OPENSUSE-SU-2026:12096-1
Vulnerability from csaf_opensuse - Published: 2026-10-09 00:00 - Updated: 2026-10-10 08:44Summary
python313-datamodel-code-generator-0.83.0-1.1 on GA media
Severity
Moderate
Notes
Title of the patch: python313-datamodel-code-generator-0.83.0-1.1 on GA media
Description of the patch: These are all security issues fixed in the python313-datamodel-code-generator-0.83.0-1.1 package on the GA media of openSUSE Tumbleweed.
Patchnames: openSUSE-Tumbleweed-2026-12096
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
Affected products
Recommended
8 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Tumbleweed:python313-datamodel-code-generator-0:0.83.0-1.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python313-datamodel-code-generator-0:0.83.0-1.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python313-datamodel-code-generator-0:0.83.0-1.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python313-datamodel-code-generator-0:0.83.0-1.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python314-datamodel-code-generator-0:0.83.0-1.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python314-datamodel-code-generator-0:0.83.0-1.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python314-datamodel-code-generator-0:0.83.0-1.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Tumbleweed:python314-datamodel-code-generator-0:0.83.0-1.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
important
References
5 references
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"title": "Patchnames"
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{
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"title": "python313-datamodel-code-generator-0.83.0-1.1 on GA media",
"tracking": {
"current_release_date": "2026-10-10T08:44:26Z",
"generator": {
"date": "2026-10-09T00:00:00Z",
"engine": {
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"id": "openSUSE-SU-2026:12096-1",
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"revision_history": [
{
"date": "2026-10-09T00:00:00Z",
"number": "1",
"summary": "Current version"
},
{
"date": "2026-10-10T08:44:26Z",
"number": "2",
"summary": "unknown changes"
}
],
"status": "final",
"version": "2"
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"vulnerabilities": [
{
"cve": "CVE-2026-107377",
"ids": [
{
"system_name": "SUSE CVE Page",
"text": "https://www.suse.com/security/cve/CVE-2026-107377"
}
],
"notes": [
{
"category": "general",
"text": "datamodel-code-generator generates Python data models from schema definitions. From 0.59.0 until 0.81.0, an attacker-controlled Protobuf schema can supply absolute or parent-directory paths captured by WEAK_IMPORT_PATTERN and consumed by _write_missing_weak_imports in src/datamodel_code_generator/parser/protobuf.py. Exploitation requires a victim or automated job to process the attacker-controlled schema with Protobuf input support, which requires the grpcio-tools package. The paths escape the weak_imports temporary directory before protoc runs, allowing creation of directory trees and new files or overwrite of existing writable files with a generated Protobuf syntax declaration. The effect persists when later Protobuf compilation fails. The written content is limited to a proto2 or proto3 syntax declaration, and direct arbitrary code execution has not been demonstrated. This issue is fixed in version 0.81.0.",
"title": "CVE description"
}
],
"product_status": {
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"url": "https://www.suse.com/security/cve/CVE-2026-107377"
},
{
"category": "external",
"summary": "SUSE Bug 1284763 for CVE-2026-107377",
"url": "https://bugzilla.suse.com/1284763"
}
],
"remediations": [
{
"category": "vendor_fix",
"details": "To install this SUSE Security Update use the SUSE recommended installation methods like YaST online_update or \"zypper patch\".\n",
"product_ids": [
"openSUSE Tumbleweed:python313-datamodel-code-generator-0:0.83.0-1.1.aarch64",
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"threats": [
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"date": "2026-10-09T00:00:00Z",
"details": "important"
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"title": "CVE-2026-107377"
}
]
}
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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