Research Report: Automatic Schema Inference From Unknown Protobuf Messages

Packed binary formats present a challenge for reverse engineers. Determining which fields are required, which fields are optional, and which fields can be repeated demands attention to detail, specialized human expertise, and ample time. We present an automatic schema inference approach targeting the widely used protobuf serialization format. Our approach identifies field arity constraints from a collection of raw binary messages and reports them to a user as a complete protobuf schema. In our evaluation, our approach demonstrates high accuracy across a variety of inputs, and perfect accuracy when given at least 10 sample messages.

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Published
2025-05-15
DOI
https://doi.org/10.1109/spw67851.2025.00013
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Research Report: Automatic Schema Inference From Unknown Protobuf Messages

Jared Chandler
Advanced Malware Detection Techniques
article

Research Report: Automatic Schema Inference From Unknown Protobuf Messages

Jared Chandler
article en

Abstract

Packed binary formats present a challenge for reverse engineers. Determining which fields are required, which fields are optional, and which fields can be repeated demands attention to detail, specialized human expertise, and ample time. We present an automatic schema inference approach targeting the widely used protobuf serialization format. Our approach identifies field arity constraints from a collection of raw binary messages and reports them to a user as a complete protobuf schema. In our evaluation, our approach demonstrates high accuracy across a variety of inputs, and perfect accuracy when given at least 10 sample messages.

Dartmouth College (US)
National Science Foundation
Openalex Percentile: Top 81%
Advanced Malware Detection Techniques
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