CVE-2021-29549
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.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero.
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Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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.
CVSS
- Versión: 3.1
- Vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Puntuación base: 5.5
Probabilidad de explotación (EPSS)
- Probabilidad de explotación en los próximos 30 días: 0.19%
- Percentil entre todas las CVEs puntuadas: 8
- Fecha de la puntuación: 7/10/2026
EPSS (Exploit Prediction Scoring System, de FIRST) estima la probabilidad de que una vulnerabilidad sea explotada en 30 días. Complementa a CVSS (impacto) y a CISA KEV (explotación confirmada).
Tecnologías afectadas (1)
CWE
- CWE-369
- CWE-369
Referencias
- https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v
- https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v
JSON original (NVD)
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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.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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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"value": "TensorFlow es una plataforma de código abierto de extremo a extremo para el aprendizaje automático. Un atacante puede causar una división del tiempo de ejecución por error cero y denegación de servicio en \"tf.raw_ops.QuantizedBatchNormWithGlobalNormalization\". Esto es debido a que la implementación (https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) calcula una operación de módulo sin comprobar que el divisor no es cero. Dado que \"vector_num_elements\" se determina en función de las formas de entrada (https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), un usuario puede desencadenar escenarios donde la catnidad es 0. La corrección será incluída en TensorFlow versión 2.5.0. También seleccionaremos este commit en TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2"
}
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"lastModified": "2026-06-17T03:47:53.350",
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