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CVE-2026-31826 py-pdf CVE debrief

CVE-2026-31826 is a vulnerability in the pypdf library that can lead to large memory usage when parsing a content stream with a large /Length value. This issue is fixed in version 6.8.0. The vulnerability allows an attacker to craft a PDF that causes high memory usage, potentially leading to performance issues or crashes. Defenders should assess exposure and prioritize upgrading to version 6.8.0 or later to mitigate this issue. Systems using earlier versions should verify pypdf version and apply the fix to prevent potential memory issues.

Vendor
py-pdf
Product
pypdf
CVSS
MEDIUM 6.8
CISA KEV
Not listed in stored evidence
Original CVE published
2026-03-10
Original CVE updated
2026-11-02
Advisory published
2026-03-10
Advisory updated
2026-11-02

Who should care

Defenders responsible for systems using the pypdf library should assess exposure and prioritize upgrading to version 6.8.0 or later. Systems using earlier versions should verify pypdf version and apply the fix to prevent potential memory issues. Operators, administrators, and security teams should review the vulnerability and take necessary actions to mitigate potential impact.

Why it matters

CVE-2026-31826 is a vulnerability in the pypdf library that can lead to large memory usage. Defenders should prioritize upgrading to version 6.8.0 or later to mitigate this issue. Systems using earlier versions should assess exposure and apply the fix.

  • Potential for large memory usage when parsing content streams with large /Length values
  • Need to verify pypdf version and apply fix to prevent potential memory issues
  • Priority on upgrading to version 6.8.0 or later to ensure library security

Technical summary

The pypdf library has a vulnerability that can lead to large memory usage when parsing a content stream with a large /Length value. This issue is addressed in version 6.8.0. The vulnerability allows an attacker to craft a PDF that causes high memory usage, potentially leading to performance issues or crashes. Defenders should prioritize upgrading to pypdf version 6.8.0 or later to mitigate this vulnerability. Systems using earlier versions of pypdf should assess exposure and apply the fix to prevent potential memory issues.

Defensive priority

Defenders should prioritize upgrading to pypdf version 6.8.0 or later to mitigate this vulnerability. Systems using earlier versions of pypdf should assess exposure and apply the fix.

Recommended defensive actions

  • Upgrade to pypdf version 6.8.0 or later
  • Assess exposure and apply the fix on systems using earlier versions of pypdf
  • Monitor for potential memory usage issues with pypdf
  • 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
  • Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up

Evidence notes

The CVE record and NVD entry provide details on the vulnerability, including its description and fixed version. However, additional information on potential exploitation or impact is limited. There is no evidence of public exploitation, but defenders should verify pypdf version and assess exposure. The CVE Program and NVD provide official records, but further research is needed to understand potential impact.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-31826 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-31826

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

  • CVE-2026-31826 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-31826

    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.