CVE-2021-29550 (GCVE-0-2021-29550)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:10 – Updated: 2024-08-03 22:11
VLAI?
Title
Division by 0 in `FractionalAvgPool`
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Severity ?
2.5 (Low)
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: < 2.1.4
Affected: >= 2.2.0, < 2.2.3
Affected: >= 2.3.0, < 2.3.3
Affected: >= 2.4.0, < 2.4.2
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              "value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."
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The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.\"},{\"lang\":\"es\",\"value\":\"TensorFlow es una plataforma de c\u00f3digo abierto de extremo a extremo para el aprendizaje autom\u00e1tico. Un atacante puede causar un error de divisi\u00f3n por cero en tiempo de ejecuci\u00f3n y una denegaci\u00f3n de servicio en \\\"tf.raw_ops.FractionalAvgPool\\\". Esto es debido a que la implementaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) calcula una cantidad divisora al dividir dos valores controlados por el usuario. El usuario controla los valores de \\\"input_size[i]\\\" y \\\"pooling_ratio_[i]\\\" (por medio de los argumentos \\\"value.shape()\\\" y \\\"pooling_ratio\\\"). Si el valor en \\\"input_size[i]\\\" es menor que el \\\"pooling_ratio_[i]\\\", entonces la operaci\u00f3n floor resulta en que \\\"output_size[i]\\\" sea 0. La l\u00ednea \\\"DCHECK_GT\\\" es un no-op fuera del modo de depuraci\u00f3n, as\u00ed que en las versiones liberadas de TF  no es desencadenado. M\u00e1s tarde, estos valores calculados son usados como argumentos (https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) para \\\"GeneratePoolingSequence\\\"(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). All\u00ed, el primer c\u00e1lculo es una divisi\u00f3n en una operaci\u00f3n de m\u00f3dulo. Dado que \\\"output_length\\\" puede ser 0, esto resulta en un bloqueo en tiempo de ejecuci\u00f3n. La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.5.0. Tambi\u00e9n se incluir\u00e1 este commit en TensorFlow versi\u00f3n 2.4.2, TensorFlow versi\u00f3n 2.3.3, TensorFlow versi\u00f3n 2.2.3 y TensorFlow versi\u00f3n 2.1.4, ya que estos tambi\u00e9n est\u00e1n afectados y siguen siendo compatibles\"}],\"metrics\":{\"cvssMetricV31\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L\",\"baseScore\":2.5,\"baseSeverity\":\"LOW\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"HIGH\",\"privilegesRequired\":\"LOW\",\"userInteraction\":\"NONE\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"NONE\",\"integrityImpact\":\"NONE\",\"availabilityImpact\":\"LOW\"},\"exploitabilityScore\":1.0,\"impactScore\":1.4},{\"source\":\"nvd@nist.gov\",\"type\":\"Primary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H\",\"baseScore\":5.5,\"baseSeverity\":\"MEDIUM\",\"attackVector\":\"LOCAL\",\"attackComplexity\":\"LOW\",\"privilegesRequired\":\"LOW\",\"userInteraction\":\"NONE\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"NONE\",\"integrityImpact\":\"NONE\",\"availabilityImpact\":\"HIGH\"},\"exploitabilityScore\":1.8,\"impactScore\":3.6}],\"cvssMetricV2\":[{\"source\":\"nvd@nist.gov\",\"type\":\"Primary\",\"cvssData\":{\"version\":\"2.0\",\"vectorString\":\"AV:L/AC:L/Au:N/C:N/I:N/A:P\",\"baseScore\":2.1,\"accessVector\":\"LOCAL\",\"accessComplexity\":\"LOW\",\"authentication\":\"NONE\",\"confidentialityImpact\":\"NONE\",\"integrityImpact\":\"NONE\",\"availabilityImpact\":\"PARTIAL\"},\"baseSeverity\":\"LOW\",\"exploitabilityScore\":3.9,\"impactScore\":2.9,\"acInsufInfo\":false,\"obtainAllPrivilege\":false,\"obtainUserPrivilege\":false,\"obtainOtherPrivilege\":false,\"userInteractionRequired\":false}]},\"weaknesses\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Primary\",\"description\":[{\"lang\":\"en\",\"value\":\"CWE-369\"}]}],\"configurations\":[{\"nodes\":[{\"operator\":\"OR\",\"negate\":false,\"cpeMatch\":[{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionEndExcluding\":\"2.1.4\",\"matchCriteriaId\":\"323ABCCE-24EB-47CC-87F6-48C101477587\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"2.2.0\",\"versionEndExcluding\":\"2.2.3\",\"matchCriteriaId\":\"64ABA90C-0649-4BB0-89C9-83C14BBDCC0F\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"2.3.0\",\"versionEndExcluding\":\"2.3.3\",\"matchCriteriaId\":\"0F83E0CF-CBF6-4C24-8683-3E7A5DC95BA9\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"2.4.0\",\"versionEndExcluding\":\"2.4.2\",\"matchCriteriaId\":\"8259531B-A8AC-4F8B-B60F-B69DE4767C03\"}]}]}],\"references\":[{\"url\":\"https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96\",\"source\":\"security-advisories@github.com\",\"tags\":[\"Patch\",\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv\",\"source\":\"security-advisories@github.com\",\"tags\":[\"Exploit\",\"Patch\",\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Patch\",\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Exploit\",\"Patch\",\"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.



Detection rules are retrieved from Rulezet.