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GHSA-xqfj-35wv-m3cr

Null pointer dereference in `StringNGrams`

Source: GitHub Advisory Database (reviewed) Low CVE-2021-29541

Packages

EcosystemPackageAffectedFixed in
PyPI tensorflow < 2.1.4 2.1.4
PyPI tensorflow >= 2.2.0, < 2.2.3 2.2.3
PyPI tensorflow >= 2.3.0, < 2.3.3 2.3.3
PyPI tensorflow >= 2.4.0, < 2.4.2 2.4.2
PyPI tensorflow-cpu < 2.1.4 2.1.4
PyPI tensorflow-cpu >= 2.2.0, < 2.2.3 2.2.3
PyPI tensorflow-cpu >= 2.3.0, < 2.3.3 2.3.3
PyPI tensorflow-cpu >= 2.4.0, < 2.4.2 2.4.2
PyPI tensorflow-gpu < 2.1.4 2.1.4
PyPI tensorflow-gpu >= 2.2.0, < 2.2.3 2.2.3
PyPI tensorflow-gpu >= 2.3.0, < 2.3.3 2.3.3
PyPI tensorflow-gpu >= 2.4.0, < 2.4.2 2.4.2

Details

As written in the advisory (Markdown source, shown as plain text).

### Impact An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`: ```python import tensorflow as tf data=tf.constant([''] * 11, shape=[11], dtype=tf.string) splits = [0]*115 splits.append(3) data_splits=tf.constant(splits, shape=[116], dtype=tf.int64) tf.raw_ops.StringNGrams(data=data, data_splits=data_splits, separator=b'Ss', ngram_widths=[7,6,11], left_pad='ABCDE', right_pad=b'ZYXWVU', pad_width=50, preserve_short_sequences=True) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in [`ngrams_data`](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. ### Patches We have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b). 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.

References

External links from the advisory. They lead to third-party sites.