AI Threat Alert indexes 3,771+ 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 3041–3060 of 3,771 papers
Adarsh Kumarappan, Ananya Mujoo
Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves the way...
Jiangrui Zheng, Yingming Zhou, Ali Abdullah Ahmad +2 more
Bug bounty platforms (e.g., HackerOne, BugCrowd) leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of...
Yanxi Li, Ruocheng Shan
Large language models are increasingly used for text classification tasks such as sentiment analysis, yet their reliance on natural language prompts...
Yanting Wang, Runpeng Geng, Jinghui Chen +2 more
Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate...
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1 more
The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant...
Tyler Shoemaker
This position paper argues that literary scholars must engage with large language model (LLM) interpretability research. While doing so will involve...
Xiaoqing Wang, Keman Huang, Bin Liang +2 more
The rapid advancement of Large Language Model (LLM)-driven multi-agent systems has significantly streamlined software developing tasks, enabling...
Xiangrui Zhang, Zeyu Chen, Haining Wang +1 more
Large Language Models (LLMs) and their agent systems have recently demonstrated strong potential in automating code reasoning and vulnerability...
Qingsong He, Jing Nan, Jiayu Jiao +5 more
Large Language Models can break through knowledge and timeliness limitations by invoking external tools within the Model Context Protocol framework...
Swastik Bhattacharya, Sanjay Das, Anand Menon +3 more
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these...
Mohamed Afane, Ying Wang, Juntao Chen
Public health agencies face critical challenges in identifying high-risk neighborhoods for childhood lead exposure with limited resources for...
Saeid Jamshidi, Amin Nikanjam, Negar Shahabi +4 more
As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in...
Pinaki Prasad Guha Neogi, Ahmad Mohammadshirazi, Dheeraj Kulshrestha +1 more
Mixture-of-Experts (MoE) architectures are increasingly adopted in large language models (LLMs) for their scalability and efficiency. However, their...
Junrui Zhang, Xinyu Zhao, Jie Peng +3 more
Multimodal learning has shown significant superiority on various tasks by integrating multiple modalities. However, the interdependencies among...
Itay Hazan, Yael Mathov, Guy Shtar +2 more
Securing AI agents powered by Large Language Models (LLMs) represents one of the most critical challenges in AI security today. Unlike traditional...
Aram Vardanyan
Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings...
Oluleke Babayomi, Dong-Seong Kim
Electric Vehicle (EV) charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid...
Adela Bara, Simona-Vasilica Oprea
Our paper introduces a generative, multiagent AI framework designed to overcome the rigidity, limited flexibility and technical barriers of current...
Yunyi Zhang, Shibo Cui, Baojun Liu +4 more
LLM applications (i.e., LLM apps) leverage the powerful capabilities of LLMs to provide users with customized services, revolutionizing traditional...
Zhiyuan Xu, Stanislav Abaimov, Joseph Gardiner +1 more
Modern large language models (LLMs) are typically secured by auditing data, prompts, and refusal policies, while treating the forward pass as an...
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 3,771+ 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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert