CVE-2021-29533: CHECK-fail in DrawBoundingBoxes
First published: Fri May 14 2021(Updated: )
### Impact An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`: ```python 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](https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) 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`. ```cc 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. ### Patches We have patched the issue in GitHub commit [b432a38fe0e1b4b904a6c222cbce794c39703e87](https://github.com/tensorflow/tensorflow/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. ### 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 Yakun Zhang and Ying Wang of Baidu X-Team.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | <2.1.4 | |
| Google TensorFlow | >=2.2.0<2.2.3 | |
| Google TensorFlow | >=2.3.0<2.3.3 | |
| Google TensorFlow | >=2.4.0<2.4.2 | |
| pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
Never miss a vulnerability like this again
Sign up to SecAlerts for real-time vulnerability data matched to your software, aggregated from hundreds of sources.
Reference Links
- https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69
- https://nvd.nist.gov/vuln/detail/CVE-2021-29533
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-461.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-659.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-170.yaml
- https://github.com/advisories/GHSA-393f-2jr3-cp69 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29533?
CVE-2021-29533 has a medium severity rating due to potential denial of service when handling empty images.
How do I fix CVE-2021-29533?
To fix CVE-2021-29533, upgrade to TensorFlow version 2.4.2 or later.
Which versions are affected by CVE-2021-29533?
CVE-2021-29533 affects TensorFlow versions below 2.1.4, as well as versions 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2.
Can CVE-2021-29533 lead to application crashes?
Yes, CVE-2021-29533 can cause application crashes due to denial of service triggered by empty images.
Is there a workaround for CVE-2021-29533?
Currently, there are no reliable workarounds for CVE-2021-29533 except for upgrading to the fixed version.
- collector/nvd-index
- agent/type
- agent/remedy
- collector/mitre-cve
- source/MITRE
- agent/weakness
- agent/title
- agent/severity
- agent/first-publish-date
- collector/github-advisory-latest
- source/GitHub
- alias/GHSA-393f-2jr3-cp69
- alias/CVE-2021-29533
- agent/software-canonical-lookup
- agent/author
- agent/references
- agent/last-modified-date
- agent/description
- collector/nvd-cve
- source/NVD
- agent/softwarecombine
- agent/event
- collector/github-advisory
- agent/trending
- agent/tags
- agent/source
- vendor/google
- canonical/google tensorflow
- version/google tensorflow/2.2.0
- version/google tensorflow/2.3.0
- version/google tensorflow/2.4.0
- package-manager/pip