aswin1906/github-advisory-2021.csv huggingface dataset:
Cross-site scripting in phpoffice/phpspreadsheet
Published: 2021-05-06T18:53:37Z
Aliases: CVE-2020-7776

This affects the package phpoffice/phpspreadsheet. The library is vulnerable to XSS when creating an html output from an excel file by adding a comment on any cell. The root cause of this issue is within the HTML writer where user comments are concatenated as part of link and this is returned as HTML.
OS Command Injection in gulkp-styledocco
Published: 2021-05-07T16:14:48Z
Aliases: CVE-2020-7607

gulp-styledocco through 0.0.3 allows execution of arbitrary commands. The argument `options` of the exports function in `index.js` can be controlled by users without any sanitization.
Reference binding to nullptr in `SdcaOptimizer`
Published: 2021-05-21T14:25:31Z
Aliases: CVE-2021-29572

### Impact
The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer:

```python
import tensorflow as tf

sparse_example_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_indices = [tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_values = []

dense_features = []
dense_weights = []

example_weights = tf.constant((0.0), dtype=tf.float32)
example_labels = tf.constant((0.0), dtype=tf.float32)

sparse_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_weights = [tf.constant((0.0), dtype=tf.float32), tf.constant((0.0), dtype=tf.float32)]
  
example_state_data = tf.constant([0.0, 0.0, 0.0, 0.0], shape=[1, 4], dtype=tf.float32)
  
tf.raw_ops.SdcaOptimizer(
  sparse_example_indices=sparse_example_indices,
  sparse_feature_indices=sparse_feature_indices,
  sparse_feature_values=sparse_feature_values, dense_features=dense_features,
  example_weights=example_weights, example_labels=example_labels, 
  sparse_indices=sparse_indices, sparse_weights=sparse_weights, 
  dense_weights=dense_weights, example_state_data=example_state_data,
  loss_type="logistic_loss", l1=0.0, l2=0.0, num_loss_partitions=1,
  num_inner_iterations=1, adaptative=False)
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all [constraints expected by the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer).

### Patches
We have patched the issue in GitHub commit [f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb](https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Prototype pollution in 101
Published: 2021-05-17T20:57:40Z
Aliases: CVE-2021-25943

Prototype pollution vulnerability in '101' versions 1.0.0 through 1.6.3 allows an attacker to cause a denial of service and may lead to remote code execution.
Improper Sanitizing of plugin names in helm
Published: 2021-05-24T16:57:12Z
Aliases: CVE-2020-15186

### Impact

Security researchers at Trail of Bits discovered that plugin names are not sanitized properly. As a result, a malicious plugin author could use characters in a plugin name that would result in unexpected behavior, such as duplicating the name of another plugin or spoofing the output to `helm --help`.

### Patches

This issue has been patched in Helm 3.3.2.

### Workarounds

Do not install untrusted Helm plugins. Examine the `name` field in the `plugin.yaml` file for a plugin, looking for characters outside of the [a-zA-Z0-9._-] range.
Possible XSS when using SSO with the CLI in github.com/argoproj/argo-cd/v2
Published: 2021-05-21T14:31:41Z
Aliases: CVE-2021-23347

### Impact

When using SSO with the Argo CD CLI, a malicious SSO provider could have sent specially crafted error message that would result in XSS on the client by means of executing arbitrary JavaScript code.

We believe the exploitation of this vulnerability is only be possible when Argo CD is connected to a compromised/malicious SSO provider.

### Patches

A patch for this vulnerability is available with the v1.8.7 and v1.7.14 releases of Argo CD.

### Workarounds

* Do not use SSO with the CLI when you don't trust your SSO provider


If you have any questions or comments about this advisory:

* Open an issue in [the Argo CD issue tracker](https://github.com/argoproj/argo-cd/issues) or [discussions](https://github.com/argoproj/argo-cd/discussions)
* Join us on [Slack](https://argoproj.github.io/community/join-slack) in channel `#argo-cd`

### Credits

The issue was found during static code scanning with CodeQL and fixed by the Argo CD team. While we believed that a rogue SSO provider would pose a severe threat by itself, we fixed it like a regular bug.

The Argo CD team would like to thank Adam Gold of Snyk (https://snyk.io) for stepping up and classifying this correctly as a  security issue and for his help in determining its severity. Also, Snyk kindly reserved and provided the CVE for this vulnerability.

We fully agree with Adam that this bug is a security vulnerability and justifies publishing a SA, to provide our community and users full transparancy.
Apache Livy Cross-site scripting (XSS) in session names
Published: 2021-05-13T22:30:41Z
Aliases: CVE-2021-26544

Livy server version 0.7.0-incubating (only) is vulnerable to a cross site scripting issue in the session name. A malicious user could use this flaw to access logs and results of other users' sessions and run jobs with their privileges. This issue is fixed in Livy 0.7.1-incubating.
Division by zero in TFLite's implementation of `OneHot`
Published: 2021-05-21T14:28:04Z
Aliases: CVE-2021-29600

### Impact
The implementation of the `OneHot` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72):

