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 1201–1220 of 1,757 papers
Clear filtersYuanhang Li
Operating LEO mega-constellations requires translating high-level operator intents ("reroute financial traffic away from polar links under 80 ms")...
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang +1 more
As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to...
Luat Do, Jiao Yin, Jinli Cao +1 more
Software vulnerabilities continue to pose significant threats to modern information systems, requiring a timely and accurate risk assessment. Public...
Zhiheng Li, Zongyang Ma, Yuntong Pan +8 more
Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a...
Zhiheng Li, Zongyang Ma, Yuntong Pan +8 more
Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a...
Simon Calderon, Niklas Johansson, Onur Günlü
Ensuring ciphertext indistinguishability is fundamental to cryptographic security, but empirically validating this property in real implementations...
Ziye Wang, Guanyu Wang, Kailong Wang
Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs), but simultaneously exposes a critical vulnerability to...
Nikolaos D. Tantaroudas, Ilias Karachalios, Andrew J. McCracken
The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor...
Yizhe Zeng, Wei Zhang, Yunpeng Li +3 more
While Chain-of-Thought (CoT) prompting has become a standard paradigm for eliciting complex reasoning capabilities in Large Language Models, it...
Shunan Zhu, Jiawei Chen, Yonghao Yu +1 more
As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving...
Zi Liang, Qipeng Xie, Jun He +7 more
Recent advancements in Large Language Models (LLMs) have sparked interest in their application to Static Application Security Testing (SAST),...
Phan The Duy, Nguyen Viet Duy, Khoa Ngo-Khanh +2 more
While recent approaches leverage large language models (LLMs) and multi-agent pipelines to automatically generate proof-of-concept (PoC) exploits...
Adrian Shuai Li, Md Ajwad Akil, Elisa Bertino
Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied...
Yinghan Hou, Zongyou Yang
OpenClaw's ClawHub marketplace hosts over 13,000 community-contributed agent skills, and between 13% and 26% of them contain security vulnerabilities...
Peigui Qi, Kunsheng Tang, Yanpu Yu +7 more
Vision-Language Models (VLMs) face significant safety vulnerabilities from malicious prompt attacks due to weakened alignment during visual...
Manish Bhatt, Sarthak Munshi, Vineeth Sai Narajala +4 more
We prove that no continuous, utility-preserving wrapper defense-a function $D: X\to X$ that preprocesses inputs before the model sees them-can make...
Peng Huang, Yiming Wang, Yineng Chen +9 more
Echocardiography plays an important role in the screening and diagnosis of cardiovascular diseases. However, automated intelligent analysis of...
Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao +2 more
When introducing Large Language Models (LLMs) into industrial applications, such as healthcare and education, the risk of generating harmful content...
Changgeon Ko, Jisu Shin, Hoyun Song +3 more
Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates...
Nirajan Acharya, Gaurav Kumar Gupta
The Model Context Protocol (MCP), introduced by Anthropic in November 2024 and now governed by the Linux Foundation's Agentic AI Foundation, has...
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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