CVE-2026-108385
8.8 HIGHpublic exploit availablePublished 2026-10-13 · Updated 2026-10-16
AI risk analysis
- Summary
- The vulnerability allows attackers to inject arbitrary Python code into generated enum classes by using specially crafted enum symbols that contain newlines or punctuation, which can lead to remote code execution or namespace manipulation. This affects the dataclasses-avroschema library when processing user-controlled schema data.
- Exploitability
- Exploitation requires a user-controllable schema with enum symbols containing newlines or punctuation, making it moderately difficult but possible to trigger through schema generation workflows.
- Blast radius
- If exploited, this vulnerability could allow attackers to execute arbitrary code in the context of applications using dataclasses-avroschema, potentially leading to full system compromise or data exfiltration.
- Detection
- No reliable host or network indicator is derivable from the published description.
- Prioritized remediation
- Upgrade to version 0.70.6 or later to fix the enum symbol validation vulnerability.
Analysis generated locally by garnet-coder (no data left the box). AI-assisted — verify against primary sources before acting.
NVD description
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, BaseGenerator.parse_enum and EnumRepresentation.render_symbols use each schema-controlled enum symbol as a generated enum member name after case conversion but without validating it as an identifier. When generator classes are used directly, newlines and punctuation can inject constrained statements into the generated enum class body, allowing namespace modification or preventing the module from loading. Version 0.70.6 fixes the vulnerability by validating every enum symbol as a name.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Weaknesses
CWE-94
Public exploit & PoC references
- https://github.com/marcosschroh/dataclasses-avroschema/commit/429f3d705d2d04c0c65e2ef832daed5ff91318fc
- https://github.com/marcosschroh/dataclasses-avroschema/releases/tag/0.70.6
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-gw6w-pvv9-2fqc
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-gw6w-pvv9-2fqc
All references
- https://github.com/marcosschroh/dataclasses-avroschema/commit/429f3d705d2d04c0c65e2ef832daed5ff91318fc
- https://github.com/marcosschroh/dataclasses-avroschema/releases/tag/0.70.6
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-gw6w-pvv9-2fqc
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-gw6w-pvv9-2fqc
Source data: NVD (nvd.nist.gov), public domain. Exploit-DB.ai adds local AI analysis for defensive use only.
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