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