aswin1906/github-advisory-2022 huggingface dataset:
TensorFlow vulnerable to `CHECK` fail in `DrawBoundingBoxes`
Advisory: GHSA-jqm7-m5q7-3hm5
Aliases: CVE-2022-36001
Published: 2022-09-16T22:08:58Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
When `DrawBoundingBoxes` receives an input `boxes` that is not of dtype `float`, it gives a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
import numpy as np
arg_0=tf.constant(value=np.random.random(size=(1, 3, 2, 3)), shape=(1, 3, 2, 3), dtype=tf.half)
arg_1=tf.constant(value=np.random.random(size=(1, 2, 4)), shape=(1, 2, 4), dtype=tf.float32)
arg_2=''
tf.raw_ops.DrawBoundingBoxes(images=arg_0, boxes=arg_1, name=arg_2)
```

### Patches
We have patched the issue in GitHub commit [da0d65cdc1270038e72157ba35bf74b85d9bda11](https://github.com/tensorflow/tensorflow/commit/da0d65cdc1270038e72157ba35bf74b85d9bda11).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.
TensorFlow vulnerable to `CHECK` fail in `AvgPoolGrad`
Advisory: GHSA-2475-53vw-vp25
Aliases: CVE-2022-35968
Published: 2022-09-16T22:16:52Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### 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, shape=[4], dtype=tf.int32)
grad = tf.constant(.0890338004362538, shape=[1,5,7,1], dtype=tf.float64)
tf.raw_ops.AvgPoolGrad(orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding, data_format=data_format)
```

### Patches
We have patched the issue in GitHub commit [3a6ac52664c6c095aa2b114e742b0aa17fdce78f](https://github.com/tensorflow/tensorflow/commit/3a6ac52664c6c095aa2b114e742b0aa17fdce78f).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.
Caddy-SSH vulnerable to Authorization Bypass due to incorrect usage of PAM library
Advisory: GHSA-gmhj-xjfh-cf6m
Published: 2022-09-23T21:37:21Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:H/UI:N/S:C/C:H/I:H/A:N

Not invoking a call to `pam_acct_mgmt` after a call to `pam_authenticate` to check the validity of a login can lead to an authorization bypass.

### Impact

#### Exploitability

The attack can be carried over the network. A complex non-standard configuration or a specialized condition is required for the attack to be successfully conducted. The attacker also requires access to a users credentials, be it expired, for an attack to be successful. There is no user interaction required for successful execution. The attack can affect components outside the scope of the target module.

#### Impact

Using this attack vector, an attacker may access otherwise restricted parts of the system. The attack can be used to gain access to confidential files like passwords, login credentials and other secrets. Hence, it has a high impact on confidentiality. It may also be directly used to affect a change on a system resource. Hence has a medium to high impact on integrity. This attack may not be used to affect the availability of the system. Taking this account an appropriate CVSS v3.1 vector would be
[AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:L/A:N](https://nvd.nist.gov/vuln-metrics/cvss/v3-calculator?vector=AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:L&version=3.1)

### Root Cause Analysis

In this case, in the following PAM transaction, only a call to `pam.Authenticate` is used to login a user.

https://github.com/mohammed90/caddy-ssh/blob/1d980ceea6e67765daf19b5e644c7a0773fdaa13/internal/authentication/os/pam.go#L60

This implies that a user with expired credentials can still login.

The bug can be verified easily by creating a new user account, expiring it with `chage -E0 <username>` and then trying to log in with the expired credentials.

### Patches
This can be fixed by invoking a call to `pam.AcctMgmt` after a successful call to `pam.Authenticate`

### References
* [Man Page for pam_acct_mgmt](https://man7.org/linux/man-pages/man3/pam_acct_mgmt.3.html)
* [CWE-863](http://cwe.mitre.org/data/definitions/863.html)
* [CWE-285](http://cwe.mitre.org/data/definitions/285.html)
Twig may load a template outside a configured directory when using the filesystem loader
Advisory: GHSA-52m2-vc4m-jj33
Aliases: CVE-2022-39261
Published: 2022-09-30T05:29:36Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

# Description

When using the filesystem loader to load templates for which the name is a user input, it is possible to use the `source` or `include` statement to read arbitrary files from outside the templates directory when using a namespace like `@somewhere/../some.file` (in such a case, validation is bypassed).

# Resolution

We fixed validation for such template names.

Even if the 1.x branch is not maintained anymore, a new version has been released.

