AI Threat Alert indexes 3,082+ 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 2061–2080 of 3,082 papers
Le Wang, Zonghao Ying, Xiao Yang +7 more
Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks, yet they remain vulnerable...
Ahmed M. Hussain, Salahuddin Salahuddin, Panos Papadimitratos
Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability...
Jiashuo Liu, Jiayun Wu, Chunjie Wu +5 more
The rapid proliferation of Large Language Models (LLMs) and diverse specialized benchmarks necessitates a shift from fragmented, task-specific...
Lichao Wu, Sasha Behrouzi, Mohamadreza Rostami +2 more
Mixture-of-Experts (MoE) architectures have advanced the scaling of Large Language Models (LLMs) by activating only a sparse subset of parameters per...
Yihan Wang, Huanqi Yang, Shantanu Pal +1 more
The integration of Large Language Models (LLMs) into wearable sensing is creating a new class of mobile applications capable of nuanced human...
Yifan Yao, Baojuan Wang, Jinhao Duan +4 more
Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on...
Anselm Paulus, Ilia Kulikov, Brandon Amos +4 more
Ensuring the safety of language models (LMs) while maintaining their usefulness remains a critical challenge in AI alignment. Current approaches rely...
Miles Q. Li, Benjamin C. M. Fung, Martin Weiss +3 more
As autonomous AI agents are increasingly deployed in high-stakes environments, ensuring their safety and alignment with human values has become a...
Omer Gazit, Yael Itzhakev, Yuval Elovici +1 more
Radio frequency (RF) based systems are increasingly used to detect drones by analyzing their RF signal patterns, converting them into spectrogram...
Adam Elaoumari
The purpose of this project is to assess how well defenders can detect DNS-over-HTTPS (DoH) file exfiltration, and which evasion strategies can be...
Kanchon Gharami, Sanjiv Kumar Sarkar, Yongxin Liu +1 more
Large Language Models (LLMs) like ChatGPT are now widely used in writing and reviewing scientific papers. While this trend accelerates publication...
Jaykumar Kasundra, Anjaneya Praharaj, Sourabh Surana +11 more
Safeguarding large language models (LLMs) against unsafe or adversarial behavior is critical as they are increasingly deployed in conversational and...
Zhenlei Ye, Xiaobing Sun, Sicong Cao +2 more
The advances of large language models (LLMs) have paved the way for automated software vulnerability repair approaches, which iteratively refine the...
Aaron Chan, Alex Ding, Frank Chen +3 more
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the...
Songze Li, Jiameng Cheng, Yiming Li +2 more
By integrating language understanding with perceptual modalities such as images, multimodal large language models (MLLMs) constitute a critical...
Honglin Mu, Jinghao Liu, Kaiyang Wan +4 more
Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content...
Sangryu Park, Gihyuk Ko, Homook Cho
Large Language Models (LLMs) show significant promise in automating software vulnerability analysis, a critical task given the impact of security...
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob +1 more
Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years,...
Shaghayegh Shajarian, Kennedy Marsh, James Benson +2 more
Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic...
Samruddhi Baviskar
Machine learning models used in financial decision systems operate in nonstationary economic environments, yet adversarial robustness is typically...
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 3,082+ 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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