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CVE-2021-3888 (GCVE-0-2021-3888)

Vulnerability from cvelistv5 – Published: 2021-10-19 12:30 – Updated: 2024-08-03 17:09
VLAI
Title
Use of Out-of-range Pointer Offset in bfabiszewski/libmobi
Summary
libmobi is vulnerable to Use of Out-of-range Pointer Offset
Severity
7.1 (High)
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:L
CWE
  • CWE-823 - Use of Out-of-range Pointer Offset
Assigner
References
Impacted products
Vendor Product Version CPE status
bfabiszewski bfabiszewski/libmobi Affected: unspecified , < 0.8 (custom)
guessed Create a notification for this product.
Show details on NVD website
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    "redhat_vex": {
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      "cve": "CVE-2021-3888",
      "id": "CVE-2021-3888",
      "initial_release_date": "2021-09-12T00:00:00+00:00",
      "product_status:known_not_affected": "1",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "libmobi: out-of-bounds read while parsing crafted file",
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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.


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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.