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VAR-201001-0237

Vulnerability from variot - Updated: 2021-12-18 01:40

Sun Java System Web Server (aka SJWS) 7.0 Update 7 allows remote attackers to overwrite memory locations in the heap, and discover the contents of memory locations, via a malformed HTTP TRACE request that includes a long URI and many empty headers, related to an "overflow." NOTE: this might overlap CVE-2010-0272 and CVE-2010-0273. A heap overflow vulnerability in the HTTP TRACE feature of the Java System Web server that could allow a remote, non-privileged user to cause a Web server or Web proxy server to crash, resulting in a denial of service. These vulnerabilities can also result in unauthorized access to sensitive information. Sun Java System Web Server is a Java-based WEB server. There are no detailed vulnerability details available, but the mass canvas platform already provides tools for use

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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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