# Credits

We would like to thank Dariusz Tytko for reporting the issue and Fabien Potencier for fixing the issue.
YetiForce CRM vulnerable to stored Cross-site Scripting via SlaPolicy module
Advisory: GHSA-vx3x-hwph-grvw
Aliases: CVE-2022-3005
Published: 2022-09-21T00:00:52Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N

YetiForce CRM versions 6.4.0 and prior are vulnerable to cross-site scripting via the `SlaPolicy` module. A patch is available at commit e55886781509fe39951fc7528347696474a17884.
Apache Kafka vulnerability can lead to brokers hitting OutOfMemoryException, causing Denial of Service
Advisory: GHSA-c9h3-c6qj-hh7q
Aliases: CVE-2022-34917
Published: 2022-09-21T00:00:52Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

A security vulnerability has been identified in Apache Kafka. It affects all releases since 2.8.0. The vulnerability allows malicious unauthenticated clients to allocate large amounts of memory on brokers. This can lead to brokers hitting OutOfMemoryException and causing denial of service. Example scenarios: - Kafka cluster without authentication: Any clients able to establish a network connection to a broker can trigger the issue. - Kafka cluster with SASL authentication: Any clients able to establish a network connection to a broker, without the need for valid SASL credentials, can trigger the issue. - Kafka cluster with TLS authentication: Only clients able to successfully authenticate via TLS can trigger the issue. We advise the users to upgrade the Kafka installations to one of the 3.2.3, 3.1.2, 3.0.2, 2.8.2 versions.
rdiffweb's unlimited length Fullname field can lead to DoS
Advisory: GHSA-fqfg-c577-2vc3
Aliases: CVE-2022-3364
Published: 2022-09-30T00:00:20Z
Severity: type: CVSS_V3
score: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L

rdiffweb prior to 2.5.0a3 does not validate email length, allowing users to insert an email longer than 255 characters. If a user signs up with an email with a length of 1 million or more characters and logs in, withdraws, or changes their email, the server may cause denial of service due to overload.
Harbor fails to validate the user permissions when updating tag retention policies
Advisory: GHSA-3637-v6vq-xqqw
Aliases: CVE-2022-31670
Published: 2022-09-16T19:29:31Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:N/I:H/A:N

### Impact
 Harbor fails to validate the user permissions when updating tag retention policies. API call:

  PUT /retentions/{id}

By sending a request to update a tag retention policy with an id that belongs to a project
that the currently authenticated user doesn’t have access to, the attacker could modify
tag retention policies configured in other projects.

### Patches
This and similar issues are fixed in Harbor v2.5.2 and later. Please upgrade as soon as possible.

### Workarounds
There are no workarounds available.

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the Harbor GitHub repository](https://github.com/goharbor/harbor)

### Credits
Thanks to [Gal Goldstein](https://www.linkedin.com/in/gal-goldshtein/) and [Daniel Abeles](https://www.linkedin.com/in/daniel-abeles/) from [Oxeye Security](https://www.oxeye.io/) for reporting this issue.
Apache Pulsar Brokers and Proxies vulnerable to Improper Certificate Validation
Advisory: GHSA-j3qw-g67q-7m64
Aliases: CVE-2022-33683
Published: 2022-09-25T00:00:27Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:N

Apache Pulsar Brokers and Proxies create an internal Pulsar Admin Client that does not verify peer TLS certificates, even when tlsAllowInsecureConnection is disabled via configuration. The Pulsar Admin Client's intra-cluster and geo-replication HTTPS connections are vulnerable to man in the middle attacks, which could leak authentication data, configuration data, and any other data sent by these clients. An attacker can only take advantage of this vulnerability by taking control of a machine 'between' the client and the server. The attacker must then actively manipulate traffic to perform the attack. This issue affects Apache Pulsar Broker and Proxy versions 2.7.0 to 2.7.4; 2.8.0 to 2.8.3; 2.9.0 to 2.9.2; 2.10.0; 2.6.4 and earlier.
Unbounded resource exhaustion in cmark-gfm autolink extension may lead to denial of service
Advisory: GHSA-4qw4-jpp4-8gvp
Published: 2022-09-21T18:18:05Z

### Impact

CommonMarker uses `cmark-gfm` for rendering [Github Flavored Markdown](https://github.github.com/gfm/). A polynomial time complexity issue in cmark-gfm's autolink extension may lead to unbounded resource exhaustion and subsequent denial of service.

### Patches

This vulnerability has been patched in the following CommonMarker release:

- v0.23.6

### Workarounds

Disable use of the autolink extension.

### References

https://github.com/gjtorikian/commonmarker/pull/190
https://github.com/github/cmark-gfm/security/advisories/GHSA-cgh3-p57x-9q7q
https://en.wikipedia.org/wiki/Time_complexity

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [github/cmark-gfm](https://github.com/github/cmark-gfm)

### Acknowledgements

We would like to thank [Legit Security](https://www.legitsecurity.com) for reporting this vulnerability.
Harbor fails to validate the user permissions when reading job execution logs through the P2P preheat execution logs
Advisory: GHSA-q76q-q8hw-hmpw
Aliases: CVE-2022-31671
Published: 2022-09-09T19:47:03Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:L/I:N/A:N

### Impact
Harbor fails to validate the user permissions when reading job execution logs through the P2P preheat execution logs - API call

  GET /projects/{project_name}/preheat/policies/{preheat_policy_name}/executions/{execution_id}/tasks/{task_id}/logs

By sending a request that attempts to read P2P preheat execution logs and specifying different job ids, malicious authenticatedusers could read all the job logs stored in the Harbor database.

