Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
arXiv:2604.04738v2 Announce Type: replace-cross Abstract: Fine-tuning is the dominant paradigm for adapting large machine learning models, yet current deployment pipelines provide no way to verify how a released model was updated. In particular, a model provider or auditor cannot check whether a fine-tuned model adheres to a claimed update procedure without access to its parameters. We introduce \emph{fine-tuning integrity} (FTI), a cryptographic objective for verifying that a deployed model differs from a trusted base model only within a declared class of admissible updates. We construct \emp
The proliferation of fine-tuned large models necessitates new integrity checks, especially as these models move into critical applications and the supply chain becomes more complex.
This development addresses a critical trust and security gap in the AI model supply chain, ensuring that fine-tuned models adhere to declared update procedures and preventing unauthorized modifications.
The ability to cryptographically verify fine-tuning integrity will introduce greater accountability and trust into the deployment of machine learning models, especially those operating at scale.
- · AI model providers
- · Auditors
- · Enterprise AI adopters
- · Cybersecurity sector
- · Malicious actors
- · Model counterfeiters
- · AI companies with opaque development practices
Increased confidence in deployed AI models and reduced risk of supply chain attacks.
Standardization of fine-tuning integrity verification methods across the industry, potentially leading to new regulatory requirements.
Enhanced trust fostering greater adoption of AI in highly sensitive sectors, accelerating AI integration into national infrastructure.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG