aswin1906/github-advisory-2022 huggingface dataset:
Orckestra C1 CMS's deserialization of untrusted data allows for arbitrary code execution.
Advisory: GHSA-gfhp-jgp6-838j
Aliases: CVE-2022-39256
Published: 2022-09-30T04:54:06Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H

### Impact

This vulnerability allows remote attackers to execute arbitrary code on affected installations of Orckestra C1 CMS. 
Authentication is required to exploit this vulnerability.
The authenticated user may perform the actions unknowingly by visiting a specially crafted site.

### Patches
Patched in C1 CMS v6.13

### Workarounds
Upgrade to C1 CMS v6.13 or newer is required

### Credit
This issue was discovered and reported by Markus Wulftange  / [Code White GmbH](https://code-white.com/en/).
WildFly vulnerable to Insecure Default Initialization of Resource
Advisory: GHSA-fmq7-gh8v-mjvc
Aliases: CVE-2022-1278
Published: 2022-09-14T00:00:48Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

A flaw was found in WildFly, where an attacker can see deployment names, endpoints, and any other data the trace payload may contain.
TensorFlow vulnerable to `CHECK` failure in tf.reshape via overflows
Advisory: GHSA-f4w6-h4f5-wx45
Aliases: CVE-2022-35934
Published: 2022-09-16T20:56:02Z
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 tf.reshape op in TensorFlow is vulnerable to a denial of service via CHECK-failure (assertion failure) caused by overflowing the number of elements in a tensor:
```python
import tensorflow as tf

tf.reshape(tensor=[[1]],shape=tf.constant([0 for i in range(255)], dtype=tf.int64))
```
This is another instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (https://github.com/advisories/GHSA-prcg-wp5q-rv7p).

### Patches
We have patched the issue in GitHub commit [61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555](https://github.com/tensorflow/tensorflow/commit/61f0f9b94df8c0411f0ad0ecc2fec2d3f3c33555).

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 from Singapore Management University.
CrafterCMS Crafter Studio Improperly Controls Dynamically-Managed Code Resources
Advisory: GHSA-2jv3-v37p-65w3
Aliases: CVE-2022-40634
Published: 2022-09-14T00:00:45Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

Improper Control of Dynamically-Managed Code Resources vulnerability in Crafter Studio of Crafter CMS allows authenticated developers to execute OS commands via FreeMarker SSTI.
mozjpeg DecompressScanlines::read_scanlines is Unsound
Advisory: GHSA-v8gq-5grq-9728
Published: 2022-09-16T21:03:43Z

This issue and vector is similar to [RUSTSEC-2020-0029] of `rgb` crate which `mozjpeg` depends on.

Affected versions of `mozjpeg` crate allow creating instances of any type `T` from bytes,
and do not correctly constrain `T` to the types for which it is safe to do so.

Examples of safety violation possible for a type `T`:

* `T` contains a reference type, and it constructs a pointer to an invalid, arbitrary memory address.
* `T` requires a safety and/or validity invariant for its construction that may be violated.

The issue was fixed in 0.8.19 by using safer types and involving `rgb` dependency bump.

[RUSTSEC-2020-0029]: https://rustsec.org/advisories/RUSTSEC-2020-0029.html
Apache Batik vulnerable to Server-Side Request Forgery
Advisory: GHSA-h4qg-p7r2-cpg3
Aliases: CVE-2022-40146
Published: 2022-09-23T00:00:40Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Server-Side Request Forgery (SSRF) vulnerability in Batik of Apache XML Graphics allows an attacker to access files using a Jar url. This issue affects Apache XML Graphics Batik 1.14.
Nepxion Discovery vulnerable to SpEL Injection leading to Remote Code Execution
Advisory: GHSA-q979-9m39-23mq
Aliases: CVE-2022-23463
Published: 2022-09-25T00:00:15Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:L

Nepxion Discovery is a solution for Spring Cloud. Discovery is vulnerable to SpEL Injection in discovery-commons. DiscoveryExpressionResolver’s eval method is evaluating expression with a StandardEvaluationContext, allowing the expression to reach and interact with Java classes such as java.lang.Runtime, leading to Remote Code Execution. There is no patch available for this issue at time of publication. There are no known workarounds.
Apache Pulsar Broker, Proxy, and WebSocket Proxy vulnerable to Improper Certificate Validation
Advisory: GHSA-jvf3-mfxv-jcqr
Aliases: CVE-2022-33682
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

