Heap buffer overflow in `BandedTriangularSolve`
Impact
An attacker can trigger a heap buffer overflow in Eigen implementation of tf.raw_ops.BandedTriangularSolve:
import tensorflow as tf
import numpy as np
matrix_array = np.array([])
matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32)
rhs_array = np.array([1,1])
rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(1,2)),dtype=tf.float32)
tf.raw_ops.BandedTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor)The implementation calls ValidateInputTensors for input validation but fails to validate that the two tensors are not empty:
void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0, const Tensor& in1) {
OP_REQUIRES(
ctx, in0.dims() >= 2,
errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));
OP_REQUIRES(
ctx, in1.dims() >= 2,
errors::InvalidArgument("In[1] ndims must be >= 2: ", in1.dims()));
}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.
Czego dotyczy
Kliknij technologię, aby zobaczyć wszystkie dotyczące jej podatności.
| Platforma | Paczka | Podatne wersje | Poprawka w wersji |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-cpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-gpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
Odnośniki
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2xgj-xhgf-ggjv
- https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2
- https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0
- https://nvd.nist.gov/vuln/detail/CVE-2021-29612
- 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
- Wektor CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L- Słabość (CWE)
CWE-120, CWE-787- Platforma
- Python (PyPI)
- Paczka
tensorflow, tensorflow-cpu, tensorflow-gpu- Ujawniono
- 14.05.2021
- Aktualizacja
- 11.10.2026 13:19
Źródła i licencje
OSV / GitHub Advisory Database - PYSEC-2021-249 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-540 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-738 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-2xgj-xhgf-ggjv - CC BY 4.0
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