KBF: Knowledge Boundary as Fingerprint for Language Model and Black-Box API Auditing

Researchers introduce KBF, a black-box auditing protocol that detects fraudulent LLM API substitutions by analyzing model behavior at knowledge boundaries. Testing across 16 production endpoints revealed all economically relevant model swaps without false positives, and identified inconsistencies in 7 of 27 model cells across major AI platforms, particularly affecting Claude premium endpoints.

The proliferation of API intermediaries accessing large language models creates a critical trust gap in the AI infrastructure layer. Users purchasing access through resellers and relay services have no reliable way to verify they're actually receiving the advertised model, creating economic vulnerability and enabling potential bait-and-switch attacks. KBF addresses this by establishing a novel fingerprinting mechanism based on stable numerical recall patterns near the knowledge boundary—the threshold where models transition from confident to uncertain responses. This approach provides a practical, low-cost detection method without requiring model internals access.

The research builds on growing concerns about API integrity in the AI services market. As LLM access becomes commoditized through multiple distribution channels, the risk of model substitution increases proportionally. Bad actors can profit significantly by replacing premium models with cheaper alternatives, creating misaligned incentives throughout the supply chain. The shadow audit findings—revealing inconsistencies in major platform offerings—suggest this problem extends beyond theoretical scenarios into current production systems.

For the AI infrastructure ecosystem, KBF introduces accountability mechanisms previously absent from black-box API environments. The detection of high-separation mixed-routing attacks (traffic substitution at 5-10% levels) indicates the protocol maintains sensitivity even under sophisticated evasion attempts. The concentration of inconsistencies around premium Claude endpoints raises questions about deployment standardization and quality control at scale. This research empowers users to audit their service providers independently, potentially reshaping how SLAs and pricing are structured in the API access market.

• →KBF detected 100% of economically relevant LLM model substitutions across 16 production endpoints with zero false positives on legitimate models
• →Shadow audit found 26% of platform model cells showing statistical inconsistencies with reference endpoints, concentrated on premium Claude offerings
• →Protocol detects mixed-routing attacks where only 5-10% of traffic is substituted, demonstrating robust fingerprinting under adversarial conditions
• →Low-cost black-box auditing eliminates need for API provider cooperation or internal model access to verify service claims
• →Findings suggest widespread integrity issues in AI API distribution channels that current market mechanisms fail to detect or prevent