Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways

Computer Science > Cryptography and Security [Submitted on 22 Apr 2026] Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways Guanjie Lin, Yinxin Wan, Shichao Pei, Ting Xu, Kuai Xu, Guoliang Xue Third-party Large Language Model (LLM) API gateways are rapidly emerging as unified access points to models offered by multiple vendors. However, the internal routing, caching, and billing policies of these gateways are largely undisclosed, leaving users with limited visibility into whether requests are served by the advertised models, whether responses remain faithful to upstream APIs, or whether invoices accurately reflect public pricing policies. To address this gap, we introduce GateScope, a lightweight black-box measurement framework for evaluating behavioral consistency and operational transparency in commercial LLM gateways. GateScope is designed to detect key misbehaviors, including model downgrading or switching, silent truncation, billing inaccuracies, and instability in latency by auditing gateways along four critical dimensions: response content analysis, multi-turn conversation performance, billing accuracy, and latency characteristics. Our measurements across 10 real-world commercial LLM API gateways reveal frequent gaps between expected and actual behaviors, including silent model substitutions, degraded memory retention, deviations from announced pricing, and substantial variation in latency stability across platforms. Comments: 11 pages. Initially submitted to IMC 2026 Cycle 1 on November 20, 2025; accepted on March 13, 2026. To appear in Proceedings of the 2026 ACM Internet Measurement Conference (IMC '26) Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); Software Engineering (cs.SE) Cite as: arXiv:2604.21083 [cs.CR]   (or arXiv:2604.21083v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.21083 Focus to learn more Submission history From: Guanjie Lin [view email] [v1] Wed, 22 Apr 2026 20:51:20 UTC (903 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.NI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)