ecosystem stringclasses 14 values | vuln_id stringlengths 10 19 | summary stringlengths 4 267 ⌀ | details stringlengths 9 13.5k | aliases stringlengths 17 144 ⌀ | modified_date stringdate 2010-05-27 05:47:00 2022-05-10 08:46:52 | published_date stringdate 2005-12-31 05:00:00 2022-05-10 08:46:50 | severity stringclasses 5 values | score float64 0 10 ⌀ | cwe_id stringclasses 988 values | refs stringlengths 30 17.7k ⌀ | introduced stringlengths 75 4.26k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2016-37 | null | Radicale before 1.1 allows remote authenticated users to bypass owner_write and owner_only limitations via regex metacharacters in the user name, as demonstrated by ".*". | {'CVE-2015-8748'} | 2021-12-14T08:18:58.669643Z | 2016-02-03T18:59:00Z | null | null | null | {'https://github.com/Kozea/Radicale/pull/341', 'http://www.securityfocus.com/bid/80255', 'https://nvd.nist.gov/vuln/detail/CVE-2015-8748', 'http://www.openwall.com/lists/oss-security/2016/01/06/4', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175776.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175738.html', 'https://pypi.org/project/radicale', 'http://www.debian.org/security/2016/dsa-3462', 'http://www.openwall.com/lists/oss-security/2016/01/05/7', 'https://github.com/Unrud/Radicale/commit/4bfe7c9f7991d534c8b9fbe153af9d341f925f98'} | null |
PyPI | GHSA-rxq3-5249-8hgg | Validation bypass vulnerability | Back in min June a security vulnerability was reported to the team, the reason for the slow response was due to ownership of some packages
was locked and we wanted to be sure to update all packages before any disclosure was released.
The issue is deemed being a Low severity vulnerability.
### Impact
This vulnerability impacts users who rely on the for last digits of personnummer to be a _real_ personnummer.
### Patches
The issue have been patched in all repositories. The following versions should be updated to as soon as possible:
C# 3.0.2
D 3.0.1
Dart 3.0.3
Elixir 3.0.0
Go 3.0.1
Java 3.3.0
JavaScript 3.1.0
Kotlin 1.1.0
Lua 3.0.1
PHP 3.0.2
Perl 3.0.0
Python 3.0.2
Ruby 3.0.1
Rust 3.0.0
Scala 3.0.1
Swift 1.0.1
If you are using any of the earlier packages, please update to latest.
### Workarounds
The issue arrieses from the regular expression allowing the first three digits in the last four digits of the personnummer to be
000, which is invalid. To mitigate this without upgrading, a check on the last four digits can be made to make sure it's not
000x.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Personnummer Meta](https://github.com/personnummer/meta/issues)
* Email us at [Personnummer Email](mailto:security@personnummer.dev)
### Credits
Niklas Sköldmark (Medborgarskolan) | null | 2022-03-03T05:12:48.920420Z | 2020-09-09T17:29:41Z | LOW | null | null | {'https://github.com/personnummer/python/security/advisories/GHSA-rxq3-5249-8hgg', 'https://pypi.org/project/personnummer/'} | null |
PyPI | GHSA-gq9m-qvpx-68hc | Insufficient Entropy in werkzeug | Pallets Werkzeug before 0.15.3, when used with Docker, has insufficient debugger PIN randomness because Docker containers share the same machine id. | {'CVE-2019-14806'} | 2022-03-03T05:12:59.845405Z | 2019-08-21T16:15:24Z | HIGH | null | {'CWE-331'} | {'https://palletsprojects.com/blog/werkzeug-0-15-3-released/', 'https://github.com/pallets/werkzeug/blob/7fef41b120327d3912fbe12fb64f1951496fcf3e/src/werkzeug/debug/__init__.py#L168', 'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14806'} | null |
PyPI | PYSEC-2020-272 | null | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15192', 'GHSA-8fxw-76px-3rxv'} | 2021-12-09T06:34:40.896350Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | GHSA-785x-qw4v-6872 | Improper Output Neutralization and Improper Encoding or Escaping of Output for Logs in ansible | An Improper Output Neutralization for Logs flaw was found in Ansible when using the uri module, where sensitive data is exposed to content and json output. This flaw allows an attacker to access the logs or outputs of performed tasks to read keys used in playbooks from other users within the uri module. The highest threat from this vulnerability is to data confidentiality. | {'CVE-2020-14330'} | 2022-03-03T05:12:08.635409Z | 2022-02-09T22:00:08Z | MODERATE | null | {'CWE-117', 'CWE-116'} | {'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-14330', 'https://nvd.nist.gov/vuln/detail/CVE-2020-14330', 'https://github.com/ansible/ansible', 'https://github.com/ansible/ansible/issues/68400', 'https://www.debian.org/security/2021/dsa-4950'} | null |
PyPI | GHSA-9w4w-cpc8-h2fq | Exposure of Sensitive Information to an Unauthorized Actor in httpie | ### Impact
HTTPie have the practical concept of [sessions](https://httpie.io/docs/cli/sessions), which help users to persistently store some of the state that belongs to the outgoing requests and incoming responses on the disk for further usage. As an example, we can make an authenticated request and save it to a [named session](https://httpie.io/docs/cli/named-sessions) called `api`:
```bash
$ http --session api -a user:pass pie.dev/basic-auth/user/pass
```
```json
{
"authenticated": true,
"user": "user"
}
```
Since we have now saved the authentication data to that session, we won‘t have to enter it again and again on every invocation. We can simply reference the session, and HTTPie will use the saved state directly from it:
```bash
$ http --session api pie.dev/basic-auth/user/pass
```
```json
{
"authenticated": true,
"user": "user"
}
```
One particular use case of these sessions is storing cookies (commonly referred to as a `Cookie Jar`). If a response has a `Set-Cookie`
header, HTTPie will parse it and store the actual cookie in the session. And from that point on, all outgoing requests will attach that cookie (in the form of a `Cookie` header).
