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 481–500 of 748 papers
Clear filtersMengqi He, Xinyu Tian, Xin Shen +4 more
Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, a measure of model uncertainty,...
Duo Chai, Zizhen Liu, Shuhuai Wang +4 more
Large language models (LLMs) are highly compute- and memory-intensive, posing significant demands on high-performance GPUs. At the same time,...
Tianwei Lan, Farid Naït-Abdesselam
The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning (ML) techniques for...
Xinjie Xu, Shuyu Cheng, Dongwei Xu +2 more
In hard-label black-box adversarial attacks, where only the top-1 predicted label is accessible, the prohibitive query complexity poses a major...
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...
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...
Linzhi Chen, Yang Sun, Hongru Wei +1 more
Low-Rank Adaptation (LoRA) has emerged as an efficient method for fine-tuning large language models (LLMs) and is widely adopted within the...
Sameera K. M., Serena Nicolazzo, Antonino Nocera +2 more
Federated Learning (FL) has recently emerged as a revolutionary approach to collaborative training Machine Learning models. In particular, it enables...
Akshaj Prashanth Rao, Advait Singh, Saumya Kumaar Saksena +1 more
Prompt injection and jailbreaking attacks pose persistent security challenges to large language model (LLM)-based systems. We present PromptScreen,...
Jianyi Zhang, Shizhao Liu, Ziyin Zhou +1 more
The rapid advancement of large language models (LLMs) has intensified concerns about the robustness of their safety alignment. While existing...
Huixin Zhan
Genomic Foundation Models (GFMs), such as Evolutionary Scale Modeling (ESM), have demonstrated remarkable success in variant effect prediction....
Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh +6 more
This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a...
Hao Li, Yubing Ren, Yanan Cao +4 more
With the rapid development of cloud-based services, large language models (LLMs) have become increasingly accessible through various web platforms....
Joao Queiroz
Recent evidence shows that the versification of prompts constitutes a highly effective adversarial mechanism against aligned LLMs. The study...
Pablo Montaña-Fernández, Ines Ortega-Fernandez
Federated Learning is a machine learning setting that reduces direct data exposure, improving the privacy guarantees of machine learning models. Yet,...
Xingfu Zhou, Pengfei Wang
Large Language Model (LLM) agents relying on external retrieval are increasingly deployed in high-stakes environments. While existing adversarial...
Yunhao Yao, Zhiqiang Wang, Haoran Cheng +3 more
The evolution of Large Language Models (LLMs) into Agentic AI has established the Model Context Protocol (MCP) as the standard for connecting...
Shuxin Zhao, Bo Lang, Nan Xiao +1 more
Object detection models deployed in real-world applications such as autonomous driving face serious threats from backdoor attacks. Despite their...
Sabrine Ennaji, Elhadj Benkhelifa, Luigi Vincenzo Mancini
Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs...
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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