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 381–400 of 748 papers
Clear filtersSeyed Mohammad Hadi Hosseini, Amir Najafi, Mahdieh Soleymani Baghshah
Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large...
Haobo Wang, Weiqi Luo, Xiaojun Jia +1 more
Large vision-language models (VLMs) are vulnerable to transfer-based adversarial perturbations, enabling attackers to optimize on surrogate models...
Xiaoyu Wen, Zhida He, Han Qi +7 more
Ensuring robust safety alignment is crucial for Large Language Models (LLMs), yet existing defenses often lag behind evolving adversarial attacks due...
Ziyue Wang, Jiangshan Yu, Kaihua Qin +3 more
Decentralized Finance (DeFi) has turned blockchains into financial infrastructure, allowing anyone to trade, lend, and build protocols without...
Terry Yue Zhuo, Yangruibo Ding, Wenbo Guo +1 more
For over a decade, cybersecurity has relied on human labor scarcity to limit attackers to high-value targets manually or generic automated attacks at...
Kaiyuan Cui, Yige Li, Yutao Wu +4 more
Vision-language models (VLMs) extend large language models (LLMs) with vision encoders, enabling text generation conditioned on both images and text....
Xueyi Li, Zhuoneng Zhou, Zitao Liu +2 more
Large language models (LLMs) have demonstrated remarkable potential for automatic short answer grading (ASAG), significantly boosting student...
Licheng Pan, Yunsheng Lu, Jiexi Liu +5 more
Uncovering the mechanisms behind "jailbreaks" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these...
Md Jahedur Rahman, Ihsen Alouani
Large language models (LLMs) are increasingly used in interactive and retrieval-augmented systems, but they remain vulnerable to task drift;...
Yuxuan Lu, Yongkang Guo, Yuqing Kong
Safety alignment in Large Language Models (LLMs) often creates a systematic discrepancy between a model's aligned output and the underlying...
Yihang Chen, Zhao Xu, Youyuan Jiang +2 more
Large Vision-Language Models (LVLMs) are increasingly equipped with robust safety safeguards to prevent responses to harmful or disallowed prompts....
Jiate Li, Defu Cao, Li Li +8 more
Large language models (LLMs) have been serving as effective backbones for retrieval systems, including Retrieval-Augmentation-Generation (RAG), Dense...
Kunal Mukherjee, Zulfikar Alom, Tran Gia Bao Ngo +2 more
The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely...
Ye Yu, Haibo Jin, Yaoning Yu +2 more
Large audio-language models increasingly operate on raw speech inputs, enabling more seamless integration across domains such as voice assistants,...
Zhixiang Zhang, Zesen Liu, Yuchong Xie +2 more
Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsoft. By...
Tanusree Debi, Wentian Zhu
Large language model (LLM) based agents are increasingly used to automate financial transactions, yet their reliance on contextual reasoning exposes...
Naen Xu, Jinghuai Zhang, Ping He +6 more
Large language models (LLMs) have been widely integrated into critical automated workflows, including contract review and job application processes....
Aarush Noheria, Yuguang Yao
Vision-language models (VLMs) have become central to tasks such as visual question answering, image captioning, and text-to-image generation....
Javier Carnerero-Cano, Luis Muñoz-González, Phillippa Spencer +1 more
Regression models are widely used in industrial processes, engineering, and in natural and physical sciences, yet their robustness to poisoning has...
Xiaogeng Liu, Xinyan Wang, Yechao Zhang +5 more
Large reasoning models (LRMs) extend large language models with explicit multi-step reasoning traces, but this capability introduces a new class of...
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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