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 81–100 of 1,257 papers
Clear filtersAndrey Labunets
Refusal training protects AI models from jailbreaks by training models to decline unsafe queries, reducing the risk of misuse. Recent work finds that...
Huakang Lin, Tiancheng Zheng, Mingxuan Sun +4 more
Mixture-of-Experts (MoE) architectures enable scalable and efficient large language models (LLMs) by selectively activating expert sub-networks...
Zuobin Xiong, Deval Mukherjee, Homook Cho +1 more
The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of...
Minh Tran, Cuong Dang, Tuc Nguyen +9 more
Retrieval-Augmented Generation (RAG) enhances large language models by grounding outputs in external knowledge, improving factuality and reducing...
Dimitri Galli, Andrea Venturi, Dario Stabili +2 more
Graph Neural Networks (GNNs) represent a promising solution for Machine Learning (ML) based Network Intrusion Detection Systems (NIDS), thanks to...
Joshua Penman
Everything a language model sees is tokens. The serving stack knows what each span is -- user input, tool output, instructions -- but the model must...
Mehrdad Rostamzadeh, Sidhant Narula, Mohammad Ghasemigol +1 more
The Model Context Protocol (MCP) has emerged as the standard layer connecting Large Language Model agents to external tool backends. This openness...
Hanling Tian, Gengyu Zhang, Zeyang Sha +5 more
Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a...
Zeyu Feng, Qingyu Wu, Yuzhe Luo +1 more
Large language models (LLMs) are increasingly deployed in education, healthcare, policy advising, and other interactive settings, where users engage...
Wenyun Li, Guiping Cao, Xiangyuan Lan +1 more
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language interaction, yet their safety alignment remains...
Roshan Sood, Onat Gungor, Tajana Rosing
LLMs remain vulnerable to prompt injection attacks, where adversarial instructions embedded in user inputs or external content manipulate model...
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...
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...
Benedikt Kluss, Niklas Bunzel
Continual Learning (CL) enables deep learning models to iteratively learn from a stream of data without forgetting prior knowledge. Existing...
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...
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....
Bowen Sun, Zhengyue Zhao, Xiaogeng Liu +2 more
Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attacks...
Zhida He, Xiaoyu Wen, Han Qi +5 more
Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) without...
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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