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 101–120 of 1,175 papers
Clear filtersHongtao Wang, Se Yang, Yu Chen +1 more
Large language model (LLM) agents increasingly leverage long term memory to support persistent and autonomous task execution. However, this...
Leyi Qi, Yiming Li, Siyuan Liang +2 more
Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious...
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
LLM-based coding assistants are seeing rapid adoption, offering substantial gains in developer productivity. As organizations increasingly ship code...
Ihor Stepanov, Aleksandr Smechov
Real-time safety filtering for large language model (LLM) applications requires classifiers that can detect unsafe prompts, toxic language, jailbreak...
Junyoung Park, Sunghwan Park, Seongyong Ju +1 more
Attack Success Rate (ASR) evaluates each jailbreak with a single yes/no label at the end of generation, telling us whether a failure happened but not...
Mohan Zhang, Yuqi Jia, Zhen Tan +4 more
LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or...
Jinze Gu, Qinghua Mao, Xi Lin +1 more
Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora,...
Ziyang You, Huilong He, Xiaoke Yang +1 more
Cryptographic watermarking is a leading defense for attributing text generated by large language models (LLMs). Existing schemes, including KGW,...
Yaoyu Zhao, Yichen Xu, Oliver Bračevac +3 more
LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The...
Jaydip Sen
Artificial Intelligence has achieved remarkable success across diverse application domains. However, its vulnerability to adversarial attacks poses...
Matteo Gioele Collu, Riccardo Conte, Alberto Giaretta +4 more
In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained...
Avidan Shah, Jannik Brinkmann, Rico Angell
As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial...
Chenxi Wang, Ruiyang Huang, Jiayan Sun +2 more
Latent-based multi-agent systems replace parts of explicit inter-agent communication with hidden representations, offering a new direction for...
Yongxiang Li, Moxin Li, Zhixin Ma +4 more
Large Language Model (LLM) agents remain vulnerable to safety threats from the external environment, where attackers inject adversarial content into...
Ruoqi Guo, Yi Liu, Gelei Deng +7 more
Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from...
Junjie Mu, Qiongxiu Li
Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because raw data remain local. As a result,...
Jiachen Qian
Retrieval-Augmented Generation (RAG) mitigates LLM hallucinations but introduces a critical vulnerability: corpus integrity. We present...
Yu Yin, Shuai Wang, Bevan Koopman +1 more
Recent generative engine optimisation (GEO) research has shown that prompt-injection attacks can push a target product to the top of an LLM's...
Yuan Tian, Bing Hu, Fang Wu +3 more
Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly...
Xiang Fang, Wanlong Fang
Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms,...
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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