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-2018-8825P3HIGH≥ 1.5.0, < 1.7.12019-04-24
CVE-2018-8825 [HIGH] CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buffer in Google TensorFlow
Improper Restriction of Operations within the Bounds of a Memory Buffer in Google TensorFlow
Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local). Users passing a malformed or malicious version of a TFLite graph into TOCO will cause TOCO to crash or cause a buffer overflow, potentially allowing malicious code to be ex
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-2022-35960P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35960 [MEDIUM] CWE-617 TensorFlow vulnerable to `CHECK` failure in `TensorListReserve` via missing validation
TensorFlow vulnerable to `CHECK` failure in `TensorListReserve` via missing validation
### Impact
In [`core/kernels/list_kernels.cc's TensorListReserve`](https://github.com/tensorflow/tensorflow/blob/c8ba76d48567aed347508e0552a257641931024d/tensorflow/core/kernels/list_kernels.cc#L322-L325), `num_elements` is assumed to be a tensor of size 1. When a `num_elements` of more than
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-2022-36016P3LOW≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-36016 [LOW] CWE-617 TensorFlow vulnerable to `CHECK`-fail in `tensorflow::full_type::SubstituteFromAttrs`
TensorFlow vulnerable to `CHECK`-fail in `tensorflow::full_type::SubstituteFromAttrs`
### Impact
When [`tensorflow::full_type::SubstituteFromAttrs`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/math_ops.cc) receives a `FullTypeDef& t` that is not exactly three args, it triggers a `CHECK`-fail instead of returning a status.
```cpp
Status SubstituteForEach
ghsaosv
CVE-2022-35970P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35970 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`
TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`
### Impact
If `QuantizedInstanceNorm` is given `x_min` or `x_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
output_range_given = False
given_y_min = 0
given_y_max = 0
variance_epsilon = 1e-05
min_separation = 0.001
x = tf.constant(88, shape=[1,4
ghsaosv
CVE-2022-35979P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35979 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`
TensorFlow vulnerable to segfault in `QuantizedRelu` and `QuantizedRelu6`
### Impact
If `QuantizedRelu` or `QuantizedRelu6` are given nonscalar inputs for `min_features` or `max_features`, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
out_type = tf.quint8
features = tf.constant(28, shape=[4,2], dtype=tf.quint8)
min_feature
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-2022-36017P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-36017 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `Requantize`
TensorFlow vulnerable to segfault in `Requantize`
### Impact
If `Requantize` is given `input_min`, `input_max`, `requested_output_min`, `requested_output_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
out_type = tf.quint8
input = tf.constant([1], shape=[3], dtype=tf.qint32)
input_min = tf.constant([], shape=[0], dty
ghsaosv
CVE-2022-35986P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35986 [MEDIUM] TensorFlow vulnerable to segfault in `RaggedBincount`
TensorFlow vulnerable to segfault in `RaggedBincount`
### Impact
If `RaggedBincount` is given an empty input tensor `splits`, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
binary_output = True
splits = tf.random.uniform(shape=[0], minval=-10000, maxval=10000, dtype=tf.int64, seed=-7430)
values = tf.random.uniform(shape=[], minval=-10000, maxval=10000
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-2022-35974P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35974 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange`
TensorFlow vulnerable to segfault in `QuantizeDownAndShrinkRange`
### Impact
If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
out_type = tf.quint8
input = tf.constant([1], shape=[3], dtype=tf.qint32)
input_min = tf.constant([], shape=[0], dtype=
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-2022-41888P3MEDIUM≥ 0, < 2.8.4≥ 2.9.0, < 2.9.3+1 more2022-11-21
CVE-2022-41888 [MEDIUM] CWE-20 FPE in `tf.image.generate_bounding_box_proposals`
FPE in `tf.image.generate_bounding_box_proposals`
### Impact
When running on GPU, [`tf.image.generate_bounding_box_proposals`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/generate_box_proposals_op.cu.cc) receives a `scores` input that must be of rank 4 but is not checked.
```python
import tensorflow as tf
a = tf.constant(value=[[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])
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-2022-35972P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35972 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `QuantizedBiasAdd`
TensorFlow vulnerable to segfault in `QuantizedBiasAdd`
### Impact
If `QuantizedBiasAdd` is given `min_input`, `max_input`, `min_bias`, `max_bias` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
out_type = tf.qint32
input = tf.constant([85,170,255], shape=[3], dtype=tf.quint8)
bias = tf.constant(43, shape=[2,3], dty
ghsaosv
CVE-2022-35964P3MEDIUM≥ 0, < 2.7.2≥ 2.8.0, < 2.8.1+1 more2022-09-16
CVE-2022-35964 [MEDIUM] CWE-20 TensorFlow vulnerable to segfault in `BlockLSTMGradV2`
TensorFlow vulnerable to segfault in `BlockLSTMGradV2`
### Impact
The implementation of `BlockLSTMGradV2` does not fully validate its inputs.
- `wci`, `wcf`, `wco`, `b` must be rank 1
- `w`, cs_prev`, `h_prev` must be rank 2
- `x` must be rank 3
This results in a a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
use_peephole = False
seq_len_max = tf.constant(
ghsaosv