  Vertical federated learning (VFL) leverages various privacy-preserving
algorithms, e.g., homomorphic encryption or secret sharing based SecureBoost,
to ensure data privacy. However, these algorithms all require a semi-honest
secure definition, which raises concerns in real-world applications. In this
paper, we present Aegis, a trusted, automatic, and accurate verification
framework to verify the security of VFL jobs. Aegis is separated from local
parties to ensure the security of the framework. Furthermore, it automatically
adapts to evolving VFL algorithms by defining the VFL job as a finite state
machine to uniformly verify different algorithms and reproduce the entire job
to provide more accurate verification. We implement and evaluate Aegis with
different threat models on financial and medical datasets. Evaluation results
show that: 1) Aegis can detect 95% threat models, and 2) it provides
fine-grained verification results within 84% of the total VFL job time.
