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CVE-2022-25147 (GCVE-0-2022-25147)

Vulnerability from cvelistv5 – Published: 2023-01-31 15:54 – Updated: 2025-02-13 16:32
VLAI
Title
Apache Portable Runtime Utility (APR-util): out-of-bounds writes in the apr_base64 family of functions
Summary
Integer Overflow or Wraparound vulnerability in apr_base64 functions of Apache Portable Runtime Utility (APR-util) allows an attacker to write beyond bounds of a buffer. This issue affects Apache Portable Runtime Utility (APR-util) 1.6.1 and prior versions.
Severity
No CVSS data available.
CWE
  • CWE-190 - Integer Overflow or Wraparound
Assigner
References
Impacted products
Vendor Product Version CPE status
Apache Software Foundation Apache Portable Runtime Utility (APR-util) Affected: 0 , ≤ 1.6.1 (semver)
guessed Create a notification for this product.
Credits
Ronald Crane (Zippenhop LLC)
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.