nvd-json-data-feeds/CVE-2021/CVE-2021-295xx/CVE-2021-29569.json

{
  "id": "CVE-2021-29569",
  "sourceIdentifier": "security-advisories@github.com",
  "published": "2021-05-14T20:15:13.790",
  "lastModified": "2024-11-21T06:01:24.117",
  "vulnStatus": "Modified",
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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;La implementaci\u00f3n de \"tf.raw_ops.MaxPoolGradWithArgmax\" puede causar una lectura fuera de l\u00edmites de los datos asignados a la pila si el atacante suministra entradas especialmente dise\u00f1adas.&#xa0;La implementaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) asume que los tensores \"input_min\" y\" input_max\" tienen al menos un elemento, a medida que accede al primer elemento en dos matrices.&#xa0;Si los tensores est\u00e1n vac\u00edos, \".flat (T) ()\" es un objeto vac\u00edo, respaldado por una matriz vac\u00eda.&#xa0;Por lo tanto, acceder incluso al elemento 0 es una lectura fuera de l\u00edmites.&#xa0;La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.5.0.&#xa0;Tambi\u00e9n seleccionaremos este commit en TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 y TensorFlow 2.1.4"
    }
  ],
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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,
        "impactScore": 1.4
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        "type": "Primary",
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          "scope": "UNCHANGED",
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        "impactScore": 5.2
      }
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        "source": "nvd@nist.gov",
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          "baseScore": 3.6,
          "accessVector": "LOCAL",
          "accessComplexity": "LOW",
          "authentication": "NONE",
          "confidentialityImpact": "PARTIAL",
          "integrityImpact": "NONE",
          "availabilityImpact": "PARTIAL"
        },
        "baseSeverity": "LOW",
        "exploitabilityScore": 3.9,
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        "acInsufInfo": false,
        "obtainAllPrivilege": false,
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      "url": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e",
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