FKIE_CVE-2026-55389
Vulnerability from fkie_nvd - Published: 2026-07-28 22:17 - Updated: 2026-08-06 19:44
Severity
Summary
datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. Prior to 0.62.0, datamodel-code-generator resolves JSON Schema $ref targets in src/datamodel_code_generator/parser/jsonschema.py through is_url and _get_ref_body without containing file:// or ../ traversal references to the input directory and without honoring --no-allow-remote-refs, allowing arbitrary local file reads. This issue is fixed in version 0.62.0.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| koxudaxi | datamodel-code-generator | * |
{
"affected": [
{
"affectedData": [
{
"product": "datamodel-code-generator",
"vendor": "koxudaxi",
"versions": [
{
"status": "affected",
"version": "\u003c 0.62.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:koxudaxi:datamodel-code-generator:*:*:*:*:*:*:*:*",
"matchCriteriaId": "4B1622B4-E307-4720-981C-D1A663293975",
"versionEndExcluding": "0.62.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "datamodel-code-generator generates Pydantic v2 models, dataclasses, TypedDict, and msgspec.Struct from OpenAPI, JSON Schema, GraphQL, Avro, Protobuf, and raw JSON, YAML, or CSV. Prior to 0.62.0, datamodel-code-generator resolves JSON Schema $ref targets in src/datamodel_code_generator/parser/jsonschema.py through is_url and _get_ref_body without containing file:// or ../ traversal references to the input directory and without honoring --no-allow-remote-refs, allowing arbitrary local file reads. This issue is fixed in version 0.62.0."
}
],
"id": "CVE-2026-55389",
"lastModified": "2026-08-06T19:44:40.270",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-55389",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-07-29T12:33:40.109394Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-07-28T22:17:48.400",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/koxudaxi/datamodel-code-generator/commit/2ff4a72b4550a2b2069754c5b075b1655067e5fb"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/koxudaxi/datamodel-code-generator/releases/tag/0.62.0"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://github.com/koxudaxi/datamodel-code-generator/security/advisories/GHSA-8359-h9fx-j6v9"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://github.com/koxudaxi/datamodel-code-generator/security/advisories/GHSA-8359-h9fx-j6v9"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-22"
},
{
"lang": "en",
"value": "CWE-200"
},
{
"lang": "en",
"value": "CWE-610"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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