Intel Optimization For Tensorflow vulnerabilities
429 known vulnerabilities affecting intel/optimization_for_tensorflow.
Total CVEs
429
CISA KEV
0
Public exploits
0
Exploited in wild
0
Severity breakdown
CRITICAL5HIGH121MEDIUM200LOW103
Vulnerabilities
Page 5 of 22
CVE-2022-36027P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-36027 [MEDIUM] CWE-20 TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions
TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions
### Impact
When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.
```python
import tensorflow as tf
class QuantConv2DTransposed(tf.keras.layers.Layer):
def build(self, input_shape):
self.kernel = self.add_weight("ker
ghsaosv
CVE-2022-35941P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35941 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failure in `AvgPoolOp`
TensorFlow vulnerable to `CHECK` failure in `AvgPoolOp`
### Impact
The [`AvgPoolOp`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/avgpooling_op.cc#L56-L98) function takes an argument `ksize` that must be positive but is not checked. A negative `ksize` can trigger a `CHECK` failure and crash the program.
```python
import tensorflow as tf
import
ghsaosv
CVE-2022-35952P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35952 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failures in `UnbatchGradOp`
TensorFlow vulnerable to `CHECK` failures in `UnbatchGradOp`
### Impact
The [`UnbatchGradOp`](https://github.com/tensorflow/tensorflow/blob/769eddaf479c8debead9a59a72617d6ed6f0fe10/tensorflow/core/kernels/batch_kernels.cc#L891) function takes an argument `id` that is assumed to be a scalar. A nonscalar `id` can trigger a `CHECK` failure and crash the program.
```python
import numpy as np
import tensorfl
ghsaosv
CVE-2022-36013P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-36013 [MEDIUM] CWE-476 TensorFlow vulnerable to null-dereference in `mlir::tfg::GraphDefImporter::ConvertNodeDef`
TensorFlow vulnerable to null-dereference in `mlir::tfg::GraphDefImporter::ConvertNodeDef`
### Impact
When [`mlir::tfg::GraphDefImporter::ConvertNodeDef`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ir/importexport/graphdef_import.cc) tries to convert NodeDefs without an op name, it crashes.
```cpp
Status GraphDefImporter::ConvertNodeDef(OpBuilder &
ghsaosv
CVE-2022-35940P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35940 [MEDIUM] CWE-190 TensorFlow vulnerable to Int overflow in `RaggedRangeOp`
TensorFlow vulnerable to Int overflow in `RaggedRangeOp`
### Impact
The [`RaggedRangOp`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/kernels/ragged_range_op.cc#L74-L88) function takes an argument `limits` that is eventually used to construct a `TensorShape` as an `int64`. If `limits` is a very large float, it can overflow when converted to an `int64
ghsaosv
CVE-2022-36015P3LOW≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-36015 [LOW] CWE-190 TensorFlow vulnerable to integer overflow in math ops
TensorFlow vulnerable to integer overflow in math ops
### Impact
When [`RangeSize`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/math_ops.cc) receives values that do not fit into an `int64_t`, it crashes.
```cpp
auto size = (std::is_integral::value
? ((Eigen::numext::abs(limit - start) +
Eigen::numext::abs(delta) - T(1)) /
Eigen::numext::abs(delta))
: (Eigen::numext::ceil(
Eigen::numex
ghsaosv
CVE-2022-41899P3MEDIUM≥ 0, < 2.8.4≥ 2.9.0, < 2.9.3+1 more2022-11-21
CVE-2022-41899 [MEDIUM] CWE-20 `CHECK` fail via inputs in `SdcaOptimizer`
`CHECK` fail via inputs in `SdcaOptimizer`
### Impact
Inputs `dense_features` or `example_state_data` not of rank 2 will trigger a `CHECK` fail in [`SdcaOptimizer`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/sdca_internal.cc).
```python
import tensorflow as tf
tf.raw_ops.SdcaOptimizer(
sparse_example_indices=4 * [tf.random.uniform([5,5,5,3], dtype=tf.dtypes.int64, maxval=100)],
sparse_f
ghsaosv
CVE-2022-41887P3MEDIUM≥ 0, < 2.9.3≥ 2.10.0, < 2.10.12022-11-21
CVE-2022-41887 [MEDIUM] CWE-131 Overflow in `tf.keras.losses.poisson`
Overflow in `tf.keras.losses.poisson`
### Impact
[`tf.keras.losses.poisson`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py) receives a `y_pred` and `y_true` that are passed through `functor::mul` in [`BinaryOp`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h). If the resulting dimensions overflow an `int32`, TensorFlow will crash due to
ghsaosv
CVE-2022-41893P3MEDIUM≥ 0, < 2.8.4≥ 2.9.0, < 2.9.3+1 more2022-11-21
CVE-2022-41893 [MEDIUM] CWE-617 `CHECK_EQ` fail in `tf.raw_ops.TensorListResize`
`CHECK_EQ` fail in `tf.raw_ops.TensorListResize`
### Impact
If [`tf.raw_ops.TensorListResize`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/list_kernels.cc) is given a nonscalar value for input `size`, it results `CHECK` fail which can be used to trigger a denial of service attack.
