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CVE-2026-102993 py-pdf CVE debrief

A vulnerability in the pypdf library can cause excessive memory consumption when processing crafted PDF files with unusually large Roman page-label values, potentially leading to application unavailability. This issue is fixed in version 6.17.0. The vulnerability is caused by the library's inability to handle large page labels, which can result in excessively large numeral strings being generated. Defenders should be aware of this issue and take steps to mitigate it. The CVE record was published on 2026-09-30T20:17:27.630Z and has not been modified since then. The vulnerability has a CVSS score of 8.7 and a severity of HIGH.

Vendor
py-pdf
Product
pypdf
CVSS
HIGH 8.7
CISA KEV
Not listed in stored evidence
Original CVE published
2026-09-30
Original CVE updated
2026-11-02
Advisory published
2026-09-30
Advisory updated
2026-11-02

Who should care

Defenders responsible for systems using the pypdf library should assess exposure and prioritize updating to version 6.17.0 or later. They should also review and verify affected versions and systems, and monitor for potential memory consumption issues. Additionally, defenders should be aware of the vulnerability's CVSS score of 8.7 and severity of HIGH, and take steps to mitigate it accordingly.

Why it matters

Defenders should care about this vulnerability as it can cause excessive memory consumption, potentially leading to application unavailability, and requires verification of affected versions and remediation efforts.

  • Potential application unavailability due to excessive memory consumption
  • Need to verify affected versions and systems
  • Priority on updating to version 6.17.0 or later

Technical summary

The pypdf library is vulnerable to excessive memory consumption when processing crafted PDF files with unusually large Roman page-label values. This issue is fixed in version 6.17.0. The vulnerability is caused by the library's inability to handle large page labels, which can result in excessively large numeral strings being generated. The issue requires verification of affected versions and remediation efforts. The CVE record was published on 2026-09-30T20:17:27.630Z and has not been modified since then. The vulnerability has a CVSS score of 8.7 and a severity of HIGH. Defenders should prioritize updating to version 6.17.0 or later to mitigate this vulnerability.

Defensive priority

Defenders should prioritize updating to version 6.17.0 or later to mitigate this vulnerability.

Recommended defensive actions

  • Update to pypdf version 6.17.0 or later
  • Review and verify affected versions and systems
  • Monitor for potential memory consumption issues
  • 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
  • Check relevant monitoring, detection, and logs for exposed assets that need extra review

Evidence notes

The vulnerability is caused by unusually large Roman page-label values in crafted PDF files, which can generate excessively large numeral strings. This issue requires verification of affected versions and remediation efforts.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-102993 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-102993

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

  • CVE-2026-102993 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-102993

    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.