```cc
int prefix_dim_size = 1;
for (int i = 0; i < op_context.axis; ++i) {
  prefix_dim_size *= op_context.indices->dims->data[i];
}
const int suffix_dim_size = NumElements(op_context.indices) / prefix_dim_size;
```

An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 0.

### Patches
We have patched the issue in GitHub commit [3ebedd7e345453d68e279cfc3e4072648e5e12e5](https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Autobinding vulnerability in MITREid Connect
Published: 2021-05-13T22:31:22Z
Aliases: CVE-2021-27582

org/mitre/oauth2/web/OAuthConfirmationController.java in the OpenID Connect server implementation for MITREid Connect through 1.3.3 contains a Mass Assignment (aka Autobinding) vulnerability. This arises due to unsafe usage of the @ModelAttribute annotation during the OAuth authorization flow, in which HTTP request parameters affect an authorizationRequest.
Command injection in Apache Flink
Published: 2021-05-21T19:20:35Z
Aliases: CVE-2020-1960

A vulnerability in Apache Flink where, when running a process with an enabled JMXReporter, with a port configured via metrics.reporter.reporter_name>.port, an attacker with local access to the machine and JMX port can execute a man-in-the-middle attack using a specially crafted request to rebind the JMXRMI registry to one under the attacker's control. This compromises any connection established to the process via JMX, allowing extraction of credentials and any other transferred data.
Heap buffer overflow in `Conv2DBackpropFilter`
Published: 2021-05-21T14:23:09Z
Aliases: CVE-2021-29540

### Impact
An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`:

```python
import tensorflow as tf

input_tensor = tf.constant([386.078431372549, 386.07843139643234],
                           shape=[1, 1, 1, 2], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1],
                           dtype=tf.float32)
  
tf.raw_ops.Conv2DBackpropFilter(
  input=input_tensor,
  filter_sizes=filter_sizes,
  out_backprop=out_backprop,
  strides=[1, 66, 49, 1],
  use_cudnn_on_gpu=True,
  padding='VALID',
  explicit_paddings=[],
  data_format='NHWC',
  dilations=[1, 1, 1, 1]
)
```

Alternatively, passing empty tensors also results in similar behavior: 

```python
import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32)
filter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter(
  input=input_tensor,
  filter_sizes=filter_sizes, 
  out_backprop=out_backprop,
  strides=[1, 66, 49, 1], 
  use_cudnn_on_gpu=True,
  padding='VALID',
  explicit_paddings=[],
  data_format='NHWC',
  dilations=[1, 1, 1, 1]
)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor.

### Patches 
We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Improper Authentication in Apache Shiro
Published: 2021-05-07T15:53:18Z
Aliases: CVE-2020-1957

Apache Shiro before 1.5.2, when using Apache Shiro with Spring dynamic controllers, a specially crafted request may cause an authentication bypass.
XWiki users registered with email verification can self re-activate their disabled accounts
Published: 2021-05-18T18:36:21Z
Aliases: CVE-2021-32620

### Impact
A user disabled on a wiki using email verification for registration can re-activate himself by using the activation link provided for his registration. 

### Patches
The problem has been patched in the following versions of XWiki: 11.10.13,  12.6.7, 12.10.2, 13.0.

### Workarounds
It's possible to workaround the issue by resetting the `validkey` property of the disabled XWiki users. This can be done by editing the user profile with object editor.

### References
https://jira.xwiki.org/browse/XWIKI-17942

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Jira](http://jira.xwiki.org)
* Email us at [Security mailing-list](mailto:security@xwiki.org)
Heap buffer overflow and undefined behavior in `FusedBatchNorm`
Published: 2021-05-21T14:26:35Z
Aliases: CVE-2021-29583

### Impact
The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow:
      
```python
import tensorflow as tf

x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
    
tf.raw_ops.FusedBatchNorm(
  x=x, scale=scale, offset=offset, mean=mean, variance=variance,
  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
  data_format=data_format, is_training=is_training)
```
  
If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:

```python 
import tensorflow as tf
import numpy as np

x = tf.zeros([10, 10, 10, 1], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False

tf.raw_ops.FusedBatchNorm(
  x=x, scale=scale, offset=offset, mean=mean, variance=variance, 
  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
  data_format=data_format, is_training=is_training)
``` 

The  [implementation](https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.

If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.

### Patches
We have patched the issue in GitHub commit [6972f9dfe325636b3db4e0bc517ee22a159365c0](https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Insecure temporary directory usage in frontend build functionality of Vaadin 14 and 15-19
Published: 2021-05-06T15:27:12Z
Aliases: CVE-2021-31411

Insecure temporary directory usage in frontend build functionality of `com.vaadin:flow-server` versions 2.0.9 through 2.5.2 (Vaadin 14.0.3 through Vaadin 14.5.2), 3.0 prior to 6.0 (Vaadin 15 prior to 19), and 6.0.0 through 6.0.5 (Vaadin 19.0.0 through 19.0.4) allows local users to inject malicious code into frontend resources during application rebuilds.

