ZKLoRA: Efficient Zero-Knowledge Proofs for LoRA Verification

TL;DR

ZKLoRA addresses the problem of verifying privately held LoRA adapters when paired with a base LLM in untrusted, distributed environments. It uses succinct zero-knowledge proofs within a Multi-Party Inference workflow to confirm that LoRA transforms are valid without revealing the adapters themselves, achieving per-module verification on state-of-the-art LLMs in about $1$–$2$ seconds; the final output is $baseOut + Delta$, where $baseOut = Wx$ and $Delta = BAx$. The approach leverages Incrementally Verifiable Computation concepts and recursive proof systems (Nova and HyperNova) to scale to large models while preserving zero-knowledge. The result enables privacy-preserving, contract-based collaboration and near real-time verification, facilitating secure LoRA deployments in decentralized pipelines.

Abstract

Low-Rank Adaptation (LoRA) is a widely adopted method for customizing large-scale language models. In distributed, untrusted training environments, an open source base model user may want to use LoRA weights created by an external contributor, leading to two requirements: (1) the base model user must confirm that the LoRA weights are effective when paired with the intended base model, and (2) the LoRA contributor must keep their proprietary weights private until compensation is assured. We present ZKLoRA, a zero-knowledge verification protocol that relies on succinct proofs and our novel Multi-Party Inference procedure to verify LoRA-base model compatibility without exposing LoRA weights. ZKLoRA produces deterministic correctness guarantees and validates each LoRA module in only 1-2 seconds on state-of-the-art large language models. This low-latency approach enables nearly real-time verification and promotes secure collaboration among geographically decentralized teams and contract-based training pipelines. The protocol ensures that the delivered LoRA module works as claimed, safeguarding the contributor's intellectual property while providing the base model user with verification of compatibility and lineage.

Figures (3)

• Figure 1: Total verification time (seconds) vs. number of LoRA modules, with dot size reflecting average LoRA size.
• Figure 2: Three-Step ZKLoRA Process (Vertical).(1) Base Model User and LoRA Contributor exchange “Base Acts” and “LoRA Acts” in a multi-party inference. (2) The LoRA Contributor generates cryptographic proofs for correctness. (3) The Base Model User verifies these proofs, ensuring correct LoRA alignment without revealing private adapter weights.
• Figure 3: Flow in a Multi-Party Inference scenario between local base model and remote LoRA weights. The base model performs a local forward pass, computing $\mathbf{base\_out} = \mathbf{Wx}$. In parallel, the input $\mathbf{x}$ is sent to the remote LoRA module, which returns $\mathbf{\Delta} = \mathbf{BAx}$, where $\mathbf{B,A}$ are the low-rank finetuned matrices. The final output is $\mathbf{base\_out} + \mathbf{\Delta}$.