TLS hostname verification cannot be enabled in the Pulsar Broker's Java Client, the Pulsar Broker's Java Admin Client, the Pulsar WebSocket Proxy's Java Client, and the Pulsar Proxy's Admin Client leaving intra-cluster connections and geo-replication connections vulnerable to man in the middle attacks, which could leak credentials, configuration data, message data, and any other data sent by these clients. The vulnerability is for both the pulsar+ssl protocol and HTTPS. 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 by providing the client with a cryptographically valid certificate for an unrelated host. This issue affects Apache Pulsar Broker, Proxy, and WebSocket 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.
Apache Airflow Session Fixation vulnerability
Advisory: GHSA-5ff8-7639-6v6g
Aliases: CVE-2022-38054
Published: 2022-09-03T00:00:25Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

In Apache Airflow versions 2.2.4 through 2.3.3, the `database` webserver session backend was susceptible to session fixation.
Missing permission check in Jenkins SCM HttpClient Plugin allow capturing credentials
Advisory: GHSA-q9j5-2mjx-8x28
Aliases: CVE-2022-41250
Published: 2022-09-22T00:00:26Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:L/A:N

SCM HttpClient Plugin 1.5 and earlier does not perform permission check in a method implementing form validation.

This allows attackers with Overall/Read permission to connect to an attacker-specified HTTP server using attacker-specified credentials IDs obtained through another method, capturing credentials stored in Jenkins.

Additionally, this form validation method does not require POST requests, resulting in a cross-site request forgery (CSRF) vulnerability.
Stored XSS vulnerability in Jenkins DotCi Plugin
Advisory: GHSA-q9g4-9fx4-v533
Aliases: CVE-2022-41239
Published: 2022-09-22T00:00:28Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

DotCi Plugin 2.40.00 and earlier does not escape the GitHub user name parameter provided to commit notifications when displaying them in a build cause.

This results in a stored cross-site scripting (XSS) vulnerability exploitable by attackers able to submit crafted commit notifications to the `/githook/` endpoint (see also [SECURITY-2867](https://www.jenkins.io/security/advisory/2022-09-21/#SECURITY-2867)).

This vulnerability is only exploitable in Jenkins 2.314 and earlier, LTS 2.303.1 and earlier. See the [LTS upgrade guide](https://www.jenkins.io/doc/upgrade-guide/2.303/#SECURITY-2452).
rdiffweb Cross-Site Request Forgery vulnerability
Advisory: GHSA-74j6-3hh4-w3f5
Aliases: CVE-2022-3267
Published: 2022-09-23T00:00:30Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:N

rdiffweb prior to 2.4.6 is vulnerable to cross-site request forgery on the repository settings. A malicious user can change the settings of a repository by sending a URL to the victim. This issue is fixed in version 2.4.6.
autogluon.multimodal vulnerable to unsafe YAML deserialization
Advisory: GHSA-6h2x-4gjf-jc5w
Published: 2022-09-21T21:42:05Z

### Impact

A potential unsafe deserialization issue exists within the `autogluon.multimodal` module, where YAML files are loaded via `yaml.load()` instead of `yaml.safe_load()`. The deserialization of untrusted data may allow an unprivileged third party to cause remote code execution, denial of service, and impact to both confidentiality and integrity.

Impacted versions: `>=0.4.0;<0.4.3`, `>=0.5.0;<0.5.2`.

### Patches
The patches are included in `autogluon.multimodal==0.4.3`, `autogluon.multimodal==0.5.2` and Deep Learning Containers `0.4.3` and `0.5.2`.

### Workarounds
Do not load data which originated from an untrusted source, or that could have been tampered with. **Only load data you trust.**

### References
* https://cwe.mitre.org/data/definitions/502.html
* https://www.cvedetails.com/cve/CVE-2017-18342/
TensorFlow vulnerable to OOB write in `scatter_nd` in TF Lite
Advisory: GHSA-ffjm-4qwc-7cmf
Aliases: CVE-2022-35939
Published: 2022-09-16T21:04:23Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:H

### Impact
The [`ScatterNd`](https://github.com/tensorflow/tensorflow/blob/266558ac4c1f361e9a178ee9d3f0ce2e648ae499/tensorflow/lite/kernels/internal/reference/reference_ops.h#L659-L698) function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash.

### Patches
We have patched the issue in GitHub commit [b4d4b4cb019bd7240a52daa4ba61e3cc814f0384](https://github.com/tensorflow/tensorflow/commit/b4d4b4cb019bd7240a52daa4ba61e3cc814f0384).

