Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways

TL;DR

This paper introduces GateScope, a framework for auditing commercial LLM API gateways to evaluate their behavioral consistency and transparency, revealing frequent discrepancies and operational gaps.

Contribution

The paper presents GateScope, a novel black-box measurement tool for detecting misbehaviors and transparency issues in commercial LLM gateways.

Findings

Frequent silent model substitutions and downgrades were observed.

Deviations from announced pricing policies were common.

Latency stability varied significantly across platforms.

Abstract

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.