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 601–620 of 759 papers
Clear filtersChing-Yun Ko, Pin-Yu Chen
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for...
Pengfei He, Ash Fox, Lesly Miculicich +7 more
Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability...
Jiayao Wang, Yang Song, Zhendong Zhao +5 more
Federated self-supervised learning (FSSL) enables collaborative training of self-supervised representation models without sharing raw unlabeled data....
Mingrui Liu, Sixiao Zhang, Cheng Long +1 more
Large Language Models (LLMs) are increasingly vulnerable to Prompt Injection (PI) attacks, where adversarial instructions hidden within retrieved...
Pengyu Li, Lingling Zhang, Zhitao Gao +5 more
While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and...
Seyed 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...
Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1 more
Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved...
Eliron Rahimi, Elad Hirshel, Rom Himelstein +3 more
Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering parallel decoding and...
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...
Xinyi Hou, Shenao Wang, Yifan Zhang +4 more
Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that...
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...
Manveer Singh Tamber, Hosna Oyarhoseini, Jimmy Lin
Research on adversarial robustness in language models is currently fragmented across applications and attacks, obscuring shared vulnerabilities. In...
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....
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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