Open-Source Security Intelligence

Know every vulnerability
before it knows you.

DevGuard continuously monitors your dependencies and alerts you when CVEs like this one affect your stack — with real-time threat intelligence built for developers.

Search

GHSA-whr9-vfh2-7hm6

MediumCVSS 4.5 / 10
Published May 21, 2021·Last modified Mar 13, 2026
Affected Components(0)

No affected components available

Description

Impact

The implementation of tf.raw_ops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:

import tensorflow as tf

images = tf.fill([10, 96, 0, 1], 0.)
boxes = tf.fill([10, 53, 0], 0.)
colors = tf.fill([0, 1], 0.)

tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors)

The implementation 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:

const auto tboxes = boxes.tensor<T, 3>();
for (int64 bb = 0; bb < num_boxes; ++bb) {
  ...
  const int64 min_box_row = static_cast<float>(tboxes(b, bb, 0)) * (height - 1);
  const int64 max_box_row = static_cast<float>(tboxes(b, bb, 2)) * (height - 1);
  const int64 min_box_col = static_cast<float>(tboxes(b, bb, 1)) * (width - 1);
  const int64 max_box_col = static_cast<float>(tboxes(b, bb, 3)) * (width - 1);
  ...
}

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.

Patches

We have patched the issue in GitHub commit 79865b542f9ffdc9caeb255631f7c56f1d4b6517.

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

Risk Scores
Base Score
4.5

The vulnerability requires local access to the device to be exploited. It is difficult for an attacker to exploit this vulnerability and may require special conditions. An attacker needs basic access or low-level privileges. No user interaction is needed for the attacker to exploit this vulnerability. The impact is confined to the system where the vulnerability exists. There is a low impact on the confidentiality of the information. There is a low impact on the integrity of the data. There is a low impact on the availability of the system.

Threat Intelligence
4.1

Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.

EPSS
0.24%

The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.

Exploit
Not available

We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.

Browse More

Scan your project

Continuously monitor your dependencies and get alerted when vulnerabilities like this one affect your stack.

Checkout DevGuard