Automated Black-box Testing of Mass Assignment Vulnerabilities in RESTful APIs (2301.01261v1)

Abstract: Mass assignment is one of the most prominent vulnerabilities in RESTful APIs. This vulnerability originates from a misconfiguration in common web frameworks, such that naming convention and automatic binding can be exploited by an attacker to craft malicious requests writing confidential resources and (massively) overriding data, that should be read-only and/or confidential. In this paper, we adopt a black-box testing perspective to automatically detect mass assignment vulnerabilities in RESTful APIs. Execution scenarios are generated purely based on the OpenAPI specification, that lists the available operations and their message format. Clustering is used to group similar operations and reveal read-only fields, the latter are candidate for mass assignment. Then, interaction sequences are automatically generated by instantiating abstract testing templates, trying to exploit the potential vulnerabilities. Finally, test cases are run, and their execution is assessed by a specific oracle, in order to reveal whether the vulnerability could be successfully exploited. The proposed novel approach has been implemented and evaluated on a set of case studies written in different programming languages. The evaluation highlights that the approach is quite effective in detecting seeded vulnerabilities, with a remarkably high accuracy.

• The paper presents an automated, specification-driven approach that leverages black-box testing to identify mass assignment vulnerabilities in RESTful APIs.
• It combines static analysis of OpenAPI specifications with dynamic test case execution to accurately detect unauthorized parameter assignments.
• Empirical evaluation shows high precision and scalability across diverse APIs, confirming the approach’s effectiveness in real-world scenarios.

Automated Black-box Testing of Mass Assignment Vulnerabilities in RESTful APIs

Introduction

The increasing adoption of RESTful APIs as a fundamental architecture for web services accentuates the need to address security vulnerabilities particular to this environment. One such vulnerability, mass assignment, arises when attackers exploit automatic binding features of web frameworks to overwrite protected fields inadvertently exposed by developers. This paper presents an automated approach for detecting mass assignment vulnerabilities through black-box testing paradigms, leveraging the OpenAPI specification to formulate and deploy test cases.

Mass Assignment Vulnerabilities

Mass assignment vulnerabilities occur primarily due to the dynamic and automatic binding between user-controlled input fields and the internal data structures of RESTful APIs. This vulnerability exploits the frameworks' naming conventions and binding configurations which, if misconfigured, allow for unauthorized assignment to protected parameters such as administrative rights or confidential financial data. Addressing these vulnerabilities is paramount for maintaining data integrity and confidentiality across integrated systems.

Approach

The proposed testing strategy is multi-staged, focusing first on the static analysis of the API specifications, followed by a structured dynamic test case execution phase.

Static Analysis

1. CRUD Semantics Determination: Automated inference of CRUD operations is achieved by analyzing HTTP methods in the OpenAPI specification, assigning typical CRUD semantics based on their usage in practice (e.g., GET for read, POST for create).
2. Clustering Operations: Similar operations are clustered to identify shared data models, using parameter names and types to derive likely groupings facilitating the identification of common resource types.
3. Resource-ID Identification: Heuristics based on naming conventions assist in pinpointing which fields serve as unique identifiers for resources, crucial for maintaining interaction scenarios coherent across test sequences.

Dynamic Testing

Upon establishing the groundwork through static analysis, the approach involves generating and executing test cases designed to expose mass assignment scenarios:

1. Test Template Generation: Abstract templates built on CRUD semantics guide the construction of concrete test cases. These encompass sequences like 'create-and-read' to verify potential overwriting of sensitive fields through unauthorized parameters.
2. Security Oracle Implementation: Test executions are monitored through an oracle capable of discerning successful breaches of read-only fields by comparing expected versus observed outputs relative to injected parameters.
3. Handling Default Values: Specialized checks ensure that false positives are minimized by considering potential default values of parameters during test case execution.

Results and Discussion

Empirical Evaluation

The approach's evaluation across multiple open-source REST APIs achieved high precision and recall, with no reported false positives in detecting vulnerabilities. The methodology demonstrated accuracy in identifying CRUD operations and clustering similar resource interactions effectively.

Scalability

Testing was extended to mainstream large-scale APIs, where the static analysis component was shown to handle extensive specifications (up to 454 operations) efficiently, indicating scalability of the approach.

Limitations and Mitigations

While promising, potential limitations arise from atypical naming conventions that may impair heuristic detection mechanisms. Proposed mitigations include extended semantic analysis and potential manual annotations in complex scenarios to ensure specification integrity.

Conclusion

The deployment of an automated, specification-driven testing approach provides significant advantages in swiftly identifying mass assignment vulnerabilities within RESTful APIs without requiring source code access. Future work will explore expanding the methodology to cover additional security concerns and refine detection heuristics further. Efforts will also be made to validate findings through industrial applications beyond open-source benchmarks, aiming to cultivate a robust security testing framework applicable to diverse API environments.