CVE-2026-108383 in dataclasses-avroschema
Summary
by MITRE • 10/13/2026
dataclasses-avroschema generates Avro schemas from Python dataclasses, Pydantic models, and Faust Records and generates Python code from Avro schemas. From version 0.38.0 through 0.70.5, FieldRepresentation.get_field_default renders a schema-controlled enum field default as an enum member reference after applying case conversion but without validating it as an identifier. Newlines, operators, and punctuation can escape the intended reference and inject constrained statements into the generated module, allowing namespace modification or preventing the module from loading. Version 0.70.6 fixes the vulnerability by validating enum defaults as names.
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Analysis
by VulDB Data Team • 10/13/2026
The dataclasses-avroschema library serves as a critical utility for Python developers working with Apache Avro serialization formats, facilitating the generation of schema definitions from Pythonic structures such as dataclasses and Pydantic models while also enabling code generation from existing schemas. A significant security vulnerability was identified within versions 0.38.0 through 0.70.5 affecting the FieldRepresentation.get_field_default method. This component is responsible for determining default values for fields when generating Avro schema definitions or Python code structures. The flaw stems from an insufficient validation mechanism applied to enum field defaults, specifically regarding how case conversions are handled and whether the resulting string constitutes a valid identifier within the target programming language context.
The technical root cause lies in the lack of rigorous sanitization before rendering the default value into the generated schema or module. When processing an enum field with a custom default value, the library applies case conversion to align with standard naming conventions but fails to verify that the resulting string is a syntactically valid identifier for the target language, typically Python. This oversight allows special characters such as newlines, arithmetic operators, and punctuation marks to bypass intended constraints. Because these characters are not stripped or escaped properly during the rendering process, they can be interpreted by the downstream parser or interpreter rather than being treated as literal data within an enum member reference.
This vulnerability enables a code injection attack vector where maliciously crafted input in the default field value can alter the structure of the generated Python module. By injecting newlines and operators, an attacker can effectively break out of the intended string context and inject arbitrary statements into the module body. The operational impact is severe, as this allows for namespace modification or the execution of unintended code during the import phase of the generated modules. If a developer imports a module that was generated from a schema containing such malicious defaults, the injected code may execute automatically, potentially leading to remote code execution if the generation and import process occurs in an untrusted environment without additional sandboxing measures.
From a classification perspective, this vulnerability aligns with CWE-74 Improper Neutralization of Special Elements used in an XML Document or General Code Injection (CWE-94), as it involves the injection of executable code through insufficient validation of user-supplied input during code generation. In terms of the MITRE ATT&CK framework, this behavior is consistent with techniques involving Command and Scripting Interpreter abuse, specifically where malicious content is embedded within data files or configuration structures to achieve execution upon processing. The vulnerability highlights a common risk in dynamic code generation libraries where trust boundaries between schema definitions and executable code are not strictly enforced during the rendering phase.
The issue was addressed in version 0.70.6 of the library through the implementation of stricter validation logic for enum defaults. The fix ensures that any value intended to serve as an enum default is validated against a set of rules confirming it represents a valid identifier name before being rendered into the output schema or code. This prevents special characters from escaping their contextual boundaries and eliminates the possibility of injecting arbitrary statements via field definitions. Organizations utilizing dataclasses-avroschema should immediately upgrade to version 0.70.6 or later to mitigate this risk. Additionally, developers relying on dynamic code generation should implement additional validation layers at the application level to ensure that any schema inputs are sanitized before being processed by third-party libraries, thereby reducing the attack surface associated with untrusted data sources in automated development pipelines.