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 Hui Peng from Baidu Security.
LIEF vulnerable to heap based buffer overflow
Advisory: GHSA-52xx-r3g2-p8jm
Aliases: CVE-2022-38306
Published: 2022-09-14T00:00:42Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H

LIEF commit 5d1d643 was discovered to contain a heap-buffer overflow in the component `/core/CorePrPsInfo.tcc`. Commit 53bf680ef494a835e2c4a5de328ca85416a03a5a contains a patch.
ghas-to-csv vulnerable to Improper Neutralization of Formula Elements in a CSV File
Advisory: GHSA-634p-93h9-92vh
Aliases: CVE-2022-39217
Published: 2022-09-16T22:06:55Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:C/C:L/I:L/A:L

### Impact

This GitHub Action creates a CSV file without sanitizing the output of the APIs.  If an alert is dismissed or any other custom field contains executable code / formulas, it might be run when an endpoint opens that CSV file in a spreadsheet program.  The data flow looks like this 👇🏻 

```mermaid
graph TD
    A(Repository) -->|developer dismissal, other data input| B(GitHub Advanced Security data)
    B -->|ghas-to-csv| C(CSV file)
    C -->|spreadsheet program| D(endpoint executes potentially malicious code)
```

### Patches

Please use version `v1` or later.  That tag moves from using `csv` to `defusedcsv` to mitigate this problem.

### Workarounds

There is no workaround.  Please upgrade to using the latest tag, `v1` (or later).

### References

* CWE-1236 information from [MITRE](https://cwe.mitre.org/data/definitions/1236.html)
* CSV injection information from [OWASP](https://owasp.org/www-community/attacks/CSV_Injection)
* CodeQL query for CWE-1236 in Python [here](https://github.com/github/codeql/tree/main/python/ql/src/experimental/Security/CWE-1236)
* PyPI site for `defusedcsv` [here](https://pypi.org/project/defusedcsv/)

### For more information

If you have any questions or comments about this advisory:

* Open an issue in this repository [here](https://github.com/some-natalie/ghas-to-csv/issues)
Harbor fails to validate the user permissions when updating tag immutability policies
Advisory: GHSA-8c6p-v837-77f6
Aliases: CVE-2022-31669
Published: 2022-09-16T19:38:12Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:N/I:L/A:L

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

  PUT /projects/{project_name_or_id}/immutabletagrules/{immutable_rule_id}

By sending a request to update a tag immutability policy with an id that belongs to a
project that the currently authenticated user doesn’t have access to, the attacker could
modify tag immutability 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 James vulnerable to buffering attack
Advisory: GHSA-w45j-f5g5-w94x
Aliases: CVE-2022-28220
Published: 2022-09-09T00:00:57Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Apache James prior to release 3.6.3 and 3.7.1 is vulnerable to a buffering attack relying on the use of the STARTTLS command. Fix of CVE-2021-38542, which solved similar problem fron Apache James 3.6.1, is subject to a parser differential and do not take into account concurrent requests.
express-xss-sanitizer vulnerable to Prototype Pollution via allowedTags attribute
Advisory: GHSA-grjp-4jmr-mjcw
Aliases: CVE-2022-21169
Published: 2022-09-27T00:00:22Z
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 package express-xss-sanitizer before 1.1.3 is vulnerable to Prototype Pollution via the `allowedTags` attribute, allowing the attacker to bypass xss sanitization.
Pimcore vulnerable to stored stored Cross-site Scripting via`properties` when creating new users
Advisory: GHSA-4849-x3jx-45qr
Aliases: CVE-2022-3211
Published: 2022-09-16T00:00:39Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N

Pimcore prior to 10.5.6 is vulnerable to stored cross-site scripting. This occurs when an attacker injects a payload when adding properties for a new user.
Dompdf allows remote file inclusion because URI validation failure does not halt font registration
Advisory: GHSA-6x28-7h8c-chx4
Aliases: CVE-2022-41343
Published: 2022-09-26T00:00:23Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

`registerFont` in `FontMetrics.php` in Dompdf before 2.0.1 allows remote file inclusion because a URI validation failure does not halt font registration, as demonstrated by a `@font-face` rule.
Jettison parser crash by stackoverflow
Advisory: GHSA-56h3-78gp-v83r
Aliases: CVE-2022-40149
Published: 2022-09-17T00:00:41Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Those using Jettison to parse untrusted XML or JSON data 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 stackoverflow. This effect may support a denial of service attack.
parse-url parses http URLs incorrectly, making it vulnerable to host name spoofing
Advisory: GHSA-pqw5-jmp5-px4v
Aliases: CVE-2022-3224
Published: 2022-09-16T00:00:39Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N

parse-url prior to 8.1.0 is vulnerable to Misinterpretation of Input. parse-url parses certain http or https URLs incorrectly, identifying the URL's protocol as ssh. It may also parse the host name incorrectly.
XWiki Platform Old Core vulnerable to Authentication Bypass Using the Login Action
Advisory: GHSA-8h89-34w2-jpfm
Aliases: CVE-2022-36092
Published: 2022-09-16T17:06:38Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

### Impact
All rights checks that would normally prevent a user from viewing a document on a wiki can be bypassed using the login action and directly specified templates. This exposes title, content and comments of any document and properties of objects (class and property name must be known, though). This is also exploitable on [private wikis](https://www.xwiki.org/xwiki/bin/view/Documentation/AdminGuide/Access%20Rights/#HPrivateWiki).

