AI Threat Alert indexes 3,371+ 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 1281–1300 of 1,303 papers
Clear filtersJeongyeon Hwang, Sangdon Park, Jungseul Ok
Watermarking offers a promising solution for detecting LLM-generated content, yet its robustness under realistic query-free (black-box) evasion...
Aashnan Rahman, Abid Hasan, Sherajul Arifin +5 more
Federated learning (FL) enables privacy-preserving model training by keeping data decentralized. However, it remains vulnerable to label-flipping...
Roie Kazoom, Yuval Ratzabi, Etamar Rothstein +1 more
Adversarial robustness in structured data remains an underexplored frontier compared to vision and language domains. In this work, we introduce a...
Hwan Chang, Yonghyun Jun, Hwanhee Lee
The growing deployment of large language model (LLM) based agents that interact with external environments has created new attack surfaces for...
Xingyu Li, Juefei Pu, Yifan Wu +13 more
Open-source software projects are foundational to modern software ecosystems, with the Linux kernel standing out as a critical exemplar due to its...
Wonjun Lee, Haon Park, Doehyeon Lee +2 more
Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While recent...
David Benfield, Stefano Coniglio, Phan Tu Vuong +1 more
Adversarial machine learning concerns situations in which learners face attacks from active adversaries. Such scenarios arise in applications such as...
Aravindhan G, Yuvaraj Govindarajulu, Parin Shah
Recent studies have demonstrated the vulnerability of Automatic Speech Recognition systems to adversarial examples, which can deceive these systems...
Yue Liu, Yanjie Zhao, Yunbo Lyu +3 more
Agentic AI coding editors driven by large language models have recently become more popular due to their ability to improve developer productivity...
Taeyoung Yun, Pierre-Luc St-Charles, Jinkyoo Park +2 more
We address the challenge of generating diverse attack prompts for large language models (LLMs) that elicit harmful behaviors (e.g., insults, sexual...
Jingkai Guo, Chaitali Chakrabarti, Deliang Fan
Model integrity of Large language models (LLMs) has become a pressing security concern with their massive online deployment. Prior Bit-Flip Attacks...
Miao Yu, Zhenhong Zhou, Moayad Aloqaily +5 more
Fine-tuned Large Language Models (LLMs) are vulnerable to backdoor attacks through data poisoning, yet the internal mechanisms governing these...
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...
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...
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...
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...
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...
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 3,371+ 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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