LLMs) become integral to robotics decision-making,...
Ilmo Sung
Large language models suffer from "hallucinations"-logical inconsistencies induced by semantic noise. We propose that current architectures operate...
Han Zhu, Jiale Chen, Chengkun Cai +8 more
Multi-modal Large Language Models (MLLMs) are increasingly deployed in interactive applications. However, their safety vulnerabilities become...
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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