```python
import numpy as np
import tensorflow as tf
a = data_structures.tf_tensor_list_new(elements =
ghsaosv
CVE-2022-35982P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35982 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `SparseBincount`
TensorFlow vulnerable to segfault in `SparseBincount`
### Impact
If `SparseBincount` is given inputs for `indices`, `values`, and `dense_shape` that do not make a valid sparse tensor, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
binary_output = True
indices = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int64, seed=-1288)
ghsaosv
CVE-2022-35973P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35973 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedMatMul`
TensorFlow vulnerable to segfault in `QuantizedMatMul`
### Impact
If `QuantizedMatMul` is given nonscalar input for:
- `min_a`
- `max_a`
- `min_b`
- `max_b`
It gives a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
Toutput = tf.qint32
transpose_a = False
transpose_b = False
Tactivation = tf.quint8
a = tf.constant(7, shape=[3,4], dtype=tf.quint8)
b = tf.const
ghsaosv
CVE-2023-33976P3HIGH≥ 0, < 2.12.12024-07-30
CVE-2023-33976 [HIGH] CWE-190 TensorFlow has segfault in array_ops.upper_bound
TensorFlow has segfault in array_ops.upper_bound
### Impact
`array_ops.upper_bound` causes a segfault when not given a rank 2 tensor.
### Patches
We have patched the issue in GitHub commit [915884fdf5df34aaedd00fc6ace33a2cfdefa586](https://github.com/tensorflow/tensorflow/commit/915884fdf5df34aaedd00fc6ace33a2cfdefa586).
The fix will be included in TensorFlow 2.13. We will also cherrypick this commit in TensorFlow
ghsaosv
CVE-2022-35988P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35988 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank`
TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank`
### Impact
When `tf.linalg.matrix_rank` receives an empty input `a`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
a = tf.constant([], shape=[0, 1, 1], dtype=tf.float32)
tf.linalg.matrix_rank(a=a)
```
### Patches
We have patched the issue in GitHub commit
ghsaosv
CVE-2022-35997P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35997 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `tf.sparse.cross`
TensorFlow vulnerable to `CHECK` fail in `tf.sparse.cross`
### Impact
If `tf.sparse.cross` receives an input `separator` that is not a scalar, it gives a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
tf.sparse.cross(inputs=[],name='a',separator=tf.constant(['a', 'b'],dtype=tf.string))
```
### Patches
We have patched the issue in GitHub commit [83d
ghsaosv
CVE-2022-35998P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35998 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `EmptyTensorList`
TensorFlow vulnerable to `CHECK` fail in `EmptyTensorList`
### Impact
If `EmptyTensorList` receives an input `element_shape` with more than one dimension, it gives a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
tf.raw_ops.EmptyTensorList(element_shape=tf.ones(dtype=tf.int32, shape=[1, 0]), max_num_elements=tf.constant(1),element_dtype=tf.int32)
``
ghsaosv
CVE-2022-35994P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35994 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `CollectiveGather`
TensorFlow vulnerable to `CHECK` fail in `CollectiveGather`
### Impact
When `CollectiveGather` receives an scalar input `input`, it gives a `CHECK` fails that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=1
arg_1=1
arg_2=1
arg_3=1
arg_4=(3, 3,3)
arg_5='auto'
arg_6=0
arg_7=''
tf.raw_ops.CollectiveGather(input=arg_0, group_size=arg_1, group_key=arg_2,
instance_k
ghsaosv
CVE-2022-35993P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35993 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `SetSize`
TensorFlow vulnerable to `CHECK` fail in `SetSize`
### Impact
When `SetSize` receives an input `set_shape` that is not a 1D tensor, it gives a `CHECK` fails that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=1
arg_1=[1,1]
arg_2=1
arg_3=True
arg_4=''
tf.raw_ops.SetSize(set_indices=arg_0, set_values=arg_1, set_shape=arg_2,
validate_indices=arg_3, name=arg_4)
```
### Pat
ghsaosv
CVE-2022-36019P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-36019 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel`
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannel`
### Impact
If `FakeQuantWithMinMaxVarsPerChannel` is given `min` or `max` tensors of a rank other than one, it results in a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
num_bits = 8
narrow_range = False
inputs = tf.constant(0, shape=[4], dtype=tf.float32
ghsaosv
CVE-2022-35968P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35968 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`
TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`
### Impact
The implementation of `AvgPoolGrad` does not fully validate the input `orig_input_shape`. This results in a `CHECK` failure which can be used to trigger a denial of service attack:
```python
import tensorflow as tf
ksize = [1, 2, 2, 1]
strides = [1, 2, 2, 1]
padding = "VALID"
data_format = "NHWC"
orig_input_shape = tf.constant(-536870912, shap
ghsaosv
CVE-2022-35963P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35963 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad`
TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad`
### Impact
The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack.
```python
import tensorflow as tf
overlapping = True
orig_input_tensor_shape = tf.constant(
ghsaosv