CHECK-fail in DrawBoundingBoxes
Impact
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.
Patches
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.
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.
Czego dotyczy
Kliknij technologię, aby zobaczyć wszystkie dotyczące jej podatności.
| Platforma | Paczka | Podatne wersje | Poprawka w wersji |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-cpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-gpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
Odnośniki
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69
- https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87
- 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/tensorflow/tensorflow
- Wektor CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Słabość (CWE)
CWE-754- Platforma
- Python (PyPI)
- Paczka
tensorflow, tensorflow-cpu, tensorflow-gpu- Ujawniono
- 14.05.2021
- Aktualizacja
- 11.10.2026 13:19
Źródła i licencje
OSV / GitHub Advisory Database - PYSEC-2021-170 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-461 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-659 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-393f-2jr3-cp69 - CC BY 4.0
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