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VulnerabilityPublished Updated

CVE-2026-28351: py-pdf pypdf · Uncontrolled Resource Consumption

Affects py-pdf pypdfSource CVE Database V5

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Not known to be exploited · CVSS 6.9 Medium : upgrade to 6.7.4 when convenient.

Data as of 29 Oct 2026, 19:04 UTC

CVSS 4.0 base score
6.9Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 41% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 6.7.4
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 27 Feb 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 29 Oct 2026.

What to do

Upgrade py-pdf pypdf to 6.7.4.

Affected software

ProductAffectedFixed in
py-pdfpypdfAffected
  • <6.7.4
Fixed in
  • 6.7.4
PyPIpypdfpkg:pypi/pypdfOther threatsAffected
  • <6.7.4
Fixed in
  • 6.7.4

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

To mitigate CVE-2026-28351, organizations should immediately upgrade all instances of the pypdf library to version 6.7.4 or later, where the vulnerability is fixed. If upgrading is not immediately feasible, applying the changes from pull request #3664 as a temporary workaround can reduce risk. Additionally, implement strict input validation and filtering to restrict or sanitize PDF files from untrusted sources before processing. Employ resource limiting techniques such as memory quotas or sandboxing for PDF parsing processes to contain potential resource exhaustion. Monitoring and alerting on abnormal memory usage patterns during PDF processing can help detect exploitation attempts early. Consider isolating PDF processing workloads in separate containers or virtual machines to prevent impact on critical systems. Regularly review and update dependencies to ensure timely application of security patches. Finally, educate developers and system administrators about the risks of processing untrusted PDFs and the importance of using updated libraries.

Description

### Impact An attacker who uses this vulnerability can craft a PDF which leads to large memory usage. This requires parsing the content stream using the RunLengthDecode filter. ### Patches This has been fixed in [pypdf==6.7.4](https://github.com/py-pdf/pypdf/releases/tag/6.7.4). ### Workarounds If you cannot upgrade yet, consider applying the changes from PR [#3664](https://github.com/py-pdf/pypdf/pull/3664).

References

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