CVE-2026-108382
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 when using dataclasses-avroschema with schema-controlled enum symbols that contain quotes. This can lead to remote code execution during module import.
- Exploitability
- Exploitation requires a maliciously crafted schema with enum symbols containing quotes, and the attacker must be able to influence schema generation or deployment of generated code, which is moderately difficult but possible in environments where schemas are dynamically generated from untrusted input.
- Blast radius
- If exploited, this vulnerability could allow attackers to execute arbitrary Python code on systems that import the generated modules, potentially leading to full system compromise if the affected service runs with elevated privileges.
- 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 escaping 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, EnumRepresentation.render_symbols concatenates each schema-controlled enum symbol between double quotes in a generated enum assignment. When generator classes are used directly, a quote can terminate the string literal and inject arbitrary Python code into the enum class body, which executes when the generated module is imported. Version 0.70.6 fixes the vulnerability by escaping enum symbol values before rendering them.
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-vrq9-94ww-jv3c
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-vrq9-94ww-jv3c
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-vrq9-94ww-jv3c
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-vrq9-94ww-jv3c
Source data: NVD (nvd.nist.gov), public domain. Exploit-DB.ai adds local AI analysis for defensive use only.
Related CVEs
- CRITICALCVE-2026-105135PoC
- HIGHCVE-2026-108375PoC
- HIGHCVE-2026-108377PoC
- HIGHCVE-2026-108378PoC
- HIGHCVE-2026-108381PoC
- HIGHCVE-2026-108385PoC
- HIGHCVE-2026-18770
- CRITICALCVE-2026-51872PoC
Related by shared AI tags and CWE weakness class. Browse the full archive.