### Patches
This and similar issues are fixed in Harbor v2.5.2 and later. Please upgrade as soon as possible.

### Workarounds
There are no workarounds available.

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the Harbor GitHub repository](https://github.com/goharbor/harbor)

### Credits
Thanks to [Gal Goldstein](https://www.linkedin.com/in/gal-goldshtein/) and [Daniel Abeles](https://www.linkedin.com/in/daniel-abeles/) from [Oxeye Security](https://www.oxeye.io/) for reporting this issue.
elrond-go MultiESDTNFTTransfer call on a SC address with missing function name
Advisory: GHSA-qf7j-25g9-r63f
Aliases: CVE-2022-36058
Published: 2022-09-01T22:24:26Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N

### Impact
Anyone who uses elrond-go to process blocks (historical or actual) that contains a transaction like this: `MultiESDTNFTTransfer@01@54444558544b4b5955532d323631626138@00@0793afc18c8da2ca@` (mind the missing function name after the last `@`)
Basic functionality like p2p messaging, storage, API requests and such are unaffected.

### Patches
Patch v1.3.34 or higher

### Workarounds
No workarounds

### References
For future reference, one can observe the following integration test:
[[provide the link to the integration test]](https://github.com/ElrondNetwork/elrond-go/blob/8e402fa6d7e91e779980122d3798b2bf50892945/integrationTests/vm/txsFee/asyncESDT_test.go#L402)

### For more information
If you have any questions or comments about this advisory:
* Open an issue in elrond-go (http://github.com/ElrondNetwork/elrond-go/issues)
TensorFlow vulnerable to null-dereference in `mlir::tfg::TFOp::nameAttr`
Advisory: GHSA-7j3m-8g3c-9qqq
Aliases: CVE-2022-36014
Published: 2022-09-16T21:20:58Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
When [`mlir::tfg::TFOp::nameAttr`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ir/importexport/graphdef_import.cc) receives null type list attributes, it crashes.
```cpp

StatusOr<unsigned> GraphDefImporter::ArgNumType(const NamedAttrList &attrs,
                                                const OpDef::ArgDef &arg_def,
                                                SmallVectorImpl<Type> &types) {
  // Check whether a type list attribute is specified.
  if (!arg_def.type_list_attr().empty()) {
    if (auto v = attrs.get(arg_def.type_list_attr()).dyn_cast<ArrayAttr>()) {
      for (Attribute attr : v) {
        if (auto dtype = attr.dyn_cast<TypeAttr>()) {
          types.push_back(UnrankedTensorType::get(dtype.getValue()));
        } else {
          return InvalidArgument("Expected '", arg_def.type_list_attr(),
                                 "' to be a list of types");
        }
      }
      return v.size();
    }
    return NotFound("Type attr not found: ", arg_def.type_list_attr());
  }

  unsigned num = 1;
  // Check whether a number attribute is specified.
  if (!arg_def.number_attr().empty()) {
    if (auto v = attrs.get(arg_def.number_attr()).dyn_cast<IntegerAttr>()) {
      num = v.getValue().getZExtValue();
    } else {
      return NotFound("Type attr not found: ", arg_def.number_attr());
    }
  }

  // Check for a type or type attribute.
  Type dtype;
  if (arg_def.type() != DataType::DT_INVALID) {
    TF_RETURN_IF_ERROR(ConvertDataType(arg_def.type(), b_, &dtype));
  } else if (arg_def.type_attr().empty()) {
    return InvalidArgument("Arg '", arg_def.name(),
                           "' has invalid type and no type attribute");
  } else {
    if (auto v = attrs.get(arg_def.type_attr()).dyn_cast<TypeAttr>()) {
      dtype = v.getValue();
    } else {
      return NotFound("Type attr not found: ", arg_def.type_attr());
    }
  }
  types.append(num, UnrankedTensorType::get(dtype));
  return num;
}
```


### Patches
We have patched the issue in GitHub commits [3a754740d5414e362512ee981eefba41561a63a6](https://github.com/tensorflow/tensorflow/commit/3a754740d5414e362512ee981eefba41561a63a6) and [a0f0b9a21c9270930457095092f558fbad4c03e5](https://github.com/tensorflow/tensorflow/commit/a0f0b9a21c9270930457095092f558fbad4c03e5).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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.
VNCAuthProxy authentication bypass vulnerability
Advisory: GHSA-237r-mx84-7x8c
Aliases: CVE-2022-36436
Published: 2022-09-16T17:11:31Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

