arXiv:2607. 10252v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly consumed through opaque serving chains - API aggregators, resellers, and inference providers - in which the client has no technical means to confirm that the model answering is the model advertised, and recent audits show that a substantial fraction of commercial endpoints deviate from the vendor's reference weights.
By Tomas Bruckner
arXiv:2606. 16358v1 Announce Type: cross Abstract: Agents increasingly access large language models (LLMs) through API routers.
By Sipeng Xie, Qianhong Wu, Hengrun Lu, Ziliang Sun, Qi Wu, Bo Qin, Qin Wang
arXiv:2603. 07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs).
By Heng Jin, Chaoyu Zhang, Hexuan Yu, Shanghao Shi, Ning Zhang, Y. Thomas Hou, Wenjing Lou
arXiv:2605. 02187v2 Announce Type: replace-cross Abstract: LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions.
By Mingyu Luo, Zihan Zhang, Zesen Liu, Yuchong Xie, Zhixiang Zhang, Dung Hiu Hilton Yeung, Wai Ip Lai, Ping Chen, Ming Wen, Dongdong She
arXiv:2606. 16100v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become ubiquitous, users increasingly rely on black-box fingerprinting to verify that providers are serving the advertised premium models.
By Jiahao Zhang, Xiuyu Li, Suhang Wang
arXiv:2607. 01313v1 Announce Type: cross Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures.
By Christopher Ellis, Shreyas Chaudhari, Mei-Yu Wang, Leighton Barnes, Giulia Fanti, Jos\'e M. F. Moura
arXiv:2603. 23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent.
By Toluwani Aremu, Daniil Ognev, Samuele Poppi, Nils Lukas
arXiv:2407. 10887v4 Announce Type: replace-cross Abstract: Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse.
By Mark Russinovich, Yanan Cai, Ahmed Salem
arXiv:2607. 10490v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages can carry indirect prompt injections.
By Ruksat Khan Shayoni, Muhammad Faraz Shoaib, S M Asif Hossain, M. F. Mridha
arXiv:2607. 19490v1 Announce Type: cross Abstract: Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes.
By Mert Cihangiroglu, Antonino Nocera
arXiv:2606. 15810v1 Announce Type: cross Abstract: Large language models deployed as commercial APIs are vulnerable to model extraction attacks, while existing defenses either act too late or degrade utility for legitimate users.
By Yuyang Dai, Yushun Dong
arXiv:2606. 05252v1 Announce Type: cross Abstract: Security teams routinely simulate attacks against their own systems to check whether their monitoring would catch a real intruder.
By Alexandre Cristov\~ao Maiorano