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 661–680 of 866 papers
Clear filtersAram Vardanyan
Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings...
Zhijie Chen, Xiang Chen, Ziming Li +2 more
Context: Software Vulnerability Assessment (SVA) plays a vital role in evaluating and ranking vulnerabilities in software systems to ensure their...
Patrick Amadeus Irawan, Ikhlasul Akmal Hanif, Muhammad Dehan Al Kautsar +3 more
Although the cultural dimension has been one of the key aspects in evaluating Vision-Language Models (VLMs), their ability to remain stable across...
Yinjie Zhao, Heng Zhao, Bihan Wen +1 more
As the development of AI-generated contents (AIGC), multi-modal Large Language Models (LLM) struggle to identify generated visual inputs from real...
Chae-Gyun Lim, Seung-Ho Han, EunYoung Byun +51 more
The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric,...
Chunyang Li, Zifeng Kang, Junwei Zhang +4 more
The adoption of Vision-Language Models (VLMs) in embodied AI agents, while being effective, brings safety concerns such as jailbreaking. Prior work...
Wei Zhao, Zhe Li, Yige Li +1 more
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in cross-modal understanding, but remain vulnerable to adversarial...
Jingzhuo Zhou
The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing...
Saeefa Rubaiyet Nowmi, Jesus Lopez, Md Mahmudul Alam Imon +2 more
Quantum Machine Learning (QML) integrates quantum computational principles into learning algorithms, offering improved representational capacity and...
W. Bradley Knox, Katie Bradford, Samanta Varela Castro +6 more
Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to...
Henry Wong, Clement Fung, Weiran Lin +3 more
To autonomously control vehicles, driving agents use outputs from a combination of machine-learning (ML) models, controller logic, and custom...
Abolfazl Younesi, Leon Kiss, Zahra Najafabadi Samani +2 more
Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who...
Hongwei Liu, Junnan Liu, Shudong Liu +33 more
The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to...
Huiyi Chen, Jiawei Peng, Dehai Min +5 more
Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in...
Yuyang Xia, Ruixuan Liu, Li Xiong
Large language models (LLMs) perform in-context learning (ICL) by adapting to tasks from prompt demonstrations, which in practice often contain...
Longfei Chen, Ruibin Yan, Taiyu Wong +2 more
Smart contracts are prone to vulnerabilities and are analyzed by experts as well as automated systems, such as static analysis and AI-assisted...
Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
Contrastive vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition yet remain vulnerable to spurious correlations,...
Minjie Wang, Jinguang Han, Weizhi Meng
In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage...
Yikun Li, Matteo Grella, Daniel Nahmias +5 more
In recent years, Infrastructure as Code (IaC) has emerged as a critical approach for managing and provisioning IT infrastructure through code and...
Jiayu Li, Yunhan Zhao, Xiang Zheng +4 more
Vision-Language-Action (VLA) models enable robots to interpret natural-language instructions and perform diverse tasks, yet their integration of...
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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