OSU Open Source Lab VNCAuthProxy through 1.1.1 is affected by an vncap/vnc/protocol.py VNCServerAuthenticator authentication-bypass vulnerability that could allow a malicious actor to gain unauthorized access to a VNC session or to disconnect a legitimate user from a VNC session. A remote attacker with network access to the proxy server could leverage this vulnerability to connect to VNC servers protected by the proxy server without providing any authentication credentials. Exploitation of this issue requires that the proxy server is currently accepting connections for the target VNC server.
cruddl vulnerable to ArangoDB Query Language (AQL) injection through flexSearch
Advisory: GHSA-qm4w-4995-vg7f
Aliases: CVE-2022-36084
Published: 2022-09-16T17:43:10Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H

### Impact

If a vunerable version of cruddl is used to generate a schema that uses `@flexSearchFulltext`, users of that schema may be able to inject arbitrary AQL queries that will be forwarded to and executed by ArangoDB.

Schemas that do not use `@flexSearchFulltext` are not affected.

The attacker needs to have `READ` permission to at least one root entity type that has `@flexSearchFulltext` enabled.

### Patches

The issue has been fixed in version 3.0.2 and in version 2.7.0 of cruddl.

### Workarounds

Users can temporarily remove `@flexSearchFulltext` from their schemas before they can update cruddl.

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [cruddl](https://github.com/AEB-labs/cruddl)
* Email us at [security@aeb.com](mailto:security@aeb.com)
TensorFlow vulnerable to `CHECK` fail in `DenseBincount`
Advisory: GHSA-w62h-8xjm-fv49
Aliases: CVE-2022-35987
Published: 2022-09-16T21:19:15Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
`DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
binary_output = True
input = tf.random.uniform(shape=[0, 0], minval=-10000, maxval=10000, dtype=tf.int32, seed=-2460)
size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)
weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000)
tf.raw_ops.DenseBincount(input=input, size=size, weights=weights, binary_output=binary_output)
```

### Patches
We have patched the issue in GitHub commit [bf4c14353c2328636a18bfad1e151052c81d5f43](https://github.com/tensorflow/tensorflow/commit/bf4c14353c2328636a18bfad1e151052c81d5f43).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Di Jin, Secure Systems Labs, Brown University
LibreNMS stored Cross-site Scripting via Schedule Maintenance `Title` parameter
Advisory: GHSA-3jh2-wmv7-m932
Aliases: CVE-2022-3231
Published: 2022-09-18T00:00:30Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N

LibreNMS versions 22.8.0 and prior allow attackers to execute arbitrary JavaScript code via the Schedule Maintenance `Title` parameter. A patch is available and anticipated to be part of version 22.9.0.
Go-CVSS has Out-of-bounds Read vulnerability in ParseVector function
Advisory: GHSA-xhmf-mmv2-4hhx
Aliases: CVE-2022-39213
Published: 2022-09-16T20:59:43Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
When a full CVSS v2.0 vector string is parsed using `ParseVector`, an Out-of-Bounds Read is possible due to a lack of tests. The Go module will then panic.

### Patches
The problem is patched in tag `v0.4.0`, by the commit `d9d478ff0c13b8b09ace030db9262f3c2fe031f4`.

### Workarounds
The only way to avoid it is by parsing CVSS v2.0 vector strings that does not have all attributes defined (e.g. `AV:N/AC:L/Au:N/C:P/I:P/A:C/E:U/RL:OF/RC:C/CDP:MH/TD:H/CR:M/IR:M/AR:M`).

### References
N/A

### CPE v2.3
As stated in [SECURITY.md](https://github.com/pandatix/go-cvss/blob/master/SECURITY.md), the CPE v2.3 to refer to this Go module is `cpe:2.3:a:pandatix:go_cvss:*:*:*:*:*:*:*:*`.
The entry has already been requested to the NVD CPE dictionnary.

### Exploit example
```go
package main

import (
	"log"

	gocvss20 "github.com/pandatix/go-cvss/20"
)

func main() {
	_, err := gocvss20.ParseVector("AV:N/AC:L/Au:N/C:P/I:P/A:C/E:U/RL:OF/RC:C/CDP:MH/TD:H/CR:M/IR:M/AR:M")
	if err != nil {
		log.Fatal(err)
	}
}
```

When ran, the following trace is returned.
```
panic: runtime error: index out of range [3] with length 3

