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 3001–3020 of 3,023 papers
Prakhar Sharma, Haohuang Wen, Vinod Yegneswaran +3 more
The evolution toward 6G networks is being accelerated by the Open Radio Access Network (O-RAN) paradigm -- an open, interoperable architecture that...
Haibo Tong, Dongcheng Zhao, Guobin Shen +4 more
The remarkable capabilities of Large Language Models (LLMs) have raised significant safety concerns, particularly regarding "jailbreak" attacks that...
Wei Huang, De-Tian Chu, Lin-Yuan Bai +6 more
Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns...
Jiahao Huo, Shuliang Liu, Bin Wang +5 more
Semantic-level watermarking (SWM) for large language models (LLMs) enhances watermarking robustness against text modifications and paraphrasing...
Runqi Lin, Alasdair Paren, Suqin Yuan +4 more
The integration of new modalities enhances the capabilities of multimodal large language models (MLLMs) but also introduces additional...
Ping He, Changjiang Li, Binbin Zhao +2 more
The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize...
Panagiotis Michelakis, Yiannis Hadjiyiannis, Dimitrios Stamoulis
Evaluating AI agents that solve real-world tasks through function-call sequences remains an open challenge. Existing agentic benchmarks often reduce...
Hanbo Huang, Yiran Zhang, Hao Zheng +4 more
Large Language Models (LLMs) watermarking has shown promise in detecting AI-generated content and mitigating misuse, with prior work claiming...
Anh Tu Ngo, Anupam Chattopadhyay, Subhamoy Maitra
In this paper we show that cryptographic backdoors in a neural network (NN) can be highly effective in two directions, namely mounting the attacks as...
Wenkai Guo, Xuefeng Liu, Haolin Wang +3 more
Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific...
Adam Swanda, Amy Chang, Alexander Chen +3 more
The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across...
Maria Chiper, Radu Tudor Ionescu
Phishing attacks targeting both organizations and individuals are becoming an increasingly significant threat as technology advances. Current...
Dana A Abdullah, Dana Rasul Hamad, Bishar Rasheed Ibrahim +3 more
Altered fingerprint recognition (AFR) is challenging for biometric verification in applications such as border control, forensics, and fiscal...
Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi +1 more
Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external...
Xiaofan Li, Xing Gao
In recent years, various software supply chain (SSC) attacks have posed significant risks to the global community. Severe consequences may arise if...
Wenhan Wu, Zheyuan Liu, Chongyang Gao +2 more
Current LLM unlearning methods face a critical security vulnerability that undermines their fundamental purpose: while they appear to successfully...
Tanmay Khule, Stefan Marksteiner, Jose Alguindigue +3 more
In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are...
Lauren Deason, Adam Bali, Ciprian Bejean +20 more
Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an...
Md Jueal Mia, M. Hadi Amini
Vision-Language Models (VLMs) have remarkable abilities in generating multimodal reasoning tasks. However, potential misuse or safety alignment...
Xiyu Zeng, Siyuan Liang, Liming Lu +5 more
As the capabilities of Vision Language Models (VLMs) continue to improve, they are increasingly targeted by jailbreak attacks. Existing defense...
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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AI Threat Alert