PatchSiren cyber security CVE debrief
CVE-2026-113791 py-pdf CVE debrief
CVE-2026-113791: pypdf Possible large memory usage when parsing content streams. The CVE record was published on 2026-10-22T16:49:22.000Z and has not been modified since then. This vulnerability allows an attacker to craft a PDF that leads to large memory usage when parsing content streams with many small operations. The issue has been fixed in pypdf version 6.21.0. Defenders should assess exposure and prioritize upgrading to version 6.21.0 or later. If an upgrade is not possible, consider applying the changes from PR #4218.
- Vendor
- py-pdf
- Product
- pypdf
- CVSS
- MEDIUM 6
- CISA KEV
- Not listed in stored evidence
- Original CVE published
- 2026-10-22
- Original CVE updated
- 2026-10-22
- Advisory published
- 2026-10-22
- Advisory updated
- 2026-10-22
Who should care
Defenders responsible for systems using pypdf should assess exposure and prioritize upgrading to version 6.21.0 or later. If an upgrade is not possible, consider applying the changes from PR #4218. The vulnerability can lead to large memory usage issues when parsing content streams with many small operations. Defenders should review compensating controls for exposed systems while remediation is scheduled and verified.
Why it matters
Defenders should prioritize upgrading to pypdf version 6.21.0 or later to prevent potential large memory usage issues when parsing content streams.
- Potential large memory usage issues when parsing content streams
- Need to upgrade to pypdf version 6.21.0 or later to prevent issues
Technical summary
The vulnerability in pypdf allows an attacker to craft a PDF that leads to large memory usage when parsing content streams with many small operations. This issue has been fixed in pypdf version 6.21.0. Defenders should prioritize upgrading to pypdf version 6.21.0 or later to prevent potential large memory usage issues. The vulnerability can be mitigated by applying the changes from PR #4218 if an upgrade is not possible. The CVE record was published on 2026-10-22T16:49:22.000Z and has not been modified since then. The affected product deployments should be reviewed to assess exposure.
Defensive priority
Defenders should prioritize upgrading to pypdf version 6.21.0 or later to prevent potential large memory usage issues.
Recommended defensive actions
- Upgrade to pypdf version 6.21.0 or later
- Apply changes from PR #4218 if upgrade is not possible
- Monitor for potential large memory usage issues when parsing content streams
- 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 vulnerability allows an attacker to craft a PDF that leads to large memory usage when parsing content streams with many small operations. This issue has been fixed in pypdf version 6.21.0. The CVE record was published on 2026-10-22T16:49:22.000Z and has not been modified since then. Defenders should verify the affected scope and severity based on the official advisory.
Sources and references
Verified primary and authoritative sources
-
CVE-2026-113791 CVE Program record
Publisher, destination, and source semantics verified
URL: https://www.cve.org/CVERecord?id=CVE-2026-113791
CVE Program - Official CVE Program record with source-provided CVE metadata.
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CVE-2026-113791 NVD vulnerability detail
Publisher, destination, and source semantics verified
URL: https://nvd.nist.gov/vuln/detail/CVE-2026-113791
NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.
Supplemental references
-
pypdf: Possible large memory usage when parsing content streams
Unverified legacy reference
URL: https://storage.googleapis.com/osv-vulnerabilities/PyPI/GHSA-cgf9-327m-v77c.json
osv_dev
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Source reference
Unverified legacy reference
URL: https://github.com/py-pdf/pypdf/security/advisories/GHSA-cgf9-327m-v77c
Supplemental source
-
Source reference
Unverified legacy reference
URL: https://github.com/py-pdf/pypdf/pull/4218
Supplemental source
-
Source reference
Unverified legacy reference
URL: https://github.com/py-pdf/pypdf/commit/18bd2981a74479483a0b9507602c58a63d6c9b12
Supplemental source
-
Source reference
Unverified legacy reference
URL: https://github.com/py-pdf/pypdf
Supplemental source
-
Source reference
Unverified legacy reference
URL: https://github.com/py-pdf/pypdf/releases/tag/6.21.0
Supplemental source
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.