goroutine 1 [running]:
github.com/pandatix/go-cvss/20.ParseVector({0x4aed6c?, 0x0?})
        /home/lucas/go/pkg/mod/github.com/pandatix/go-cvss@v0.2.0/20/cvss20.go:54 +0x578
main.main()
        /media/lucas/HDD-K/Documents/cve-2022-xxxxx/main.go:10 +0x25
exit status 2
```

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [pandatix/go-cvss](https://github.com/pandatix/go-cvss/issues)
* Email me at [lucastesson@protonmail.com](mailto:lucastesson@protonmail.com)
Lithium vulnerable to Cross Site Scripting in provided Swagger-UI
Advisory: GHSA-f36p-42jv-8rh2
Published: 2022-09-30T04:53:37Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N

### Impact
A  XSS vulnerability in the provided (outdated) Swagger-UI is exploitable in applications using lithium with Swagger-UI enabled.
This allows an attacker gain Remote Code Execution (RCE) and potentially exfiltrate secrets in the context of this swagger session.


### Patches
The used swagger-ui was updated by switching to the latest version of dropwizard-swagger in 8b9b406d608fe482ec0e7adf8705834bca92d7df


### Workarounds
The risk of injected external content can be reduced by setting up a [Content-Security-Policy](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy).


### References
* https://www.vidocsecurity.com/blog/hacking-swagger-ui-from-xss-to-account-takeovers/


### Credits
We thank [Mohit Kumar](https://www.linkedin.com/in/mohit-kumar-4ab6b3bb) for reporting this vulnerability!
Tauri's readDir Endpoint Scope can be Bypassed With Symbolic Links
Advisory: GHSA-28m8-9j7v-x499
Aliases: CVE-2022-39215
Published: 2022-09-16T19:28:49Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:N/A:N

### Impact
Due to missing canonicalization when `readDir` is called recursively, it was possible to display directory listings outside of the defined `fs` scope. This required a crafted symbolic link or junction folder inside an allowed path of the `fs` scope. No arbitrary file content could be leaked.


### Patches
The issue has been resolved in https://github.com/tauri-apps/tauri/pull/5123 and the implementation now properly checks if the
requested (sub) directory is a symbolic link outside of the defined `scope`.

### Workarounds
Disable the `readDir` endpoint in the `allowlist` inside the `tauri.conf.json`.

### For more information

This issue was initially reported by [martin-ocasek]( https://github.com/martin-ocasek) in [#4882](https://github.com/tauri-apps/tauri/issues/4882).

If you have any questions or comments about this advisory:
* Open an issue in [tauri](https://github.com/tauri-apps/tauri)
* Email us at [security@tauri.app](mailto:security@tauri.app)
Buildah's incorrect handling of the supplementary groups may lead to data disclosure, modification
Advisory: GHSA-fjm8-m7m6-2fjp
Aliases: CVE-2022-2990
Published: 2022-09-14T00:00:48Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N

An incorrect handling of the supplementary groups in the Buildah container engine might lead to the sensitive information disclosure or possible data modification if an attacker has direct access to the affected container where supplementary groups are used to set access permissions and is able to execute a binary code in that container.
Jenkins Compuware Common Configuration Plugin vulnerable to Improper Restriction of XML External Entity Reference
Advisory: GHSA-g43x-pcc9-f472
Aliases: CVE-2022-41226
Published: 2022-09-22T00:00:28Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N

Jenkins Compuware Common Configuration Plugin 1.0.14 and earlier does not configure its XML parser to prevent XML external entity (XXE) attacks.

This allows attackers able to change the contents of the Topaz Workbench CLI home directory on agents to have Jenkins parse a crafted file that uses external entities for extraction of secrets from the Jenkins controller or server-side request forgery.
TensorFlow vulnerable to segfault in `QuantizedInstanceNorm`
Advisory: GHSA-g35r-369w-3fqp
Aliases: CVE-2022-35970
Published: 2022-09-16T22:17:57Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### 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=[1,4,4,32], dtype=tf.quint8)
x_min = tf.constant([], shape=[0], dtype=tf.float32)
x_max = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedInstanceNorm(x=x, x_min=x_min, x_max=x_max, output_range_given=output_range_given, given_y_min=given_y_min, given_y_max=given_y_max, variance_epsilon=variance_epsilon, min_separation=min_separation)
```