### Patches
This has been patched in versions 14.2 and 13.10.4 by properly checking view rights before loading documents and disallowing non-default templates in the login, registration and skin action.

### Workarounds
It would be possible to protect all templates individually by adding code to check access rights first, but due to the number of templates and the fact that some of them need to be used without view rights, this seems impractical.

### References
* https://jira.xwiki.org/browse/XWIKI-19549
* https://jira.xwiki.org/browse/XWIKI-18602

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Jira XWiki.org](https://jira.xwiki.org/)
* Email us at [security mailing-list](mailto:security@xwiki.com)
XWiki Platform Improper Authorization check for inactive users
Advisory: GHSA-jgc8-gvcx-9vfx
Aliases: CVE-2022-36090
Published: 2022-09-16T17:40:00Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N

### Impact

Some resources are missing a check for inactive (not yet activated or disabled) users in XWiki, including the REST service: so a disabled user can enable themselves using a REST call. On the same way some resources handler created by extensions are not protected by default: so an inactive users could perform actions for such extensions.

This issue exists since at least version 1.1 of XWiki for instance configured with the email activation required for new users. Now it's more critical for newer versions (>= 11.3RC1) since we provided the capability to disable user without deleting them, and we encouraged using that feature.

### Patches

This issue has been patched in XWiki 14.3RC1 and XWiki 13.10.5. 

### Workarounds

There is no workaround for this other than upgrading XWiki. 

### References

 * https://jira.xwiki.org/browse/XWIKI-19559

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Jira XWiki.org](https://jira.xwiki.org)
* Email us at [security mailing-list](mailto:security@xwiki.com)
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`
Advisory: GHSA-r26c-679w-mrjm
Aliases: CVE-2022-36005
Published: 2022-09-16T21:28:06Z
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 `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, 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=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)
```

### Patches
We have patched the issue in GitHub commit [f3cf67ac5705f4f04721d15e485e192bb319feed](https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed).

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
 - Neophytos Christou, Secure Systems Labs, Brown University
XXL-JOB contains a Command execution vulnerability in background tasks 
Advisory: GHSA-m54f-rp6r-rrrm
Aliases: CVE-2022-40929
Published: 2022-09-29T00:00:22Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

XXL-JOB versions 2.2.0 and prior contain a Command execution vulnerability in background tasks.
Denial of Service due to parser crash
Advisory: GHSA-fv22-xp26-mm9w
Aliases: CVE-2022-40153
Published: 2022-09-17T00:00:41Z
Withdrawn: 2022-12-06T13:40:35Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

## Withdrawn

This advisory has been withdrawn because it has been found to be a duplicate. Please see the issue [here](https://github.com/x-stream/xstream/issues/304#issuecomment-1293654236) for more information.

## Original Despcription 

Those using FasterXML/woodstox to seralize XML data 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 stackoverflow. This effect may support a denial of service attack.

This vulnerability is only relevant for users making use of the DTD parsing functionality.
Eclipse Milo vulnerable to Resource Exhaustion (Denial of Service)
Advisory: GHSA-fph9-f5r6-vhqf
Aliases: CVE-2022-25897
Published: 2022-09-15T03:35:46Z
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

Denial of Service

### Details

OPC UA specification describes a concept named _Subscriptions_. _Subscriptions_ monitor a set of _Monitored Items_ for _Notifications_ and return them to the _Client_ in response to _Publish_ requests. The server notifies the client about changes only in case the value is changed. Each monitored item is configured on a subscription, each subscription is linked to a single OPC UA session. Most OPC UA implementations set many controls and limitations for excessive memory consumption. For example:

* What is the maximum allowed number of concurrent sessions
* For each active sessions - what is the maximum allowed number of concurrent subscription per a single session
* For each active subscription - what is the maximum allowed number of concurrent monitored items per a single subscription

Clarity Research discovered a unique way to bypass those restrictions and fill up the OPC UA server process memory.

The close session request closes a connected session. A `deleteSubscription` flag is also sent in that message and determines whether the server should save the subscriptions for a future session reconnection or discard them upon session termination. If the `deleteSubscription` flag is `False` the server will store the subscriptions thus filling up the memory in an unlimited manner.

Sending multiple subscribe requests with multiple monitored items from multiple sessions will quickly fill up the process memory until the server crashes.

To trigger this bug all is needed is to create many sessions with subscriptions and monitored items without ever deleting the monitored items. Eventually these allocations will consume all the available process memory which will lead to a crash and denial of service condition.

Clarity PoC does:
```
while True:
    Open a valid OPC UA session
    Create multiple subscriptions
    Add monitored items to each subscription
    Close the session with the DeleteSubscriptions flag = False
````

### Acknowledgement

We would like to thanks Vera Mens, Uri Katz, @sharonbrizinov of Team82 ([Claroty Research](https://claroty.com/)) for this report.

### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Eclipse Milo repository](https://github.com/eclipse/milo/issues)
* Email us at [milo-dev](https://accounts.eclipse.org/mailing-list/milo-dev)
Python-TUF vulnerable to incorrect threshold signature computation for new root metadata
Advisory: GHSA-r7vq-6425-j94w
Published: 2022-09-15T03:35:12Z

### Impact

The function `_verify_root_self_signed()`, introduced in [v0.14.0](https://github.com/theupdateframework/tuf/releases/tag/v0.14.0), and which verifies self-signatures in a new root metadata file, counted multiple signatures by any new root key towards the new threshold. That is, any single new root key could theoretically provide enough signatures to meet the threshold for new key self-signatures required during root metadata update.

A scenario where this attack could be relevant is amazingly unlikely in practice to the point where labeling this issue as a security advisory is potentially overstating the impact of the issue. Given that new root keys only become trusted by the client after a successful root metadata update, which also requires the quorum of signatures from old trusted root keys, this issue has been evaluated as low in severity.

In particular, in order to exploit this vulnerability, an attacker must:

1. Control one new root key.
2. Craft a new root metadata file such that there is a number of signatures by this new root key greater than or equal to the new threshold.
3. Cause a valid threshold of the old root keys to sign this new root metadata file.
4. Cause this new root metadata file to be published on the repository.
5. Cause clients to rotate to this new root metadata file.

### Patches
A [fix](https://github.com/theupdateframework/tuf/commit/83ac7be525b733f79a7e9bc573ec580ec835f179) is available since [version 0.16.0](https://github.com/theupdateframework/tuf/releases/tag/v0.16.0).

### Workarounds
No workarounds are known for this issue.

### References
* Pull request resolving the issue [PR 1218](https://github.com/theupdateframework/tuf/pull/1218)
* Pull request which introduced the faulty computation of signature threshold on new root metadata [PR 1101](https://github.com/theupdateframework/tuf/pull/1101)
* A similar previous issue with incorrectly computed signature thresholds in tuf is described in [GHSA-pwqf-9h7j-7mv8](https://github.com/theupdateframework/tuf/security/advisories/GHSA-pwqf-9h7j-7mv8)
TensorFlow vulnerable to `CHECK` fail in `LRNGrad`
Advisory: GHSA-9942-r22v-78cp
Aliases: CVE-2022-35985
Published: 2022-09-16T22:29:52Z
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
If `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
depth_radius = 1
bias = 1.59018219
alpha = 0.117728651
beta = 0.404427052
input_grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
input_image = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
output_image = tf.random.uniform(shape=[4, 4, 4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
tf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_image, output_image=output_image, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)
```

