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CVE-2011-1142 (GCVE-0-2011-1142)

Vulnerability from cvelistv5 – Published: 2011-03-03 00:00 – Updated: 2024-10-21 16:13
VLAI
Summary
Stack consumption vulnerability in the dissect_ber_choice function in the BER dissector in Wireshark 1.2.x through 1.2.15 and 1.4.x through 1.4.4 might allow remote attackers to cause a denial of service (infinite loop) via vectors involving self-referential ASN.1 CHOICE values.
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-02-23 19:47 UTC
CWE
  • n/a
  • CWE-835 - Loop with Unreachable Exit Condition ('Infinite Loop')
Date Public
2011-03-01 00:00
Show details on NVD website

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        "dateUpdated": "2024-10-21T16:13:16.900Z",
        "state": "PUBLISHED"
      },
      "dataType": "CVE_RECORD",
      "dataVersion": "5.1"
    }
  }
}



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Tags
Taxonomy of the tags.


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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.

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Detection rules are retrieved from Rulezet.

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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.


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