The paper introduces Distributional Fingerprint Auditing (DFA), a statistical method for auditing large language model (LLM) relay services. DFA uses only textual responses from suspect and trusted endpoints, repeatedly querying simple finite-choice probes to build categorical response distributions that serve as fingerprints. By comparing these fingerprints probe‑by‑probe and aggregating evidence with the Cauchy combination test, DFA produces an approximate endpoint‑level p‑value for audit decisions at a chosen significance level, and further supports model attribution and substitution ratio estimation through a likelihood‑based framework.
By Fangran Miao, Shengjie Niu, Jian Huang, Ting Li
arXiv:2609.00052v1 Announce Type: cross
Abstract: Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to...
By Xun Wang, Bihe Zhao, Michael Backes, Franziska Boenisch, Adam Dziedzic
The paper presents a simple detector for model extraction attacks on large language model APIs. It frames detection as a benign‑calibrated traffic‑window distribution test, embedding queries into a semantic space and using maximum mean discrepancy (MMD) to compare against historical benign traffic. Evaluated on fourteen attacker‑normal query pairs across four extraction scenarios, MMD achieves near‑perfect true‑positive rates while maintaining a 0.3% false‑positive rate, outperforming several existing baselines.
By Shuze Liu, Qianwen Guo, Yushun Dong
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:2608.21895v1 Announce Type: cross
Abstract: Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-serv...
By Aaditya Pratap, Harsh Kasyap, Somanath Tripathy
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
CascadeLedger is a counterfactual execution‑audit framework designed for budgeted language‑model cascades. It enforces call‑before‑read semantics, records service and cache identities, and freezes event traces before scoring, ensuring that two executions can share a final answer and resource projection only if their routing decisions used legitimately observable information. The framework introduces counterfactual prefix non‑interference (CPNI), counterfactual influence frontier (CIF), and projection‑preserving adversarial mutation (PPAM) to detect and localize violations, achieving a clean false‑positive rate and high detection rates for serialized and hidden‑read faults while quantifying overhead for online and offline audits.
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Daren Zha, Jun Xiao
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
RouteScan is a non‑intrusive auditing framework that detects harmful behavior in Mixture‑of‑Experts (MoE) large language models by analyzing expert‑routing telemetry captured from GPU execution. It uses the number of active GPU threads during the prefilling phase as a micro‑architectural fingerprint to isolate cross‑domain risk indicators and precisely identify malicious prompts. Evaluations on four open‑source MoE LLMs show strong generalization with AUROC > 0.91 on unseen harmful domains, while privacy tests indicate that full prompts cannot be reliably recovered from aggregated telemetry.
By Bo Lv, Zhiheng Xu, KeDong Xiu, Ruyi Ding, Tianhang Zheng, Zhibo Wang, Kui Ren
arXiv:2609.31877v2 Announce Type: replace-cross
Abstract: Cloud-based Large language model (LLM) services create a network-level traffic side channel that can expose model, prompt, and task behavior...
By Shahrooz Pouryousef, Jesus Lopez, Saeefa Rubaiyet Nowmi, Md Mahmuduzzaman Kamol, Moinul Hossain, Muoi Tran, Mohammad Saidur Rahman
The paper introduces a privacy‑preserving zk‑SNARK audit framework that uses adversarial‑style probes to detect logit drift between an approved large language model and a modified deployment. It offers three probe families—token‑based (black‑box), embedding‑based (gray‑box), and stress probes (partial white‑box)—allowing users to balance sensitivity, access, and cost. Experiments across LLM architectures and GPU platforms show token‑based probes achieve the highest mean sensitivity while remaining practical in a black‑box setting, with Groth16 proving times scaling modestly from 1.02 to 1.78 seconds and constant proof size.
By Cameron Wilding, Mina Shaker, Fatemeh Ganji