Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways
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| Format: | Preprint |
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2026
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| author | Lin, Guanjie Wan, Yinxin Pei, Shichao Xu, Ting Xu, Kuai Xue, Guoliang |
| author_facet | Lin, Guanjie Wan, Yinxin Pei, Shichao Xu, Ting Xu, Kuai Xue, Guoliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21083 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways Lin, Guanjie Wan, Yinxin Pei, Shichao Xu, Ting Xu, Kuai Xue, Guoliang Cryptography and Security Artificial Intelligence Networking and Internet Architecture Software Engineering 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. |
| title | Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways |
| topic | Cryptography and Security Artificial Intelligence Networking and Internet Architecture Software Engineering |
| url | https://arxiv.org/abs/2604.21083 |