### Patches
We have patched the issue in GitHub commit [bd90b3efab4ec958b228cd7cfe9125be1c0cf255](https://github.com/tensorflow/tensorflow/commit/bd90b3efab4ec958b228cd7cfe9125be1c0cf255).

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
Barbican authorization flaw before v14.0.0
Advisory: GHSA-p2jg-q8hw-p7gc
Aliases: CVE-2022-23451
Published: 2022-09-07T00:01:53Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H

An authorization flaw was found in openstack-barbican. The default policy rules for the secret metadata API allowed any authenticated user to add, modify, or delete metadata from any secret regardless of ownership. This flaw allows an attacker on the network to modify or delete protected data, causing a denial of service by consuming protected resources.
OAuthLib vulnerable to DoS when attacker provides malicious IPV6 URI
Advisory: GHSA-3pgj-pg6c-r5p7
Aliases: CVE-2022-36087
Published: 2022-09-16T21:02:52Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:N/I:N/A:H

### Impact
- Attacker providing malicious redirect uri can cause DoS to oauthlib's web application.
- Attacker can also leverage usage of `uri_validate` functions depending where it is used.

_What kind of vulnerability is it? Who is impacted?_

Oauthlib applications using OAuth2.0 provider support or use directly `uri_validate` function.

### Patches
_Has the problem been patched? What versions should users upgrade to?_

Issue fixed in 3.2.2 release.

### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_

The `redirect_uri` can be verified in web toolkit (i.e `bottle-oauthlib`, `django-oauth-toolkit`, ...) before oauthlib is called. A sample check if `:` is present to reject the request can prevent the DoS, assuming no port or IPv6 is fundamentally required.

