CVE-2021-29580 Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`

2021-05-1419:15:43

CWE-908

GitHub_M

www.cve.org

tensorflow

fractionalmaxpoolgrad

denial of service

CVSS3

2.5

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

EPSS

Percentile

12.8%

JSON

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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"
      }
    ]
  }
]