2024-12-08 03:06:42 +00:00

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{
"id": "CVE-2021-29536",
"sourceIdentifier": "security-advisories@github.com",
"published": "2021-05-14T20:15:12.260",
"lastModified": "2024-11-21T06:01:19.947",
"vulnStatus": "Modified",
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"lang": "en",
"value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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.&#xa0;Un atacante puede causar un desbordamiento del b\u00fafer de la pila en la funci\u00f3n \"QuantizedReshape\" al pasar umbrales no comprobados para la cuantificaci\u00f3n.&#xa0;Esto es debido a que la implementaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) asume que los 2 argumentos son siempre escalares v\u00e1lidos e intenta acceder al valor num\u00e9rico directamente.&#xa0;Sin embargo, si alguno de estos tensores est\u00e1 vac\u00edo, entonces \".flat (T)()\" es un b\u00fafer vac\u00edo y acceder al elemento en una posici\u00f3n 0 resulta en un desbordamiento.&#xa0;La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.5.0.&#xa0;Tambi\u00e9n seleccionaremos 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 a\u00fan est\u00e1n en el rango compatible"
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