This is extremely useful, especially when you are dealing with websites which manage their own state on the client-side through cookies.
```bash
$ http -F --session jar pie.dev/cookies/set/x/y
```
```json
{
"cookies": {
"x": "y"
}
}
```
Before `3.1.0`, HTTPie didn‘t distinguish between cookies and hosts they belonged. This behavior resulted in the exposure of some cookies when there are redirects originating from the actual host to a third party website, e.g:
```bash
$ http -F --session jar pie.dev/redirect-to url==https://httpbin.org/cookies
```
(Pre 3.1.0)
```json
{
"cookies": {
"x": "y"
}
}
```
(Post 3.1.0)
```json
{
"cookies": {}
}
```
This behavior has been corrected in this release (with taking [RFC 6265 — HTTP State Management Mechanism](https://datatracker.ietf.org/doc/html/rfc6265) into the consideration).
A huge credit goes to [@Glyph](https://github.com/glyph) for disclosing the original vulnerability to us (through [huntr.dev](http://huntr.dev/)).
### Patches
We suggest users to upgrade their HTTPie version to `3.1.0` or higher, and run `httpie cli sessions upgrade` command on their sessions.
### For more information
If you have any questions or comments about this advisory:
* Email us: [`security@httpie.io`](mailto:security@httpie.io)
> Please note that this entry is covered by both [CVE-2022-24737](https://www.cvedetails.com/cve/CVE-2022-24737) and [CVE-2022-0430](https://nvd.nist.gov/vuln/detail/CVE-2022-0430).
| {'CVE-2022-24737'} | 2022-03-29T22:31:56.406062Z | 2022-03-07T23:44:28Z | MODERATE | null | {'CWE-200'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/4QZD2AZOL7XLNZVAV6GDNXYU6MFRU5RS/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24737', 'https://github.com/httpie/httpie/security/advisories/GHSA-9w4w-cpc8-h2fq', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/R5VYSYKEKVZEVEBIWAADGDXG4Y3EWCQ3/', 'https://github.com/httpie/httpie', 'https://github.com/httpie/httpie/releases/tag/3.1.0', 'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TXFCHGTW3V32GD6GXXJZE5QAOSDT3RTY/'} | null |
PyPI | PYSEC-2021-272 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37650', 'GHSA-f8h4-7rgh-q2gm'} | 2021-08-27T03:22:43.967494Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm', 'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} | null |
PyPI | GHSA-7hpj-hfcr-5qwm | Code injection in FreeIPA | A flaw was found in IPA, all 4.6.x versions before 4.6.7, all 4.7.x versions before 4.7.4 and all 4.8.x versions before 4.8.3, in the way the internal function ber_scanf() was used in some components of the IPA server, which parsed kerberos key data. An unauthenticated attacker who could trigger parsing of the krb principal key could cause the IPA server to crash or in some conditions, cause arbitrary code to be executed on the server hosting the IPA server. | {'CVE-2019-14867'} | 2022-03-03T05:14:16.241307Z | 2021-12-06T18:17:38Z | HIGH | null | {'CWE-94', 'CWE-400'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WLFL5XDCJ3WT6JCLCQVKHZBLHGW7PW4T/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14867', 'https://www.freeipa.org/page/Releases/4.8.3', 'https://access.redhat.com/errata/RHBA-2019:4268', 'https://github.com/pypa/advisory-db/tree/main/vulns/ipa/PYSEC-2019-28.yaml', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-14867', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/67SEUWJAJ5RMH5K4Q6TS2I7HIMXUGNKF/', 'https://www.freeipa.org/page/Releases/4.6.7', 'https://access.redhat.com/errata/RHSA-2020:0378', 'https://www.freeipa.org/page/Releases/4.7.4'} | null |
PyPI | PYSEC-2021-788 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37677', 'GHSA-qfpc-5pjr-mh26'} | 2021-12-09T06:35:39.087428Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26', 'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | null |
PyPI | PYSEC-2021-622 | null | TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-h67m-xg8f-fxcf', 'CVE-2021-41213'} | 2021-12-09T06:35:09.356832Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf', 'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | null |
