An attacker can trigger a denial of service via a CHECK
failure by passing an empty image to tf.raw_ops.DrawBoundingBoxes
:
import tensorflow as tf
images = tf.fill([53, 0, 48, 1], 0.)
boxes = tf.fill([53, 31, 4], 0.)
boxes = tf.Variable(boxes)
boxes[0, 0, 0].assign(3.90621)
tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes)
This is because the implementation uses CHECK_*
assertions instead of OP_REQUIRES
to validate user controlled inputs. Whereas OP_REQUIRES
allows returning an error condition back to the user, the CHECK_*
macros result in a crash if the condition is false, similar to assert
.
const int64 max_box_row_clamp = std::min<int64>(max_box_row, height - 1);
...
CHECK_GE(max_box_row_clamp, 0);
In this case, height
is 0 from the images
input. This results in max_box_row_clamp
being negative and the assertion being falsified, followed by aborting program execution.
We have patched the issue in GitHub commit b432a38fe0e1b4b904a6c222cbce794c39703e87.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
{ "nvd_published_at": "2021-05-14T20:15:00Z", "cwe_ids": [ "CWE-754" ], "severity": "LOW", "github_reviewed": true, "github_reviewed_at": "2021-05-18T22:50:31Z" }