GHSA-h6jh-7gv5-28vg

Dashboard / Vulnerabilities / GHSA-h6jh-7gv5-28vg

GHSA-h6jh-7gv5-28vg

Summary: Bad alloc in `StringNGrams` caused by integer conversion

Details: ### Impact The implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. ```python import tensorflow as tf tf.raw_ops.StringNGrams( data=['',''], data_splits=[0,2], separator=' '*100, ngram_widths=[-80,0,0,-60], left_pad=' ', right_pad=' ', pad_width=100, preserve_short_sequences=False) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. ### Patches We have patched the issue in GitHub commit [c283e542a3f422420cfdb332414543b62fc4e4a5](https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5). 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 members of the Aivul Team from Qihoo 360.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6jh-7gv5-28vg, https://nvd.nist.gov/vuln/detail/CVE-2021-37646, https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-559.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-757.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-268.yaml, https://github.com/tensorflow/tensorflow