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CVE-2021-34813 (GCVE-0-2021-34813)

Vulnerability from cvelistv5 – Published: 2021-06-16 17:11 – Updated: 2024-08-04 00:26
VLAI
Summary
Matrix libolm before 3.2.3 allows a malicious Matrix homeserver to crash a client (while it is attempting to retrieve an Olm encrypted room key backup from the homeserver) because olm_pk_decrypt has a stack-based buffer overflow. Remote code execution might be possible for some nonstandard build configurations.
Severity
9.8 (Critical)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE
  • n/a
Assigner
References
Show details on NVD website
To clipboard 

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      "cve": "CVE-2021-34813",
      "id": "CVE-2021-34813",
      "initial_release_date": "2021-05-25T00:00:00+00:00",
      "product_status:known_not_affected": "1",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "libolm: allows a malicious Matrix homeserver to crash a client",
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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.