`datamodel-code-generator` vulnerable to code injection in via attacker-controlled `default_factory` schema field
Summary
datamodel-code-generator is vulnerable to code injection when generating Python models from an attacker-controlled JSON Schema, OpenAPI, YAML, JSON, Avro, Protobuf, or XSD schema. When a property carries a "default_factory" key, its value is interpolated verbatim — as a raw Python expression — into the generated Field(default_factory=...) / field(default_factory=...) call. Because this assignment is evaluated at class-definition time (i.e. on import of the generated module), an attacker who controls the schema controls a Python expression that runs in the consumer's process. No special CLI flags are required.
Details
The vulnerable chain spans the JSON-Schema-shaped parser and three sink locations (Pydantic v2, dataclass, msgspec):
Source — schema → extras:
src/datamodel_code_generator/parser/jsonschema.py:600-614—DEFAULT_FIELD_KEYSincludes the literal string"default_factory".src/datamodel_code_generator/parser/jsonschema.py:457-459—JsonSchemaObject.__init__stores any non-standard key (includingdefault_factory) inself.extras.src/datamodel_code_generator/parser/jsonschema.py:797-812—get_field_extraspreservesdefault_factorythrough to the field model.
Sinks — extras → generated Python expression:
-
src/datamodel_code_generator/model/pydantic_base.py:222-249:pythondefault_factory = data.pop("default_factory", None) ... if default_factory is not None: field_arguments = [f"default_factory={default_factory}", *field_arguments]The
default_factoryvalue is interpolated raw (norepr(), no validation). -
src/datamodel_code_generator/model/dataclass.py:211:pythonf"{k}={v if k == 'default_factory' else repr(v)}"Explicit special-case to skip
repr()fordefault_factory. -
src/datamodel_code_generator/model/msgspec.py:361— same pattern as dataclass.
Because default_factory is in DEFAULT_FIELD_KEYS, no special CLI flag is needed to reach the sink. Any input format that uses the JSON-Schema-shaped parser (jsonschema, openapi, yaml, json, dict, csv) — and any input format that converts to it (avro, protobuf, xmlschema) — is in scope.
Confirmed PoC matrix
| Input file type | Output model type | Result |
|---|---|---|
jsonschema |
pydantic_v2.BaseModel |
RCE on import |
jsonschema |
dataclasses.dataclass |
RCE on import |
jsonschema |
msgspec.Struct |
RCE on import |
jsonschema |
typing.TypedDict |
safe (TypedDict doesn't render field(); default_factory silently dropped) |
openapi |
pydantic_v2.BaseModel |
RCE on import |
Other JSON-Schema-shaped inputs (yaml, json, dict, csv, avro, protobuf, xmlschema) follow the same code path and are expected to reproduce.
PoC
Self contained Proof of Concept is available at my secret gist: https://gist.github.com/thegr1ffyn/9648b0fe4fcf7d569ac8e61dd11eebaf
Impact
- Who's affected: any developer or CI pipeline that runs
datamodel-codegenagainst a schema they didn't author themselves — third-party API specs, schemas pulled from a registry, vendored upstream.json/.yaml/.avsc/.proto/.xsdfiles, schemas fetched from a remote URL or introspection endpoint — and who imports the generated.py. - What it gains: arbitrary Python code execution in the importer's process at
importtime. The PoC copies/etc/passwdto a tmp file to demonstrate arbitrary read; the same primitive supports any operation the importing process can perform (filesystem write, environment exfiltration, secondary network calls, RCE on CI runners). - What it does NOT need: no special CLI flags, no custom templates, no
--extra-template-data, no--use-schema-description. Default invocation against a malicious schema is sufficient. - What does block it: choosing
--output-model-type typing.TypedDict(which doesn't renderfield()/Field()calls). All other supported output model types are vulnerable.
Resolution
The fix validates schema-provided default_factory values while extracting JSON Schema field extras. Only the supported factory names dict, list, and set are accepted; any other value now raises a generator error before code generation. Generator-created default factories for supported mutable defaults and optional nested models continue to use the existing code paths.
Remediation
Upgrade to datamodel-code-generator 0.60.2 or later.
This issue affects datamodel-code-generator versions >= 0.17.0, <= 0.60.1 and is fixed in 0.60.2.
Submitted by: Hamza Haroon (thegr1ffyn)
Affected software
Click a technology to see all vulnerabilities affecting it.
| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| Python (PyPI) | datamodel-code-generator | >= 0.17.0, < 0.60.2 | 0.60.2 |
References
- https://github.com/koxudaxi/datamodel-code-generator/security/advisories/GHSA-386q-5hp3-95m9
- https://github.com/koxudaxi/datamodel-code-generator/commit/17fc235e234cbcfaaadef8c74cb72c9687db0d1d
- https://github.com/koxudaxi/datamodel-code-generator
- https://github.com/koxudaxi/datamodel-code-generator/releases/tag/0.60.2
- https://pypi.org/project/datamodel-code-generator
- https://github.com/advisories/GHSA-386q-5hp3-95m9
- https://nvd.nist.gov/vuln/detail/CVE-2026-54653
- CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H- Weakness (CWE)
CWE-1336, CWE-94- Platform
- Python (PyPI)
- Package
datamodel-code-generator- Disclosed
- 28.07.2026
- Updated
- 11.10.2026 15:17
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2026-3555 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-386q-5hp3-95m9 - CC BY 4.0
Vulnerability history: datamodel-code-generator
All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.
- Total entries
- 13
- First → latest
- 07.2026 10.2026
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- High 12
- Low 1
Disclosures per year
- 2026: 13
-
`datamodel-code-generator` vulnerable to code injection in via attacker-controlled `default_factory` schema field this entry
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