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 281–300 of 748 papers
Clear filtersPeter Horvath, Ilia Shumailov, Lukasz Chmielewski +2 more
The multi-million dollar investment required for modern machine learning (ML) has made large ML models a prime target for theft. In response, the...
Jiayao Wang, Mohammad Maruf Hasan, Yiping Zhang +5 more
Self-Supervised Learning (SSL) has emerged as a significant paradigm in representation learning thanks to its ability to learn without extensive...
Huw Day, Adrianna Jezierska, Jessica Woodgate
Large Language Models have intensified the scale and strategic manipulation of political discourse on social media, leading to conflict escalation....
Duoxun Tang, Dasen Dai, Jiyao Wang +3 more
Video-LLMs are increasingly deployed in safety-critical applications but are vulnerable to Energy-Latency Attacks (ELAs) that exhaust computational...
Xinyu Huang, Qiang Yang, Leming Shen +2 more
Embodied Large Language Models (LLMs) enable AI agents to interact with the physical world through natural language instructions and actions....
Jiayao Wang, Yiping Zhang, Mohammad Maruf Hasan +5 more
Self-supervised diffusion models learn high-quality visual representations via latent space denoising. However, their representation layer poses a...
Oluseyi Olukola, Nick Rahimi
Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both...
Hsin Lin, Yan-Lun Chen, Ren-Hung Hwang +1 more
Backdoor attacks pose a critical threat to the security of deep neural networks, yet existing efforts on universal backdoors often rely on visually...
Yilian Liu, Xiaojun Jia, Guoshun Nan +6 more
Multimodal Large Language Models (MLLMs) have achieved remarkable performance but remain vulnerable to jailbreak attacks that can induce harmful...
Swapnil Parekh
Image captioning models are encoder-decoder architectures trained on large-scale image-text datasets, making them susceptible to adversarial attacks....
Linxi Jiang, Zhijie Liu, Haotian Luo +1 more
Browser-use agents are widely used for everyday tasks. They enable automated interaction with web pages through structured DOM based interfaces or...
Kennedy Edemacu, Mohammad Mahdi Shokri
Retrieval-augmented generation (RAG) has emerged as a powerful paradigm for enhancing multimodal large language models by grounding their responses...
Xun Huang, Simeng Qin, Xiaoshuang Jia +6 more
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are...
Tian Zhang, Yiwei Xu, Juan Wang +8 more
Large language model (LLM) agents increasingly rely on external tools and retrieval systems to autonomously complete complex tasks. However, this...
Marcus Graves
We introduce Reverse CAPTCHA, an evaluation framework that tests whether large language models follow invisible Unicode-encoded instructions embedded...
Zhonghao Zhan, Krinos Li, Yefan Zhang +1 more
Edge deployment of LLM agents on IoT hardware introduces attack surfaces absent from cloud-hosted orchestration. We present an empirical security...
Qianlong Lan, Anuj Kaul, Shaun Jones +1 more
Agentic large language model systems increasingly automate tasks by retrieving URLs and calling external tools. We show that this workflow gives rise...
Zheng Gao, Xiaoyu Li, Zhicheng Bao +2 more
Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has...
Piyush Jaiswal, Aaditya Pratap, Shreyansh Saraswati +2 more
Large Language Models (LLMs) are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient...
Che Wang, Fuyao Zhang, Jiaming Zhang +6 more
Large Language Model (LLM) agents are susceptible to Indirect Prompt Injection (IPI) attacks, where malicious instructions in retrieved content...
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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