CVE-2021-37663 (GCVE-0-2021-37663)

Vulnerability from cvelistv5 – Published: 2021-08-12 22:45 – Updated: 2024-08-04 01:23
VLAI?
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
CWE
  • CWE-20 - Improper Input Validation
Assigner
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3
Affected: < 2.3.4
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Show details on NVD website

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comprobaci\u00f3n debe verificar que sea un valor en el rango para el rango del tensor de \\\"input\\\" y luego las longitudes de las entradas de\\\" min_range\\\" y \\\"max_range\\\" coincidan con las Dimensi\u00f3n \\\"axis\\\" del tensor\\\" input\\\".\u0026#xa0;Hemos solucionado el problema en GitHub commit 6da6620efad397c85493b8f8667b821403516708.\u0026#xa0;La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.6.0.\u0026#xa0;Tambi\u00e9n seleccionaremos este commit en TensorFlow versi\u00f3n 2.5.1, TensorFlow versi\u00f3n 2.4.3 y TensorFlow versi\u00f3n 2.3.4, ya que estos tambi\u00e9n est\u00e1n afectados y a\u00fan se encuentran en el rango 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Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j\",\"source\":\"security-advisories@github.com\",\"tags\":[\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Patch\",\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Third Party Advisory\"]}]}}"
  }
}


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Sightings

Author Source Type Date

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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