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 901–920 of 950 papers
Clear filtersKonstantinos E. Kampourakis, Vyron Kampourakis, Efstratios Chatzoglou +2 more
Realistic, large-scale, and well-labeled cybersecurity datasets are essential for training and evaluating Intrusion Detection Systems (IDS). However,...
Mizuki Sakai, Mizuki Yokoyama, Wakaba Tateishi +1 more
Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that...
Huawei Zheng, Xinqi Jiang, Sen Yang +3 more
Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare, where they introduce unique safety...
Han Zhu, Jiale Chen, Chengkun Cai +8 more
Multi-modal Large Language Models (MLLMs) are increasingly deployed in interactive applications. However, their safety vulnerabilities become...
Zhilun Zhou, Zihan Liu, Jiahe Liu +5 more
Large Language Model-based Multi-Agent Systems (LLM-based MAS), where multiple LLM agents collaborate to solve complex tasks, have shown impressive...
Saad Alqithami
Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step...
Mohamed Nabeel, Oleksii Starov
According to Gartner, more than 70% of organizations will have integrated AI models into their workflows by the end of 2025. In order to reduce cost...
Sahaya Jestus Lazer, Kshitiz Aryal, Maanak Gupta +1 more
Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over...
San Kim, Gary Geunbae Lee
Large Language Models (LLMs) have greatly advanced Natural Language Processing (NLP), particularly through instruction tuning, which enables broad...
Andreea-Elena Bodea, Stephen Meisenbacher, Alexandra Klymenko +1 more
The continued promise of Large Language Models (LLMs), particularly in their natural language understanding and generation capabilities, has driven a...
Xiaoyu Xu, Minxin Du, Zitong Li +6 more
Although machine unlearning is essential for removing private, harmful, or copyrighted content from LLMs, current benchmarks often fail to faithfully...
Xing Li, Hui-Ling Zhen, Lihao Yin +3 more
This paper presents a comprehensive empirical study on the safety alignment capabilities. We evaluate what matters for safety alignment in LLMs and...
Dinesh Srivasthav P, Ashok Urlana, Rahul Mishra +2 more
Machine unlearning aims to selectively remove the influence of specific training samples to satisfy privacy regulations such as the GDPR's 'Right to...
Di Wu, Yanyan Zhao, Xin Lu +2 more
Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve...
Sofie Goethals, Foster Provost, João Sedoc
As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their...
Bocheng Chen, Xi Chen, Han Zi +5 more
Identifying specific moral errors in an input and generating appropriate corrections require moral sensitivity in large language models (LLMs), which...
Gaurav Sarraf, Vibhor Pal
Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious...
Ahmed Ahmed, A. Feder Cooper, Sanmi Koyejo +1 more
Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's...
Md Ajoad Hasan, Dipayan Saha, Khan Thamid Hasan +5 more
The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental steps...
Neusha Javidnia, Ruisi Zhang, Ashish Kundu +1 more
We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLM owners by...
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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