CVE-2026-73626 jupyterlab CVE debrief

Who should care

JupyterLab users and administrators, especially those with custom extensions or downstream integrations that import PyPIExtensionManager and call install() directly, should assess their deployments for potential exposure. They should review and enforce secure extension installation practices, and monitor for suspicious activity related to extension installations. Additionally, security teams and vulnerability management teams should prioritize patching or mitigating this vulnerability to prevent potential code execution. Platform operators and security teams should also review compensating controls for exposed systems while remediation is scheduled and verified. Asset inventory and change management processes should be updated to reflect the potential exposure and remediation efforts. Security monitoring and detection teams should be aware of the potential for exploitation and review relevant logs for exposed assets that need extra review. Exceptions, retesting of remediated assets, and documentation of evidence should be tracked and verified before closing the item. IT operations and security teams should work together to ensure that affected systems are properly patched or mitigated, and that any necessary changes are properly tested and validated before deployment. Security awareness and training programs should also be updated to reflect the potential risks associated with this vulnerability and the importance of secure extension installation practices. The CVE record was published on 2026-08-13T12:17:27.897Z and has not been modified since then. The NVD entry is currently Received. The CVE Program and NVD provide official guidance on this vulnerability, and their records should be consulted for further information. Additional information and updates on this vulnerability can be found through the official CVE Program record and NVD detail page. JupyterLab users and administrators should stay informed about the latest developments and patches related to this vulnerability through official channels and security advisories. They should also review their current configurations and assess their exposure to this vulnerability. Furthermore, they should consider the

Technical summary

The vulnerability is caused by a missing 'await' in PyPIExtensionManager.install(), leading to an allowlist/blocklist enforcement gap in JupyterLab versions >=4.6.0,<=4.6.1 and <=4.5.9. This issue allows for potential code execution via direct calls to install() with a package name influenced by untrusted input. The stock JupyterLab HTTP API and Extension Manager UI are not affected, as they perform a separate, correctly awaited check.

Defensive priority

High-severity vulnerability in JupyterLab, requiring prompt attention to prevent potential code execution.

Recommended defensive actions

• Inventory and assess JupyterLab deployments for potential exposure.
• Apply patches or updates to affected versions.
• Review and enforce secure extension installation practices.
• Monitor for suspicious activity related to extension installations.
• 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

Evidence from official sources indicates a vulnerability in JupyterLab versions >=4.6.0,<=4.6.1 and <=4.5.9. The issue is caused by a missing 'await' in PyPIExtensionManager.install(), leading to an allowlist/blocklist enforcement gap. The stock JupyterLab HTTP API and Extension Manager UI are not affected. Affected deployments are those where a custom extension or downstream integration imports PyPIExtensionManager and calls install() directly with a package name influenced by untrusted input, an allowlist/blocklist is configured, the PyPI Extension Manager is enabled, and kernels and terminals are disabled or delegated to remote hosts.

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.