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CVE-2026-54655 koxudaxi CVE debrief

The CVE-2026-54655 vulnerability affects the datamodel-code-generator library, which generates Python data models from schema definitions. From version 0.51.0 to 0.60.2, the library inserts x-python-type values from JSON Schema content into generated field annotations without sufficient validation. This allows attacker-controlled JSON Schema content to execute Python code when the generated module is imported. The issue is fixed in version 0.60.2. Defenders should prioritize verifying if their Python projects use the datamodel-code-generator library, especially versions between 0.51.0 and 0.60.2, and assess if they have imported generated modules from untrusted or attacker- - -

Vendor
koxudaxi
Product
datamodel-code-generator
CVSS
HIGH 7.8
CISA KEV
Not listed in stored evidence
Original CVE published
2026-07-28
Original CVE updated
2026-10-23
Advisory published
2026-07-28
Advisory updated
2026-10-23

Who should care

Defenders responsible for Python projects using the datamodel-code-generator library, especially those using versions between 0.51.0 and 0.60.2, should assess their exposure and prioritize verification and remediation.

Why it matters

The CVE-2026-54655 vulnerability in the datamodel-code-generator library allows attacker-controlled JSON Schema content to execute Python code. Defenders should prioritize verifying exposure, especially for Python projects using versions between 0.51.0 and 0.60.2, and update to version 0.60.2 or later.

  • Code execution can occur when importing generated modules from untrusted JSON Schema content.
  • The vulnerability requires verification of Python project dependencies and generated modules.
  • Remediation involves updating the datamodel-code-generator library to version 0.60.2 or later.
  • Monitoring for suspicious activity related to the library is necessary.

Technical summary

The datamodel-code-generator library generates Python data models from schema definitions. From version 0.51.0 to 0.60.2, the library inserts x-python-type values from JSON Schema content into generated field annotations without sufficient validation. This allows attacker-controlled JSON Schema content to execute Python code when the generated module is imported. The issue is fixed in version 0.60.2.

Defensive priority

Defenders should prioritize verifying if their Python projects use the datamodel-code-generator library, especially versions between 0.51.0 and 0.60.2, and assess if they have imported generated modules from untrusted or attacker-controlled JSON Schema content.

Recommended defensive actions

  • Verify if Python projects use the datamodel-code-generator library, especially versions between 0.51.0 and 0.60.2.
  • Assess if generated modules have been imported from untrusted or attacker-controlled JSON Schema content.
  • Update the datamodel-code-generator library to version 0.60.2 or later.
  • Monitor for any suspicious activity related to the datamodel-code-generator library.
  • Review compensating controls for exposed systems while remediation is scheduled and verified.
  • Check relevant monitoring, detection, and logs for exposed assets that need extra review.
  • Track exceptions, retest remediated assets, and close the item only after evidence is documented.

Evidence notes

The CVE record and NVD entry provide details on the vulnerability in the datamodel-code-generator library. The issue is fixed in version 0.60.2. However, the corpus does not provide information on known affected or fixed versions beyond this range.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-54655 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-54655

    CVE Program - Official CVE Program record with source-provided CVE metadata.

  • CVE-2026-54655 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-54655

    NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.

Supplemental references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.