Описание
Bad alloc in StringNGrams caused by integer conversion
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.
The implementation 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.
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 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.
Ссылки
- 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
Пакеты
tensorflow
< 2.3.4
2.3.4
tensorflow
>= 2.4.0, < 2.4.3
2.4.3
tensorflow
= 2.5.0
2.5.1
tensorflow-cpu
< 2.3.4
2.3.4
tensorflow-cpu
>= 2.4.0, < 2.4.3
2.4.3
tensorflow-cpu
= 2.5.0
2.5.1
tensorflow-gpu
< 2.3.4
2.3.4
tensorflow-gpu
>= 2.4.0, < 2.4.3
2.4.3
tensorflow-gpu
= 2.5.0
2.5.1
Связанные уязвимости
TensorFlow is an end-to-end open source platform for machine learning. In affected versions 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. 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. We have patched the issue in GitHub 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
TensorFlow is an end-to-end open source platform for machine learning. ...
