CVE-2016-5001 (GCVE-0-2016-5001)

Vulnerability from cvelistv5 – Published: 2017-08-30 19:00 – Updated: 2024-09-16 19:51
VLAI
Summary
This is an information disclosure vulnerability in Apache Hadoop before 2.6.4 and 2.7.x before 2.7.2 in the short-circuit reads feature of HDFS. A local user on an HDFS DataNode may be able to craft a block token that grants unauthorized read access to random files by guessing certain fields in the token.
CWE
  • Information Disclosure
References
URL Tags
http://www.securityfocus.com/bid/94950 vdb-entryx_refsource_BID
http://seclists.org/oss-sec/2016/q4/698 mailing-listx_refsource_MLIST
https://lists.apache.org/thread.html/r66de86b9a60… mailing-listx_refsource_MLIST
Impacted products
Vendor Product Version CPE status
Apache Software Foundation Apache Hadoop Affected: 2.1.0 to 2.6.3
Affected: 2.7.0 to 2.7.1
guessed Create a notification for this product.
Date Public
2016-12-16 00:00
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.

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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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