GHSA-386q-5hp3-95m9 is a high-severity (CVSS 8.8) Code Injection vulnerability in datamodel-code-generator. O3 Security confirms whether GHSA-386q-5hp3-95m9 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.
`datamodel-code-generator` vulnerable to code injection in via attacker-controlled `default_factory` schema field
Exploitation Status
No confirmed exploitation observed yet
- A successful exploit gives an attacker total control of the affected component, not partial access.
- CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.
Exploitation and automatability from CISA’s SSVC triage for GHSA-386q-5hp3-95m9.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
GHSA-386q-5hp3-95m9 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 372,423 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
Real-World Exposure
datamodel-code-generatorReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
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:default_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:f"{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 Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | datamodel-code-generator | ≥ 0.17.0&&< 0.60.2 | 0.60.2 |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for datamodel-code-generator. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update datamodel-code-generator to 0.60.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-386q-5hp3-95m9 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-386q-5hp3-95m9 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-386q-5hp3-95m9. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-386q-5hp3-95m9 in your dependencies?
O3 detects GHSA-386q-5hp3-95m9 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.