nvd-json-data-feeds/CVE-2021/CVE-2021-295xx/CVE-2021-29529.json

{
  "id": "CVE-2021-29529",
  "sourceIdentifier": "security-advisories@github.com",
  "published": "2021-05-14T20:15:11.937",
  "lastModified": "2024-11-21T06:01:19.080",
  "vulnStatus": "Modified",
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer 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. Un atacante puede desencadenar un desbordamiento del b\u00fafer de la pila en \"tf.raw_ops.QuantizedResizeBilinear\" al manipular los valores de entrada para que el redondeo flotante resulta en un error de uno en uno al acceder a los elementos de la imagen. Esto es debido a que la implementaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) calcula dos n\u00fameros enteros (que representan los l\u00edmites superior e inferior) por techo y piso un valor de punto flotante. Para algunos valores de \"in\",\" interpolation-)upper[i]\" podr\u00eda ser menor que \"interpolation-)lower [i] \". Esto es un problema si \"interpolation-)upper[i]\" est\u00e1 limitado a \"in_size-1\" ya que significa que \"interpolation-)lower[i]\" apunta fuera de la imagen. Luego, en el c\u00f3digo de interpolaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), esto resultar\u00eda en un desbordamiento del b\u00fafer de la pila. La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.5.0. 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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          "baseScore": 2.5,
          "baseSeverity": "LOW",
          "attackVector": "LOCAL",
          "attackComplexity": "HIGH",
          "privilegesRequired": "LOW",
          "userInteraction": "NONE",
          "scope": "UNCHANGED",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "availabilityImpact": "LOW"
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        "exploitabilityScore": 1.0,
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        "exploitabilityScore": 3.9,
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        "acInsufInfo": false,
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        "obtainOtherPrivilege": false,
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      }
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      "description": [
        {
          "lang": "en",
          "value": "CWE-193"
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      "url": "https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7",
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