### References
Attack Vector:
- Attacker providing malicious redirect uri:
https://github.com/oauthlib/oauthlib/blob/d4bafd9f1d0eba3766e933b1ac598cbbf37b8914/oauthlib/oauth2/rfc6749/grant_types/base.py#L232
- Vulnerable `uri_validate` functions:
https://github.com/oauthlib/oauthlib/blob/2b8a44855a51ad5a5b0c348a08c2564a2e197ea2/oauthlib/uri_validate.py

### PoC
```python
is_absolute_uri("http://[:::::::::::::::::::::::::::::::::::::::]/path")
```

### Acknowledgement
Special thanks to Sebastian Chnelik - PyUp.io
TensorFlow vulnerable to `CHECK` fail in `CollectiveGather`
Advisory: GHSA-fhfc-2q7x-929f
Aliases: CVE-2022-35994
Published: 2022-09-16T22:11:58Z
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 `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_key=arg_3, shape=arg_4,
                            communication_hint=arg_5, timeout_seconds=arg_6, name=arg_7)
```

### Patches
We have patched the issue in GitHub commit [c1f491817dec39a26be3c574e86a88c30f3c4770](https://github.com/tensorflow/tensorflow/commit/c1f491817dec39a26be3c574e86a88c30f3c4770).

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.
Apache Pinot has Groovy Function support enabled by default
Advisory: GHSA-qj9p-jvmw-82rh
Aliases: CVE-2022-26112
Published: 2022-09-25T00:00:26Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Pinot allows you to run any function using Apache Groovy scripts. In versions prior to 0.10.0, Pinot query endpoint and realtime ingestion layer has a vulnerability in unprotected environments due to groovy function support being enabled by default. This issue has been fixed by making function support disabled by default, in version 0.11.0. A potential workaround is to disable groovy script support.
Apache IoTDB Session Fixation vulnerability
Advisory: GHSA-g6vm-3ch8-c6jq
Aliases: CVE-2022-38369
Published: 2022-09-06T00:00:27Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Apache IoTDB version 0.13.0 is vulnerable to session id attack. Users should upgrade to version 0.13.1 which addresses this issue.
matrix-appservice-irc vulnerable to IRC mode parameter confusion
Advisory: GHSA-cq7q-5c67-w39w
Aliases: CVE-2022-39202
Published: 2022-09-15T03:26:25Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:U/C:L/I:L/A:L

### Impact

IRC allows you to specify multiple modes in a single mode command. Due to a bug in the underlying matrix-org/node-irc library, affected versions of matrix-appservice-irc perform parsing of such modes incorrectly, potentially resulting in the wrong user being given permissions.

Mode commands can only be executed by privileged users, so this can only be abused if an operator is tricked into running the command on behalf of an attacker.

### Patches

The vulnerability has been patched in matrix-appservice-irc 0.35.0.

### Workarounds

Refrain from entering mode commands suggested by untrusted users. Avoid using multiple modes in a single command.

### References

- https://matrix.org/blog/2022/09/13/security-release-of-matrix-appservice-irc-0-35-0-high-severity

### Credits

Discovered and reported by [Val Lorentz](https://valentin-lorentz.fr/).

### For more information

If you have any questions or comments about this advisory email us at [security@matrix.org](mailto:security@matrix.org).
TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`
Advisory: GHSA-37jf-mjv6-xfqw
Aliases: CVE-2022-35999
Published: 2022-09-16T19:24:49Z
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 `Conv2DBackpropInput` receives empty `out_backprop` inputs (e.g. `[3, 1, 0, 1]`), the current CPU/GPU kernels `CHECK` fail (one with dnnl, the other with cudnn). This can be used to trigger a denial of service attack.
```python
import tensorflow as tf
import numpy as np
input_sizes = [3, 1, 1, 2]
filter = np.ones([1, 3, 2, 3])
out_backprop = np.ones([3, 1, 0, 3])
strides = [1, 1, 2, 1]
padding = 'VALID'

