CVE-2026-108375
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 modules by manipulating default values in dataclasses-avroschema, potentially leading to remote code execution when the compromised modules are imported.
- Exploitability
- Exploitation requires crafting malicious default values that terminate string literals and inject executable statements, with no additional preconditions beyond having the vulnerable version installed.
- Blast radius
- The impact could be significant across applications using dataclasses-avroschema for schema generation, especially those importing generated modules from untrusted sources.
- 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 string escaping vulnerability in FieldRepresentation.get_field_default.
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, FieldRepresentation.get_field_default inserts schema-controlled string, UUID, bytes, and fixed field defaults into quoted literals in generated Python source without escaping them. A crafted default can terminate the literal and inject statements into the generated module, which execute when the module is imported. Version 0.70.6 fixes the vulnerability by escaping schema values rendered into generated string literals.
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/01e6d5e15ab0ff69faac39a340d21b85da1e852f
- https://github.com/marcosschroh/dataclasses-avroschema/releases/tag/0.70.6
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-9h8h-264x-jcg2
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-9h8h-264x-jcg2
All references
- https://github.com/marcosschroh/dataclasses-avroschema/commit/01e6d5e15ab0ff69faac39a340d21b85da1e852f
- https://github.com/marcosschroh/dataclasses-avroschema/releases/tag/0.70.6
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-9h8h-264x-jcg2
- https://github.com/marcosschroh/dataclasses-avroschema/security/advisories/GHSA-9h8h-264x-jcg2
Source data: NVD (nvd.nist.gov), public domain. Exploit-DB.ai adds local AI analysis for defensive use only.
Related CVEs
- HIGHCVE-2026-108381PoC
- CRITICALCVE-2026-105135PoC
- HIGHCVE-2026-108376PoC
- HIGHCVE-2026-108377PoC
- HIGHCVE-2026-108378PoC
- HIGHCVE-2026-108380PoC
- HIGHCVE-2026-108382PoC
- HIGHCVE-2026-108383PoC
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