CVE-2021-21862 (GCVE-0-2021-21862)

Vulnerability from cvelistv5 – Published: 2021-08-18 14:25 – Updated: 2024-08-03 18:23
VLAI
Summary
Multiple exploitable integer truncation vulnerabilities exist within the MPEG-4 decoding functionality of the GPAC Project on Advanced Content library v1.0.1. A specially crafted MPEG-4 input can cause an improper memory allocation resulting in a heap-based buffer overflow that causes memory corruption The implementation of the parser used for the “Xtra” FOURCC code is handled. An attacker can convince a user to open a video to trigger this vulnerability.
Severity
8.8 (High)
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
CWE
  • CWE-680 - Integer Overflow to Buffer Overflow
Assigner
References
Impacted products
Vendor Product Version CPE status
n/a GPAC Affected: GPAC Project Advanced Content commit a8a8d412dabcb129e695c3e7d861fcc81f608304, GPAC Project Advanced Content v1.0.1
Show details on NVD website
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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.


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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.