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CVE-2026-71852 py-pdf CVE debrief

A crafted PDF can cause long runtimes and large memory consumption in pypdf versions before 6.15.0 due to the expansion of unusually large CID font /W width ranges or excessive width entries during text extraction. This issue can impact PDF processing workflows, particularly those using pypdf for text extraction or other operations. Defenders should assess their exposure and prioritize updates to mitigate potential performance issues and memory consumption vulnerabilities. The vulnerability is fixed in pypdf version 6.15.0.

Vendor
py-pdf
Product
pypdf
CVSS
MEDIUM 4.8
CISA KEV
Not listed in stored evidence
Original CVE published
2026-08-07
Original CVE updated
2026-09-10
Advisory published
2026-08-07
Advisory updated
2026-09-10

Who should care

Defenders responsible for PDF processing workflows, particularly those using pypdf versions before 6.15.0, should assess exposure and prioritize updates. This includes operators, platform administrators, vulnerability management teams, and security teams who handle PDF processing or related operations. They should review the vulnerability details, assess their exposure, and plan for updates or mitigations as needed.

Why it matters

Defenders should care about CVE-2026-71852 because it can cause performance issues and memory consumption vulnerabilities in pypdf versions before 6.15.0. Those responsible for PDF processing workflows should assess exposure, prioritize updates, and consider compensating controls.

  • Potential performance issues and memory consumption vulnerabilities
  • Need for verification of affected versions and remediation
  • Possible impact on PDF processing workflows

Technical summary

The pypdf library has a vulnerability that can cause long runtimes and large memory consumption when processing crafted PDFs. This is due to the expansion of unusually large CID font /W width ranges or excessive width entries during text extraction. The issue is fixed in pypdf version 6.15.0. Defenders should prioritize updating to this version or later to mitigate potential performance issues and memory consumption vulnerabilities. The vulnerability can impact PDF processing workflows, particularly those using pypdf for text extraction or other operations.

Defensive priority

Defenders should prioritize updating to pypdf version 6.15.0 or later to mitigate potential performance issues and memory consumption vulnerabilities.

Recommended defensive actions

  • Update to pypdf version 6.15.0 or later
  • Review and monitor PDF processing workflows for potential performance issues
  • Consider implementing compensating controls for PDF processing
  • 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
  • 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, but evidence of exploitation or specific attacks is not provided. The issue is related to the handling of CID font /W width ranges and excessive width entries during text extraction in pypdf versions before 6.15.0. Defenders should verify affected versions and consider compensating controls while remediation is scheduled and verified.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-71852 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-71852

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

  • CVE-2026-71852 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-71852

    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.