tf.raw_ops.Conv2DBackpropInput(
   input_sizes = input_sizes,
   filter = filter,
   out_backprop = out_backprop,
   strides = strides,
   padding = padding
)
```

### Patches
We have patched the issue in GitHub commit [27a65a43cf763897fecfa5cdb5cc653fc5dd0346](https://github.com/tensorflow/tensorflow/commit/27a65a43cf763897fecfa5cdb5cc653fc5dd0346).

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.
Indy's NODE_UPGRADE transaction vulnerable to remote code execution
Advisory: GHSA-r6v9-p59m-gj2p
Aliases: CVE-2022-31020
Published: 2022-09-02T21:55:20Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H

### Impact

The `pool-upgrade` request handler in Indy-Node `<=1.12.4` allows an improperly authenticated attacker to remotely execute code on nodes within the network.

Network operators are strongly encouraged to upgrade to the latest Indy-Node release `>=1.12.5` as soon as possible.

### Patches

The `pool-upgrade` request handler in Indy-Node `>=1.12.5` has been updated to properly authenticate `pool-upgrade` transactions before any processing is performed by the request handler.  The transactions are further sanitized to prevent remote code execution.

### Mitigations

Network operators are strongly encouraged to upgrade to the latest Indy-Node release `>=1.12.5` as soon as possible.

### Acknowledgements
Thank you to @shakreiner at CyberArk Labs for finding and responsibly disclosing this issue.
matrix-js-sdk subject to user impersonation due to key/device identifier confusion in SAS verification
Advisory: GHSA-5w8r-8pgj-5jmf
Aliases: CVE-2022-39250
Published: 2022-09-30T22:46:50Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:N/I:H/A:N

## Impact

An attacker cooperating with a malicious homeserver could interfere with the verification flow between two users, injecting its own cross-signing user identity in place of one of the users’ identities, leading to the other device trusting/verifying the user identity under the control of the homeserver instead of the intended one.

The vulnerability is a bug in the matrix-js-sdk, caused by checking and signing user identities and devices in two separate steps, and inadequately fixing the keys to be signed between those steps.

Even though the attack is partly made possible due to the design decision of treating cross-signing user identities as Matrix devices on the server side (with their device ID set to the public part of the user identity key), no other examined implementations were vulnerable.

## Patches

The matrix-js-sdk has been modified to double check that the key signed is the one that was verified instead of just referencing the key by ID. An additional check has been made to report an error when one of the device ID matches a cross-signing key.

## Workarounds

As this attack requires coordination between a malicious homeserver and an attacker -- if you trust your homeserver no particular workaround is needed. 

As a potential way of detecting compromise, it’s possible to review your device list or the device list of other users for devices with IDs in the form of a base64 cross-signing key (`5XaczGNlfz0bl8R1IX5qn+tBoue2tWJqLMh+SDUuvCk`) instead of classical device ID (`SEHACYDHMG`).

## References
Blog post: https://matrix.org/blog/2022/09/28/upgrade-now-to-address-encryption-vulns-in-matrix-sdks-and-clients

## For more information
If you have any questions or comments about this advisory, e-mail us at security@matrix.org
d3-color vulnerable to ReDoS
Advisory: GHSA-36jr-mh4h-2g58
Published: 2022-09-29T14:12:55Z

The d3-color module provides representations for various color spaces in the browser. Versions prior to 3.1.0 are vulnerable to a Regular expression Denial of Service. This issue has been patched in version 3.1.0. There are no known workarounds.
CrafterCMS OS Command Injection vulnerability
Advisory: GHSA-j6x3-3jqq-m922
Aliases: CVE-2022-40635
Published: 2022-09-14T00:00:45Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

Improper Control of Dynamically-Managed Code Resources vulnerability in Crafter Studio of Crafter CMS allows authenticated developers to execute OS commands via Groovy Sandbox Bypass.
TensorFlow vulnerable to `CHECK` fail in `MaxPool`
Advisory: GHSA-j43h-pgmg-5hjq
Aliases: CVE-2022-35989
Published: 2022-09-16T22:28:06Z
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 `MaxPool` receives a window size input array `ksize` with dimensions greater than its input tensor `input`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
import numpy as np

input = np.ones([1, 1, 1, 1])
ksize = [1, 1, 2, 2]
strides = [1, 1, 1, 1]
padding = 'VALID'
data_format = 'NCHW'

tf.raw_ops.MaxPool(input=input, ksize=ksize, strides=strides, padding=padding, data_format=data_format)
```

### Patches
We have patched the issue in GitHub commit [32d7bd3defd134f21a4e344c8dfd40099aaf6b18](https://github.com/tensorflow/tensorflow/commit/32d7bd3defd134f21a4e344c8dfd40099aaf6b18).

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.
Shopware access control list bypassed via crafted specific URLs
Advisory: GHSA-qc43-pgwq-3q2q
Aliases: CVE-2022-36102
Published: 2022-09-16T21:01:29Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L

### Impact
If backend admin controllers are called with a certain notation, the ACL could be bypassed. Users could execute actions, which they are normally not able to do.

