CVE-2021-29569 Heap out of bounds read in `RequantizationRange`

2021-05-1419:16:36

CWE-125

GitHub_M

www.cve.org

2.5 Low

CVSS3

Attack Vector

LOCAL

Attack Complexity

HIGH

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

NONE

Integrity Impact

NONE

Availability Impact

LOW

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

0.0005 Low

EPSS

Percentile

17.8%

JSON

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.

CNA Affected

[
  {
    "product": "tensorflow",
    "vendor": "tensorflow",
    "versions": [
      {
        "status": "affected",
        "version": "< 2.1.4"
      },
      {
        "status": "affected",
        "version": ">= 2.2.0, < 2.2.3"
      },
      {
        "status": "affected",
        "version": ">= 2.3.0, < 2.3.3"
      },
      {
        "status": "affected",
        "version": ">= 2.4.0, < 2.4.2"
      }
    ]
  }
]