CVE-2026-54654 koxudaxi CVE debrief

Who should care

Defenders responsible for systems using datamodel-code-generator, especially those in development and security teams, should assess exposure and prioritize remediation. They should verify if systems are affected by checking their version and review generated Python models for potential code injection. Security teams should monitor systems for suspicious activity related to generated models and plan vendor-supported updates or mitigations through normal

Why it matters

CVE-2026-54654 allows attacker-controlled comment values to inject Python code into generated models by datamodel-code-generator. Defenders should verify and remediate affected systems, especially those using versions between 0.14.1 and 0.60.2.

• Potential code injection into generated Python models
• Possible execution of attacker-controlled code when models are imported
• Need for verification of affected systems and model review
• Priority for upgrading to version 0.60.2 or later

Technical summary

The datamodel-code-generator tool generates Python data models from schema definitions. A vulnerability exists in versions from 0.14.1 to 0.60.2, where the --extra-template-data comment field is rendered into Python comments without neutralizing carriage returns, allowing an attacker to inject Python code into generated models. This issue is fixed in version 0.60.2. The vulnerability allows attacker-controlled comment values to inject Python code into generated models that runs when imported. Defenders should prioritize verifying and remediating affected systems, especially those using datamodel-code-generator versions between 0.14.1 and 0.60.2.

Defensive priority

Defenders should prioritize verifying and remediating affected systems, especially those using datamodel-code-generator versions between 0.14.1 and 0.60.2.

Recommended defensive actions

• Verify if systems using datamodel-code-generator are affected by checking their version
• Upgrade datamodel-code-generator to version 0.60.2 or later
• Review generated Python models for potential code injection
• Monitor systems for suspicious activity related to generated models
• Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up
• Review the supplied official advisory or CVE record to validate affected scope, severity, and vendor guidance
• Plan vendor-supported updates or mitigations through normal change control where exposure is confirmed

Evidence notes

The CVE record and NVD entry provide details on the vulnerability in datamodel-code-generator. The issue allows attacker-controlled comment values to inject Python code into generated models. The fix is included in version 0.60.2.

Sources and 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.