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CVE-2026-46689 kanidm CVE debrief

CVE-2026-46689 is a high-severity vulnerability in the Kanidm identity management platform. Prior to version 1.9.3, a single unauthenticated GET request to any /scim/v1/... endpoint with a ?filter= query string containing a few thousand nested parentheses (≈ 4–12 KB) can cause a stack overflow, leading to the termination of the kanidmd process. This issue is patched in version 1.9.3.

Vendor
kanidm
Product
Unknown
CVSS
HIGH 8.7
CISA KEV
Not listed in stored evidence
Original CVE published
2026-06-10
Original CVE updated
2026-06-11
Advisory published
2026-06-10
Advisory updated
2026-06-11

Who should care

Administrators and users of the Kanidm identity management platform should be aware of this vulnerability and take action to update to version 1.9.3 or later.

Technical summary

The vulnerability exists in the recursive-descent PEG parser used by Kanidm. An unauthenticated attacker can exploit this vulnerability by sending a malicious GET request with a ?filter= query string containing nested parentheses, causing a stack overflow and terminating the kanidmd process.

Defensive priority

High

Recommended defensive actions

  • Update Kanidm to version 1.9.3 or later.
  • Restrict access to /scim/v1/... endpoints to authenticated users only.

Evidence notes

The CVE-2026-46689 vulnerability has a CVSS score of 8.7 and is classified as HIGH severity. The vulnerability is caused by a stack overflow in the recursive-descent PEG parser used by Kanidm.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-46689 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-46689

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

  • CVE-2026-46689 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-46689

    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.