CVE-2021-29582: Heap OOB read in `tf.raw_ops.Dequantize`
First published: Fri May 14 2021(Updated: )
### Impact Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data: ```python import tensorflow as tf input_tensor=tf.constant( [75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\ -10, -10, -10, -10], shape=[5, 10], dtype=tf.int32) input_tensor=tf.cast(input_tensor, dtype=tf.quint8) min_range = tf.constant([-10], shape=[1], dtype=tf.float32) max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32) tf.raw_ops.Dequantize( input=input_tensor, min_range=min_range, max_range=max_range, mode='SCALED', narrow_range=True, axis=0, dtype=tf.dtypes.float32) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape: ```cc if (num_slices == 1) { const float min_range = input_min_tensor.flat<float>()(0); const float max_range = input_max_tensor.flat<float>()(0); DequantizeTensor(ctx, input, min_range, max_range, &float_output); } else { ... auto min_ranges = input_min_tensor.vec<float>(); auto max_ranges = input_max_tensor.vec<float>(); for (int i = 0; i < num_slices; ++i) { DequantizeSlice(ctx->eigen_device<Device>(), ctx, input_tensor.template chip<1>(i), min_ranges(i), max_ranges(i), output_tensor.template chip<1>(i)); ... } } ``` ### Patches We have patched the issue in GitHub commit [5899741d0421391ca878da47907b1452f06aaf1b](https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b). 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/5899741d0421391ca878da47907b1452f06aaf1b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c45w-2wxr-pp53
- https://nvd.nist.gov/vuln/detail/CVE-2021-29582
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-510.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-708.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-219.yaml
- https://github.com/advisories/GHSA-c45w-2wxr-pp53 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-29582?
CVE-2021-29582 has a medium severity rating due to the potential for heap overflow attacks.
How do I fix CVE-2021-29582?
To fix CVE-2021-29582, upgrade to TensorFlow version 2.4.2 or later.
Which versions of TensorFlow are affected by CVE-2021-29582?
CVE-2021-29582 affects TensorFlow versions prior to 2.1.4, between 2.2.0 and 2.2.3, between 2.3.0 and 2.3.3, and between 2.4.0 and 2.4.2.
What is the impact of CVE-2021-29582?
The impact of CVE-2021-29582 is that it allows an attacker to trigger a read from outside of bounds of heap allocated data.
Who is affected by CVE-2021-29582?
Anyone using the vulnerable versions of TensorFlow, especially in applications involving user-provided data, could be affected by CVE-2021-29582.
- collector/nvd-index
- agent/type
- agent/remedy
- collector/mitre-cve
- source/MITRE
- agent/title
- agent/severity
- agent/weakness
- agent/first-publish-date
- collector/github-advisory-latest
- source/GitHub
- alias/GHSA-c45w-2wxr-pp53
- alias/CVE-2021-29582
- agent/software-canonical-lookup
- agent/references
- agent/last-modified-date
- agent/softwarecombine
- agent/description
- agent/event
- collector/nvd-cve
- source/NVD
- agent/author
- 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