- https://vaadin.com/security/cve-2021-31411
github.com/unknwon/cae Path Traversal vulnerability
Published: 2021-05-18T20:31:18Z
Aliases: CVE-2020-7668

The ExtractTo function doesn't securely escape file paths in zip archives which include leading or non-leading "..". This allows an attacker to add or replace files system-wide.
OS Command Injection in gulp-tape
Published: 2021-05-07T16:14:56Z
Aliases: CVE-2020-7605

gulp-tape through 1.0.0 allows execution of arbitrary commands. It is possible to inject arbitrary commands as part of `gulp-tape` options.
SQL Injection in odata4j
Published: 2021-05-07T15:52:56Z
Aliases: CVE-2016-11023

odata4j 0.7.0 allows ExecuteCountQueryCommand.java SQL injection. NOTE, this product is apparently discontinued.
Null pointer dereference in `StringNGrams`
Published: 2021-05-21T14:23:12Z
Aliases: CVE-2021-29541

### Impact
An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`:

```python
import tensorflow as tf

data=tf.constant([''] * 11, shape=[11], dtype=tf.string)

splits = [0]*115
splits.append(3)
data_splits=tf.constant(splits, shape=[116], dtype=tf.int64)

tf.raw_ops.StringNGrams(data=data, data_splits=data_splits, separator=b'Ss',
                        ngram_widths=[7,6,11],
                        left_pad='ABCDE', right_pad=b'ZYXWVU',
                        pad_width=50, preserve_short_sequences=True)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in [`ngrams_data`](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size.

Later writes to the output tensor would then cause a null pointer dereference.

### Patches
We have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b).
    
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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
  
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Rancher Vulnerable to Cross-site Request Forgery (CSRF)
Published: 2021-05-18T15:42:40Z
Aliases: CVE-2019-13209

Rancher 2 through 2.2.4 is vulnerable to a Cross-Site Websocket Hijacking attack that allows an exploiter to gain access to clusters managed by Rancher. The attack requires a victim to be logged into a Rancher server, and then to access a third-party site hosted by the exploiter. Once that is accomplished, the exploiter is able to execute commands against the cluster's Kubernetes API with the permissions and identity of the victim.
Division by zero in TFLite's convolution code
Published: 2021-05-21T14:27:45Z
Aliases: CVE-2021-29594

### Impact
TFLite's [convolution code](https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc) has multiple division where the divisor is controlled by the user and not checked to be non-zero. For example:

```cc 
const int input_size = NumElements(input) / SizeOfDimension(input, 0);
```

### Patches
We have patched the issue in GitHub commit [ff489d95a9006be080ad14feb378f2b4dac35552](https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
OS Command injection in Bolt
Published: 2021-05-06T18:53:29Z
Aliases: CVE-2020-28925

Bolt before 3.7.2 does not restrict filter options in a Request in the Twig context, and is therefore inconsistent with the "How to Harden Your PHP for Better Security" guidance.
Division by 0 in `Conv3DBackprop*`
Published: 2021-05-21T14:21:39Z
Aliases: CVE-2021-29522

### Impact
The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0:

```python
import tensorflow as tf

input_sizes = tf.constant([0, 0, 0, 0, 0], shape=[5], dtype=tf.int32)
filter_tensor = tf.constant([], shape=[0, 0, 0, 1, 0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
                            
tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```
```python
import tensorflow as tf

input_sizes = tf.constant([1], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
filter_tensor = tf.constant([0, 0, 0, 1, 0], shape=[5], dtype=tf.int32)
out_backprop = tf.constant([], shape=[1, 1, 1, 1, 0], dtype=tf.float32)

tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero:

```cc
  const int64 size_A = output_image_size * dims.out_depth;
  const int64 size_B = filter_total_size * dims.out_depth;
  const int64 size_C = output_image_size * filter_total_size;
  const int64 work_unit_size = size_A + size_B + size_C;
  ...
  const size_t shard_size =
      use_parallel_contraction
        ? 1
        : (target_working_set_size + work_unit_size - 1) / work_unit_size;
```

Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error.

### Patches
We have patched the issue in GitHub commit [311403edbc9816df80274bd1ea8b3c0c0f22c3fa](https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Division by zero in TFLite's implementation of `SpaceToDepth`
Published: 2021-05-21T14:26:45Z
Aliases: CVE-2021-29587

### Impact
The `Prepare` step of the `SpaceToDepth` TFLite operator [does not check for 0 before division](https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67).

```cc 
const int block_size = params->block_size;
const int input_height = input->dims->data[1];
const int input_width = input->dims->data[2];
int output_height = input_height / block_size;
int output_width = input_width / block_size;
```   

An attacker can craft a model such that `params->block_size` would be zero.

### Patches
We have patched the issue in GitHub commit [0d45ea1ca641b21b73bcf9c00e0179cda284e7e7](https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7).

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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.