### Patches
We have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.
TensorFlow vulnerable to `CHECK` fail in `RandomPoissonV2`
Advisory: GHSA-cv2p-32v3-vhwq
Aliases: CVE-2022-36003
Published: 2022-09-16T21:39:10Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
When `RandomPoissonV2` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=tf.random.uniform(shape=(4,), dtype=tf.int32, maxval=65536)
arg_1=tf.random.uniform(shape=(4, 4, 4, 4, 4), dtype=tf.float32, maxval=None)
arg_2=0
arg_3=0
arg_4=tf.int32
arg_5=None
tf.raw_ops.RandomPoissonV2(shape=arg_0, rate=arg_1, seed=arg_2,
                           seed2=arg_3, dtype=arg_4, name=arg_5)
```

### Patches
We have patched the issue in GitHub commit [552bfced6ce4809db5f3ca305f60ff80dd40c5a3](https://github.com/tensorflow/tensorflow/commit/552bfced6ce4809db5f3ca305f60ff80dd40c5a3).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology.
Harbor fails to validate the user permissions when viewing Webhook policies
Advisory: GHSA-jf8p-3vjh-pq94
Aliases: CVE-2022-31666
Published: 2022-09-16T19:35:47Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N

### Impact
Harbor fails to validate the user permissions to view Webhook policies including relevant credentials configured in different projects the user doesn’t have access to, resulting in malicious users being able to read Webhook policies of other users/projects. API call is

  GET /projects/{project_name_or_id}/webhook/policies/{webhook_policy_id}

By sending the below request and specifying different Webhook policy ids in the last part of the URL, the malicious user may disclose Webhook policies related to other repositories/projects.: none;">

### Patches
This and similar issues are fixed in Harbor v2.5.2 and later. Please upgrade as soon as possible.

### Workarounds
There are no workarounds available.

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the Harbor GitHub repository](https://github.com/goharbor/harbor)

### Credits
Thanks to [Gal Goldstein](https://www.linkedin.com/in/gal-goldshtein/) and [Daniel Abeles](https://www.linkedin.com/in/daniel-abeles/) from [Oxeye Security](https://www.oxeye.io/) for reporting this issue.
Rank Math SEO plugin vulnerable to Server-Side Request Forgery
Advisory: GHSA-j95r-86hx-xwxg
Aliases: CVE-2022-36376
Published: 2022-09-10T00:00:27Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Server-Side Request Forgery (SSRF) vulnerability in Rank Math SEO plugin <= 1.0.95 at WordPress.
snakeYAML before 1.32 vulnerable to Denial of Service due to Out-of-bounds Write
Advisory: GHSA-9w3m-gqgf-c4p9
Aliases: CVE-2022-38752
Published: 2022-09-06T00:00:27Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Using snakeYAML to parse untrusted YAML files may be vulnerable to Denial of Service attacks (DoS). If the parser is running on user supplied input, an attacker may supply content that causes the parser to crash by stack-overflow.
TensorFlow vulnerable to `CHECK` failure in `AvgPoolOp`
Advisory: GHSA-mgmh-g2v6-mqw5
Aliases: CVE-2022-35941
Published: 2022-09-16T21:18:42Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### 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 numpy as np

value = np.ones([1, 1, 1, 1])
ksize = [1, 1e20, 1, 1]
strides = [1, 1, 1, 1]
padding = 'SAME'
data_format = 'NHWC'

tf.raw_ops.AvgPool(value=value, ksize=ksize, strides=strides, padding=padding, data_format=data_format)
```

### Patches
We have patched the issue in GitHub commit [3a6ac52664c6c095aa2b114e742b0aa17fdce78f](https://github.com/tensorflow/tensorflow/commit/3a6ac52664c6c095aa2b114e742b0aa17fdce78f).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Jingyi Shi.
Inventree vulnerable to Stored Cross-site Scripting
Advisory: GHSA-62g7-fpv9-v95f
Aliases: CVE-2022-3355
Published: 2022-09-30T00:00:45Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N