### Patches
We recommend updating to the current version 5.7.15. You can get the update to 5.7.15 regularly via the Auto-Updater or directly via the download overview.
https://www.shopware.com/en/changelog-sw5/#5-7-15

For older versions you can use the Security Plugin:
https://store.shopware.com/en/swag575294366635f/shopware-security-plugin.html


### References
https://docs.shopware.com/en/shopware-5-en/security-updates/security-update-09-2022
TensorFlow vulnerable to `CHECK` fail in `TensorListScatter` and `TensorListScatterV2`
Advisory: GHSA-vm7x-4qhj-rrcq
Aliases: CVE-2022-35991
Published: 2022-09-16T22:15:05Z
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 `TensorListScatter` and `TensorListScatterV2` receive an `element_shape` of a rank greater than one, they give a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None)
arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)
arg_3=''
tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, 
element_shape=arg_2, name=arg_3)
```

### Patches
We have patched the issue in GitHub commit [bb03fdf4aae944ab2e4b35c7daa051068a8b7f61](https://github.com/tensorflow/tensorflow/commit/bb03fdf4aae944ab2e4b35c7daa051068a8b7f61).

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.
Besu VM vulnerable to gas allocation error in CALL operations
Advisory: GHSA-4456-w38r-m53x
Aliases: CVE-2022-36025
Published: 2022-09-23T20:24:21Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H

### Impact
An error in 32 bit signed and unsigned types in the calculation of available gas in the CALL operations (including DELEGATECALL) results in incorrect gas being passed into called contracts and incorrect gas being returned after call execution.  Where the amount of gas makes a difference in the success or failure, or if the gas is a negative 64 bit value, the execution will result in a different state root than expected, resulting in a consensus failure in networks with multiple EVM implementations. 

In networks with a single EVM implementation this can be used to execute with significantly more gas than then transaction requested, possibly exceeding gas limitations. 

### Patches
Version 22.7.1 contains a fix, ensuring that excess gas will not be allocated to inner transaction calls and correcting the excess gas errors.

### Workarounds
Reverting to version 22.1.3 or earlier will prevent incorrect execution. However many ethereum mainnet networks require changes in more recent versions of Besu and should not use older versions of besu and should instead use the patched version.  

Ethereum Classic and other networks not depending on a Proof of Stake transition should function fine with version 22.1.3 or earlier.

### For more information
Issue was found by [Martin Holst Swende](https://github.com/holiman) using [goevmlab](https://github.com/holiman/goevmlab), it is believed that no production networks have transactions that would trigger this failure.
TYPO3 CMS vulnerable to Cross-Site Scripting in <f:asset.css> view helper
Advisory: GHSA-fv2m-9249-qx85
Aliases: CVE-2022-36108
Published: 2022-09-16T17:14:27Z
Severity: type: CVSS_V3
score: CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N

> ### Meta
> * CVSS: `CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:C/C:L/I:L/A:N/E:F/RL:O/RC:C` (4.1)

### Problem
It has been discovered that the `f:asset.css` view helper is vulnerable to cross-site scripting when user input is passed as variables to the CSS. 

### Solution
Update to TYPO3 version 10.4.32 or 11.5.16 that fix the problem described above.

### Credits
Thanks to TYPO3 contributor member Frank Nägler who reported this issue and to TYPO3 core & security team member Oliver Hader who fixed the issue.

### References
* [TYPO3-CORE-SA-2022-010](https://typo3.org/security/advisory/typo3-core-sa-2022-010)
TensorFlow vulnerable to `CHECK` fail in `TensorListFromTensor`
Advisory: GHSA-9v8w-xmr4-wgxp
Aliases: CVE-2022-35992
Published: 2022-09-16T22:14:20Z
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 `TensorListFromTensor` receives an `element_shape` of a rank greater than one, it gives a `CHECK` fail that can trigger a denial of service attack.
```python
import tensorflow as tf
arg_0=tf.random.uniform(shape=(6, 6, 2), dtype=tf.bfloat16, maxval=None)
arg_1=tf.random.uniform(shape=(6, 9, 1, 3), dtype=tf.int64, maxval=65536)
arg_2=''
tf.raw_ops.TensorListFromTensor(tensor=arg_0, element_shape=arg_1, name=arg_2)
```

### Patches
We have patched the issue in GitHub commit [3db59a042a38f4338aa207922fa2f476e000a6ee](https://github.com/tensorflow/tensorflow/commit/3db59a042a38f4338aa207922fa2f476e000a6ee).

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.
steal Inefficient Regular Expression Complexity vulnerability via string variable
Advisory: GHSA-rgqx-226f-2xp4
Aliases: CVE-2022-37259
Published: 2022-09-21T00:00:38Z
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 Regular Expression Denial of Service (ReDoS) flaw was found in stealjs steal 2.2.4 via the string variable in babel.js.
RCE vulnerability in Jenkins DotCi Plugin
Advisory: GHSA-x3jj-rgw9-7r5g
Aliases: CVE-2022-41237
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:H/A:H

DotCi Plugin 2.40.00 and earlier does not configure its YAML parser to prevent the instantiation of arbitrary types.

This results in a remote code execution (RCE) vulnerability exploitable by attackers able to modify `.ci.yml` files in SCM. This plugin has been suspended.