PHOENIX: Misconfiguration Detection for AWS Serverless Computing

Serverless computing is a burgeoning cloud computing paradigm that allows developers to implement applications at the function level, known as serverless applications. Amazon Web Services (AWS), the leading provider in this field, offers Serverless Application Model (AWS SAM), a widely adopted configuration schema for configuring functions and managing resources. However, misconfigurations pose a major challenge during serverless application development, and existing methods are not applicable. To our knowledge, the configuration characteristics and misconfiguration detection for serverless applications have not been well explored. To address this gap, we collect and analyze 733 real-world serverless application configuration files using AWS SAM to understand their characteristics and challenges. Based on the insights, we design PHOENIX, a misconfiguration detection approach for serverless computing. PHOENIX learns configuration patterns from uniform representations of configurations and identifies potential misconfigurations that deviate from these patterns. To evaluate PHOENIX, we construct a dataset comprising 35 injected misconfigurations and 70 real-world misconfigurations with confirmed causes. Our results show that PHOENIX detects 100% of the injected misconfigurations and identifies 97.14% of real-world misconfigurations, significantly outperforming the state-of-the-art tool.