Heap buffer overflow in `QuantizedReshape`
Impact
An attacker can cause a heap buffer overflow in QuantizedReshape by passing in invalid thresholds for the quantization:
import tensorflow as tf
tensor = tf.constant([], dtype=tf.qint32)
shape = tf.constant([], dtype=tf.int32)
input_min = tf.constant([], dtype=tf.float32)
input_max = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedReshape(tensor=tensor, shape=shape, input_min=input_min, input_max=input_max)
This is because the implementation assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:
const auto& input_min_float_tensor = ctx->input(2);
...
const float input_min_float = input_min_float_tensor.flat<float>()(0);
const auto& input_max_float_tensor = ctx->input(3);
...
const float input_max_float = input_max_float_tensor.flat<float>()(0);
However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.
Patches
We have patched the issue in GitHub commit a324ac84e573fba362a5e53d4e74d5de6729933e.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
Affected software
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| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| 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 |
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x
- https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e
- https://nvd.nist.gov/vuln/detail/CVE-2021-29536
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-464.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-662.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-173.yaml
- https://github.com/tensorflow/tensorflow
- CVSS vector
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Weakness (CWE)
CWE-787- Platform
- Python (PyPI)
- Package
tensorflow, tensorflow-cpu, tensorflow-gpu- Disclosed
- 14.05.2021
- Updated
- 11.10.2026 13:19
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2021-173 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-464 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-662 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-2gfx-95x2-5v3x - CC BY 4.0
Vulnerability history: tensorflow-cpu
All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.
- Total entries
- 430
- First → latest
- 04.2019 07.2024
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- Critical 6
- High 111
- Medium 208
- Low 105
Disclosures per year
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Heap buffer overflow in `QuantizedReshape` this entry
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