arxiv_ml 95% Match Research Paper AI developers,Legal teams,AI governance professionals,Platform providers 1 week ago

Model Provenance Testing for Large Language Models

large-language-models › evaluation

📄 Abstract

Abstract: Large language models are increasingly customized through fine-tuning and other adaptations, creating challenges in enforcing licensing terms and managing downstream impacts. Tracking model origins is crucial both for protecting intellectual property and for identifying derived models when biases or vulnerabilities are discovered in foundation models. We address this challenge by developing a framework for testing model provenance: Whether one model is derived from another. Our approach is based on the key observation that real-world model derivations preserve significant similarities in model outputs that can be detected through statistical analysis. Using only black-box access to models, we employ multiple hypothesis testing to compare model similarities against a baseline established by unrelated models. On two comprehensive real-world benchmarks spanning models from 30M to 4B parameters and comprising over 600 models, our tester achieves 90-95% precision and 80-90% recall in identifying derived models. These results demonstrate the viability of systematic provenance verification in production environments even when only API access is available.

Authors (3)

Ivica Nikolic

Teodora Baluta

Prateek Saxena

Submitted

February 2, 2025

arXiv Category

cs.CR

arXiv PDF

Key Contributions

Develops a novel framework for testing model provenance (whether one model is derived from another) using only black-box access. The approach relies on statistical analysis of model output similarities, achieving high precision and recall on real-world benchmarks.

Business Value

Provides a crucial tool for protecting intellectual property, ensuring compliance with licensing agreements, and maintaining accountability in the rapidly evolving LLM ecosystem.

Paper Metadata

Innovation Type

Novel Testing Methodology

Deployment Feasibility

High, as it operates via black-box access, making it applicable to a wide range of proprietary and open-source models.

Limitations Addressed

Addresses the challenge of tracking model origins and enforcing licensing terms for LLMs that are frequently customized through fine-tuning.

Performance Gains

Achieves 90-95% precision and 80-90% recall on benchmarks covering models from 30M to 4B parameters.

Technical Tags

Model provenanceLarge Language Models (LLMs)Fine-tuningDerived modelsIntellectual propertyBlack-box testingMultiple hypothesis testingModel output similarityFoundation models

Research Topics

AI EthicsIntellectual PropertyModel SecurityMachine Learning AuditingLarge Language Models

Methods & Architectures

Black-box model comparisonMultiple hypothesis testingStatistical analysis of model outputs Large Language Models (LLMs)

Applications & Tasks

AI Governance Software Licensing Model Auditing Model Derivation VerificationIP ProtectionAttribution Tracking Determining if one model is derived from anotherEnforcing licensing terms for LLMsTracking lineage of customized LLMs

Datasets & Benchmarks

Datasets

Real-world benchmarks

Benchmarks

90-95% precision • 80-90% recall

PrecisionRecallAccuracy

Related Fields

AI EthicsMachine LearningSoftware EngineeringIntellectual Property LawCybersecurity

Keywords

model provenanceLLMfine-tuningintellectual propertylicensingblack-box testingderived modelsattributionauditingfoundation modelsAI governance

Academic Context

#AI Ethics#Intellectual Property#Model Security#Machine Learning Auditing#Large Language Models

Technology Stack

ML Infrastructure

Model testing frameworks

Commercial Potential

Potential Products

Model provenance verification serviceIP protection tools for AI modelsCompliance auditing software

Target Industries

TechnologySoftware DevelopmentLegal ServicesAI Platform Providers

Use Case Examples

Verifying if a commercial LLM was trained using unauthorized data from a proprietary foundation model.Ensuring that fine-tuned models adhere to the usage restrictions of the base model.

Competitive Edge

Offers a unique solution for model provenance testing, addressing a critical gap in managing and securing customized LLMs.

Market Opportunity

Growing need for AI governance and IP protection tools.

Revenue Models

Service feeslicensing of the testing framework.

Resource Requirements

Compute Needs

Moderate, requires running inference on multiple models for comparison.

Data Requirements

Requires access to the models being tested (black-box) and potentially a set of reference models.

Deployment Constraints

Requires careful selection of test prompts and statistical analysis methods.

Scalability

Scalable to large numbers of models and parameters, as it relies on black-box testing.

Regulatory Considerations

Potential legal implications regarding IP infringement detection.

Production Readiness

Maturity Level

Research/Tool Development

Time to Market

1-2 years for integration into auditing tools.

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