CVE-2017-14059 (GCVE-0-2017-14059)

Vulnerability from cvelistv5 – Published: 2017-08-31 15:00 – Updated: 2024-08-05 19:13
VLAI
Summary
In FFmpeg 3.3.3, a DoS in cine_read_header() due to lack of an EOF check might cause huge CPU and memory consumption. When a crafted CINE file, which claims a large "duration" field in the header but does not contain sufficient backing data, is provided, the image-offset parsing loop would consume huge CPU and memory resources, since there is no EOF check inside the loop.
Severity
6.5 (Medium)
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H
CWE
  • n/a
Assigner
References
URL Tags
http://www.securityfocus.com/bid/100631 vdb-entryx_refsource_BID
https://github.com/FFmpeg/FFmpeg/commit/7e80b63ec… x_refsource_CONFIRM
http://www.debian.org/security/2017/dsa-3996 vendor-advisoryx_refsource_DEBIAN
Date Public
2017-08-31 00:00
Show details on NVD website
To clipboard 

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}



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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

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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.