Inventree prior to 0.8.3 is vulnerable to stored cross-site scripting by uploading SVG files. Version 0.8.3 contains a patch for this issue.
TensorFlow vulnerable to `CHECK` fail in `ParameterizedTruncatedNormal`
Advisory: GHSA-p2xf-8hgm-hpw5
Aliases: CVE-2022-35984
Published: 2022-09-16T22:30:29Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
`ParameterizedTruncatedNormal` assumes `shape` is of type `int32`. A valid `shape` of type `int64` results in a mismatched type `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
seed = 1618
seed2 = 0
shape = tf.random.uniform(shape=[3], minval=-10000, maxval=10000, dtype=tf.int64, seed=4894)
means = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971)
stdevs = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971)
minvals = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971)
maxvals = tf.random.uniform(shape=[3, 3, 3], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2971)
tf.raw_ops.ParameterizedTruncatedNormal(shape=shape, means=means, stdevs=stdevs, minvals=minvals, maxvals=maxvals, seed=seed, seed2=seed2)
```

### Patches
We have patched the issue in GitHub commit [72180be03447a10810edca700cbc9af690dfeb51](https://github.com/tensorflow/tensorflow/commit/72180be03447a10810edca700cbc9af690dfeb51).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Di Jin, Secure Systems Labs, Brown University
TYPO3 HTML Sanitizer Bypasses Cross-Site Scripting Protection
Advisory: GHSA-gqqf-g5r7-84vf
Published: 2022-09-15T03:26:51Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N

> ### Meta
> * CVSS: `CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N/E:F/RL:O/RC:C` (5.7)

### Problem
Due to a parsing issue in upstream package [`masterminds/html5`](https://packagist.org/packages/masterminds/html5), malicious markup used in a sequence with special HTML comments cannot be filtered and sanitized. This allows to by-pass the cross-site scripting mechanism of [`typo3/html-sanitizer`](https://github.com/TYPO3/html-sanitizer).

### Solution
Update to TYPO3 version 7.6.58 ELTS, 8.7.48 ELTS, 9.5.37 ELTS, 10.4.32 or 11.5.16 that fix the problem described above.

### Credits
Thanks to David Klein who reported this issue, and to TYPO3 security team member Oliver Hader who fixed the issue.

### References
* [TYPO3-CORE-SA-2022-011](https://typo3.org/security/advisory/typo3-core-sa-2022-011)
* [GHSA-47m6-46mj-p235](https://github.com/TYPO3/html-sanitizer/security/advisories/GHSA-47m6-46mj-p235)
Hyperledger indy-node vulnerable to denial of service
Advisory: GHSA-x996-7qh9-7ff7
Aliases: CVE-2022-31006
Published: 2022-09-16T20:28:39Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact

An attacker can max out the number of client connections allowed by the ledger that was deployed using guidance provided in the indy-node repository, leaving the ledger unable to be used for its intended purpose.

The ledger content will not be impacted by the attack, and the ledger will resume servicing valid client requests after the attack.

### Mitigations

This attack exploits the trade-off between resilience and availability. Any protection against abusive client connections will also prevent the network being accessed by certain legitimate users. As a result, validator nodes must tune their firewall rules to ensure the right trade-off for their network's expected users. The guidance previously provided enabled a low-cost DDoS attack.

The [guidance to network operators for the use of firewall rules](https://github.com/hyperledger/indy-node/blob/main/docs/source/setup-iptables.md) in the deployment of Indy networks has been modified to better protect against denial of service attacks by increasing the cost and complexity in mounting such attacks.

The mitigation for this vulnerability is not in the Hyperledger Indy code per se, but rather in the individual deployments of Indy. The mitigations should be applied to all deployments of Indy, and are not related to a particular release.

### Acknowledgements

Thank you to Mirko Mollik at [TrustCerts.de](https://trustcerts.de) for finding and responsibly disclosing this issue.
Apache Batik Server-Side Request Forgery 
Advisory: GHSA-c5xv-qc8p-mh2v
Aliases: CVE-2022-38398
Published: 2022-09-23T00:00:39Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

Server-Side Request Forgery (SSRF) vulnerability in Batik of Apache XML Graphics allows an attacker to load a url thru the jar protocol. This issue affects Apache XML Graphics Batik 1.14.
axum-core has no default limit put on request bodies
Advisory: GHSA-m77f-652q-wwp4
Aliases: CVE-2022-3212
Published: 2022-09-15T03:25:15Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

`<bytes::Bytes as axum_core::extract::FromRequest>::from_request` would not, by default, set a limit for the size of the request body. That meant if a malicious peer would send a very large (or infinite) body your server might run out of memory and crash.

This also applies to these extractors which used `Bytes::from_request` internally:
- `axum::extract::Form`
- `axum::extract::Json`
- `String`

The fix is also in `axum-core` `0.3.0.rc.2` but `0.3.0.rc.1` _is_ vulnerable.

Because `axum` depends on `axum-core` it is vulnerable as well. The vulnerable versions of `axum` are `<= 0.5.15` and `0.6.0.rc.1`. `axum` `>= 0.5.16` and `>= 0.6.0.rc.2` does have the fix and are not vulnerable.

The patched versions will set a 2 MB limit by default.
TensorFlow vulnerable to null dereference on MLIR on empty function attributes
Advisory: GHSA-fv43-93gv-vm8f
Aliases: CVE-2022-36011
Published: 2022-09-16T21:59:08Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
When [`mlir::tfg::ConvertGenericFunctionToFunctionDef`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ir/importexport/functiondef_import.cc) is given empty function attributes, it gives a null dereference.
```cpp
// Import the function attributes with a `tf.` prefix to match the current
// infrastructure expectations.
for (const auto& namedAttr : func.attr()) {
  const std::string& name = "tf." + namedAttr.first;
  const AttrValue& tf_attr = namedAttr.second;
  TF_ASSIGN_OR_RETURN(Attribute attr,
                      ConvertAttributeValue(tf_attr, builder, tfgDialect));
  attrs.append(name, attr);
}
```
If `namedAttr.first` is empty, it will crash.

### Patches
We have patched the issue in GitHub commit [1cf45b831eeb0cab8655c9c7c5d06ec6f45fc41b](https://github.com/tensorflow/tensorflow/commit/1cf45b831eeb0cab8655c9c7c5d06ec6f45fc41b).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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.
rdiffweb's unlimited length email field can lead to DoS
Advisory: GHSA-qrj3-hrgj-fm7r
Aliases: CVE-2022-3272
Published: 2022-09-27T00:00:17Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

rdiffweb prior to 2.4.8 does not validate email length, allowing users to insert an email longer than 255 characters. If a user signs up with an email with a length of 1 million or more characters and logs in, withdraws, or changes their email, the server may cause denial of service due to overload. Version 2.4.8 sets length limits for username, email, and root directory.
TensorFlow vulnerable to segfault in `LowerBound` and `UpperBound`
Advisory: GHSA-qxpx-j395-pw36
Aliases: CVE-2022-35965
Published: 2022-09-16T22:14:39Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### Impact
If `LowerBound` or `UpperBound` is given an empty`sorted_inputs` input, it results in a `nullptr` dereference, leading to a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf

out_type = tf.int32
sorted_inputs = tf.constant([], shape=[10,0], dtype=tf.float32)
values = tf.constant([], shape=[10,10,0,10,0], dtype=tf.float32)
tf.raw_ops.LowerBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type)
```
```python
import tensorflow as tf

out_type = tf.int64
sorted_inputs = tf.constant([], shape=[2,2,0,0,0,0,0,2], dtype=tf.float32)
values = tf.constant(0.372660398, shape=[2,4], dtype=tf.float32)
tf.raw_ops.UpperBound(sorted_inputs=sorted_inputs, values=values, out_type=out_type)
```

