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 321–340 of 3,771 papers
Jaiden Fairoze, Neal Mangaokar, Kamalika Chaudhuri +2 more
For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial...
Ling Zhou, Yihao Huang, Jingling Sun +4 more
Large vision-language models (LVLMs) have achieved remarkable progress in video understanding and reasoning. Despite extensive studies on text- and...
Tenghui Huang, Jiawen Kang, Dongning Liu +5 more
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories...
Ye Tao, Hong Shen, Hui Tian +2 more
Gradient inversion attacks recover private training text from gradients shared in federated learning, posing a serious threat to collaborative model...
Avijit Gayen, Sayan Mondal, Angshuman Jana
In today's era of rapid advancements in artificial intelligence, computer security and online safeguarding measures have undergone significant...
Md Kibria Saroare, Md Rubel Ahmed
The rapid growth of large data-center (DC) loads is creating new challenges for power-system visibility, privacy, and cyber-physical security. System...
Krishna Teja Medam
A connected vehicle has roughly 100 milliseconds to decide whether an incoming Basic Safety Message is real or fabricated. If a false emergency...
Sepehr Ghaffarzadegan, Boubakr Nour, Makan Pourzandi +2 more
Mechanisms for dynamically converting cyber threat intelligence (CTI) into actionable detection capabilities are necessary due to the rapid evolution...
Benedikt Kluss, Niklas Bunzel
Continual Learning (CL) enables deep learning models to iteratively learn from a stream of data without forgetting prior knowledge. Existing...
Ilan Zini, Boussad Addad, Katarzyna Kapusta
Vision Language Models (VLMs) have recently emerged as a critical component of multimodal AI systems, enabling joint reasoning over visual and...
Kai Li, Jong-Ik Park, Carlee Joe-Wong +2 more
Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or...
Muris Sladić, Veronica Valeros, Eman Alibalić +1 more
LLM-based SSH honeypots often use closed cloud LLMs because they give strong shell realism, but cloud models create deployment problems. These...
Maosen Zhang, Jianshuo Dong, Boting Lu +5 more
LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However,...
Bilal Hussain, Xiao Tang, Qinghe Du +3 more
Deep learning-based DDoS detectors for 5G-enabled cyber-physical systems face scarce labeled attack data and unrealistic synthetic substitutes, which...
Xukun Luan, Jinyan Liu, Yuhui Gong +4 more
Vision-Language models (VLMs) achieve outstanding performance largely due to the amount of training data available on the internet. At the same time,...
Sujin Chen, Lijun Li, Tianyi Du +1 more
LLM-powered GUI agents that autonomously operate smartphones are rapidly transitioning from research prototypes to early real-world deployment....
Yajing Bai, Jinhao Duan, Jie Peng +4 more
Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external...
Istiaque Ahmed, Afia Anjum Borsha, Ranat Das Prangon +2 more
Large Language Models (LLMs) in real-world applications often face the risks of specially crafted prompts designed to bypass the safety controls....
Enrique Barba Roque, Luís Cruz, Annibale Panichella
Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems...
Sarvesh Gharat, Junpei Komiyama
Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experiments, and...
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.
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