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CVE-2025-66019 (GCVE-0-2025-66019)

Vulnerability from cvelistv5 – Published: 2025-11-25 23:38 – Updated: 2026-01-20 18:33
VLAI
Title
pypdf manipulated LZWDecode streams can exhaust RAM
Summary
pypdf is a free and open-source pure-python PDF library. Prior to version 6.4.0, an attacker who uses this vulnerability can craft a PDF which leads to a memory usage of up to 1 GB per stream. This requires parsing the content stream of a page using the LZWDecode filter. This issue has been patched in version 6.4.0.
Severity
6.6 (Medium)
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:U
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-11-26 16:12 UTC
CWE
  • CWE-400 - Uncontrolled Resource Consumption
  • CWE-409 - Improper Handling of Highly Compressed Data (Data Amplification)
Assigner
References
Impacted products
Vendor Product Version CPE status
py-pdf pypdf Affected: < 6.4.0
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.


Detection rules are retrieved from Rulezet.


Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.