Behavioral Fingerprints for LLM Endpoint Stability and Identity

2026-03-19

AI Summary

This work addresses the challenge that existing metrics struggle to capture behavioral drift in large language model (LLM) endpoints caused by changes in weights, tokenizers, quantization, or inference stacks—threatening the consistency of AI applications. To this end, we propose Stability Monitor, the first system that models LLM endpoint behavior through black-box behavioral fingerprinting. By periodically sampling outputs from a fixed prompt set, it constructs output distribution fingerprints and leverages energy distance combined with permutation testing to enable internal-agnostic stability monitoring and change detection. Experimental evaluation demonstrates that our approach effectively identifies differences across model families, versions, quantization schemes, and inference stacks, revealing significant stability disparities in real-world deployments across multiple providers.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The consistency of AI-native applications depends on the behavioral consistency of the model endpoints that power them. Traditional reliability metrics such as uptime, latency and throughput do not capture behavioral change, and an endpoint can remain "healthy" while its effective model identity changes due to updates to weights, tokenizers, quantization, inference engines, kernels, caching, routing, or hardware. We introduce Stability Monitor, a black-box stability monitoring system that periodically fingerprints an endpoint by sampling outputs from a fixed prompt set and comparing the resulting output distributions over time. Fingerprints are compared using a summed energy distance statistic across prompts, with permutation-test p-values as evidence of distribution shift aggregated sequentially to detect change events and define stability periods. In controlled validation, Stability Monitor detects changes to model family, version, inference stack, quantization, and behavioral parameters. In real-world monitoring of the same model hosted by multiple providers, we observe substantial provider-to-provider and within-provider stability differences.

Problem

• behavioral consistency
• model identity
• endpoint stability
• distribution shift
• LLM reliability

Innovation

• behavioral fingerprinting
• LLM stability monitoring
• distribution shift detection
• black-box monitoring
• model identity