CVE-2020-26271
published 2020-12-10CVE-2020-26271: In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation…
low3.3CVSS 3.1
AVLACLPRLUINSUCLINAN
In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
Affected
17 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| debian | tensorflow | — | — |
| tensorflow | < 1.15.5 | 1.15.5 | |
| tensorflow | >= 2.0.0 < 2.0.4 | 2.0.4 | |
| tensorflow | >= 2.1.0 < 2.1.3 | 2.1.3 | |
| tensorflow | >= 2.2.0 < 2.2.2 | 2.2.2 | |
| tensorflow | >= 2.3.0 < 2.3.2 | 2.3.2 | |
| intel | optimization_for_tensorflow | >= 0 < 0cc38aaa4064fd9e79101994ce9872c6d91f816b | 0cc38aaa4064fd9e79101994ce9872c6d91f816b |
| intel | optimization_for_tensorflow | >= 0 < 1.15.5 | 1.15.5 |
| intel | optimization_for_tensorflow | >= 2.0.0 < 2.0.4 | 2.0.4 |
| intel | optimization_for_tensorflow | >= 2.1.0 < 2.1.3 | 2.1.3 |
| intel | optimization_for_tensorflow | >= 2.2.0 < 2.2.2 | 2.2.2 |
| intel | optimization_for_tensorflow | >= 2.3.0 < 2.3.2 | 2.3.2 |
| tensorflow | tensorflow | < 1.15.5 | 1.15.5 |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
| tensorflow | tensorflow | — | — |
OSV
CVE-2020-26271: In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computat
osv·2020-12-10
CVE-2020-26271 CVE-2020-26271: In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computat
In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the bo
GHSA
Heap out of bounds access in MakeEdge in TensorFlow
ghsa·2020-12-10
CVE-2020-26271 [MEDIUM] CWE-125 Heap out of bounds access in MakeEdge in TensorFlow
Heap out of bounds access in MakeEdge in TensorFlow
### Impact
Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality:
```cc
DataType src_out = src->output_type(output_index);
DataType dst_in = dst->input_type(input_index
OSV
Heap out of bounds access in MakeEdge in TensorFlow
osv·2020-12-10
CVE-2020-26271 [MEDIUM] Heap out of bounds access in MakeEdge in TensorFlow
Heap out of bounds access in MakeEdge in TensorFlow
### Impact
Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality:
```cc
DataType src_out = src->output_type(output_index);
DataType dst_in = dst->input_type(input_index
Debian
CVE-2020-26271: tensorflow - In affected versions of TensorFlow under certain cases, loading a saved model ca...
vendor_debian·2020·CVSS 4.4
CVE-2020-26271 [MEDIUM] CVE-2020-26271: tensorflow - In affected versions of TensorFlow under certain cases, loading a saved model ca...
In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the bo
No detection rules found.
No public exploits indexed.
No writeups or analysis indexed.
https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816bhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mwhttps://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816bhttps://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw
2020-12-10
Published