Описание
Heap buffer overflow in BandedTriangularSolve
Impact
An attacker can trigger a heap buffer overflow in Eigen implementation of tf.raw_ops.BandedTriangularSolve:
The implementation calls ValidateInputTensors for input validation but fails to validate that the two tensors are not empty:
Furthermore, since OP_REQUIRES macro only stops execution of current function after setting ctx->status() to a non-OK value, callers of helper functions that use OP_REQUIRES must check value of ctx->status() before continuing. This doesn't happen in this op's implementation, hence the validation that is present is also not effective.
Patches
We have patched the issue in GitHub commit ba6822bd7b7324ba201a28b2f278c29a98edbef2 followed by GitHub commit 0ab290774f91a23bebe30a358fde4e53ab4876a0.
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 for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Ye Zhang and Yakun Zhang of Baidu X-Team.
Ссылки
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2xgj-xhgf-ggjv
- https://nvd.nist.gov/vuln/detail/CVE-2021-29612
- https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0
- https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-540.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-738.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-249.yaml
Пакеты
tensorflow
< 2.1.4
2.1.4
tensorflow
>= 2.2.0, < 2.2.3
2.2.3
tensorflow
>= 2.3.0, < 2.3.3
2.3.3
tensorflow
>= 2.4.0, < 2.4.2
2.4.2
tensorflow-cpu
< 2.1.4
2.1.4
tensorflow-cpu
>= 2.2.0, < 2.2.3
2.2.3
tensorflow-cpu
>= 2.3.0, < 2.3.3
2.3.3
tensorflow-cpu
>= 2.4.0, < 2.4.2
2.4.2
tensorflow-gpu
< 2.1.4
2.1.4
tensorflow-gpu
>= 2.2.0, < 2.2.3
2.2.3
tensorflow-gpu
>= 2.3.0, < 2.3.3
2.3.3
tensorflow-gpu
>= 2.4.0, < 2.4.2
2.4.2
Связанные уязвимости
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. The fix will be included in TensorF
TensorFlow is an end-to-end open source platform for machine learning. ...