### Patches
We have patched the issue in GitHub commit [bce3717eaef4f769019fd18e990464ca4a2efeea](https://github.com/tensorflow/tensorflow/commit/bce3717eaef4f769019fd18e990464ca4a2efeea).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Neophytos Christou, Secure Systems Labs, Brown University.
Cargo extracting malicious crates can fill the file system
Advisory: GHSA-2hvr-h6gw-qrxp
Aliases: CVE-2022-36114
Published: 2022-09-16T17:12:05Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:U/C:N/I:N/A:H

The Rust Security Response WG was notified that Cargo did not prevent extracting some malformed packages downloaded from alternate registries. An attacker able to upload packages to an alternate registry could fill the file system when Cargo downloaded the package.

The severity of this vulnerability is "low" for users of alternate registries. Users relying on crates.io are not affected.

Note that **by design** Cargo allows code execution at build time, due to build scripts and procedural macros. The vulnerabilities in this advisory allow performing a subset of the possible damage in a harder to track down way. Your dependencies must still be trusted if you want to be protected from attacks, as it's possible to perform the same attacks with build scripts and procedural macros.

## Disk space exaustion

It was discovered that Cargo did not limit the amount of data extracted from compressed archives. An attacker could upload to an alternate registry a specially crafted package that extracts way more data than its size (also known as a "zip bomb"), exhausting the disk space on the machine using Cargo to download the package.

## Affected versions

The vulnerability is present in all versions of Cargo. Rust 1.64, to be released on September 22nd, will include a fix for it.

Since the vulnerability is just a more limited way to accomplish what a malicious build scripts or procedural macros can do, we decided not to publish Rust point releases backporting the security fix. Patch files are available for Rust 1.63.0 are available [in the wg-security-response repository][patches] for people building their own toolchain.

## Mitigations

We recommend users of alternate registries to excercise care in which package they download, by only including trusted dependencies in their projects. Please note that even with these vulnerabilities fixed, by design Cargo allows arbitrary code execution at build time thanks to build scripts and procedural macros: a malicious dependency will be able to cause damage regardless of these vulnerabilities.

crates.io implemented server-side checks to reject these kinds of packages years ago, and there are no packages on crates.io exploiting these vulnerabilities. crates.io users still need to excercise care in choosing their dependencies though, as the same concerns about build scripts and procedural macros apply here.

## Acknowledgements

We want to thank Ori Hollander from JFrog Security Research for responsibly disclosing this to us according to the [Rust security policy][policy].

We also want to thank Josh Triplett for developing the fixes, Weihang Lo for developing the tests, and Pietro Albini for writing this advisory. The disclosure was coordinated by Pietro Albini and Josh Stone.

[policy]: https://www.rust-lang.org/policies/security
[patches]: https://github.com/rust-lang/wg-security-response/tree/master/patches
TensorFlow vulnerable to `CHECK` fail in `EmptyTensorList`
Advisory: GHSA-qhw4-wwr7-gjc5
Aliases: CVE-2022-35998
Published: 2022-09-16T22:09:57Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H

### 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)
```

### Patches
We have patched the issue in GitHub commit [c8ba76d48567aed347508e0552a257641931024d](https://github.com/tensorflow/tensorflow/commit/c8ba76d48567aed347508e0552a257641931024d).

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Kang Hong Jin.
Cross site scripting in reveal.js
Advisory: GHSA-hhqj-cfjx-vj25
Aliases: CVE-2022-0776
Published: 2022-03-02T00:00:20Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N

The onmessage event listener in /plugin/notes/speaker-view.html does not check the origin of postMessage before adding the content to the webpage. The vulnerable code allows any origin to postMessage on the browser window and feeds attacker's input to parts using which attacker can execute arbitrary javascript code on victim's browser window hosting reveal.js