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CVE-2017-16938 (GCVE-0-2017-16938)

Vulnerability from cvelistv5 – Published: 2017-11-24 07:00 – Updated: 2024-08-05 20:43
VLAI
Summary
A global buffer overflow in OptiPNG 0.7.6 allows remote attackers to cause a denial-of-service attack or other unspecified impact with a maliciously crafted GIF format file, related to an uncontrolled loop in the LZWReadByte function of the gifread.c file.
Severity
7.8 (High)
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
CWE
  • n/a
Assigner
References
URL Tags
https://lists.debian.org/debian-lts-announce/2017… mailing-listx_refsource_MLIST
https://www.debian.org/security/2017/dsa-4058 vendor-advisoryx_refsource_DEBIAN
https://sourceforge.net/p/optipng/bugs/69/ x_refsource_MISC
https://security.gentoo.org/glsa/201801-02 vendor-advisoryx_refsource_GENTOO
Date Public
2017-11-24 00:00
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.