CVE-2021-29571 Memory corruption in `DrawBoundingBoxesV2`

2021-05-1419:16:27

CWE-787

GitHub_M

www.cve.org

4.5 Medium

CVSS3

Attack Vector

LOCAL

Attack Complexity

HIGH

Privileges Required

LOW

User Interaction

NONE

Scope

UNCHANGED

Confidentiality Impact

LOW

Integrity Impact

LOW

Availability Impact

LOW

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

0.001 Low

EPSS

Percentile

40.8%

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/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of boxes input is 4, as required by the op. Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in boxes is less than 4, accesses similar to tboxes(b, bb, 3) will access data outside of bounds. Further during code execution there are also writes to these indices. 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"
      }
    ]
  }
]