PyPI | PYSEC-2021-337 | null | This affects all versions of package Flask-User. When using the make_safe_url function, it is possible to bypass URL validation and redirect a user to an arbitrary URL by providing multiple back slashes such as /////evil.com/path or \\\evil.com/path. This vulnerability is only exploitable if an alternative WSGI server other than Werkzeug is used, or the default behaviour of Werkzeug is modified using 'autocorrect_location_header=False. | {'CVE-2021-23401', 'SNYK-PYTHON-FLASKUSER-1293188', 'GHSA-4298-89hc-6rfv'} | 2021-09-26T23:32:30.327481Z | 2021-07-05T11:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-4298-89hc-6rfv', 'https://github.com/lingthio/Flask-User/blob/master/flask_user/user_manager__utils.py', 'https://github.com/lingthio/Flask-User', 'https://snyk.io/vuln/SNYK-PYTHON-FLASKUSER-1293188'} | null |
PyPI | GHSA-5pr9-v234-jw36 | Remote Code Execution via traversal in TAL expressions | ### Impact
Most Python modules are not available for using in TAL expressions that you can add through-the-web, for example in Zope Page Templates. This restriction avoids file system access, for example via the 'os' module. But some of the untrusted modules are available indirectly through Python modules that are available for direct use.
By default, you need to have the Manager role to add or edit Zope Page Templates through the web. Only sites that allow untrusted users to add/edit Zope Page Templates through the web are at risk.
### Patches
The problem has been fixed in Zope 5.2 and 4.6.
### Workarounds
A site administrator can restrict adding/editing Zope Page Templates through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing Zope Page Templates through the web should be restricted to trusted users only.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Zope issue tracker](https://github.com/zopefoundation/Zope/issues)
* Email us at [security@plone.org](mailto:security@plone.org)
| null | 2022-03-03T05:13:56.055315Z | 2021-06-18T18:44:01Z | MODERATE | null | {'CWE-22'} | {'https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36'} | null |
PyPI | PYSEC-2021-82 | null | Plone through 5.2.4 allows remote authenticated managers to conduct SSRF attacks via an event ical URL, to read one line of a file. | {'CVE-2021-33510', 'GHSA-4mg4-wvmx-5332'} | 2021-06-09T05:01:20.334920Z | 2021-05-21T22:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-4mg4-wvmx-5332', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'https://plone.org/security/hotfix/20210518/server-side-request-forgery-via-event-ical-url'} | null |
PyPI | PYSEC-2021-767 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-4xfp-4pfp-89wg', 'CVE-2021-37656'} | 2021-12-09T06:35:37.172867Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg'} | null |
PyPI | PYSEC-2015-27 | null | The editor in IPython Notebook before 3.2.2 and Jupyter Notebook 4.0.x before 4.0.5 allows remote attackers to execute arbitrary JavaScript code via a crafted file, which triggers a redirect to files/, related to MIME types. | {'CVE-2015-7337'} | 2021-07-15T02:22:16.210618Z | 2015-09-29T19:59:00Z | null | null | null | {'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/167670.html', 'http://seclists.org/oss-sec/2015/q3/558', 'https://bugzilla.redhat.com/show_bug.cgi?id=1264067', 'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5', 'http://seclists.org/oss-sec/2015/q3/634', 'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967', 'https://security.gentoo.org/glsa/201512-02'} | null |
PyPI | GHSA-5hj3-vjjf-f5m7 | Heap OOB in `SdcaOptimizerV2` | ### Impact
An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`:
```python
import tensorflow as tf
tf.raw_ops.SdcaOptimizerV2(
sparse_example_indices=[[1]],
sparse_feature_indices=[[1]],
sparse_feature_values=[[1.0,2.0]],
dense_features=[[1.0]],
example_weights=[1.0],
example_labels=[],
sparse_indices=[1],
sparse_weights=[1.0],
dense_weights=[[1.0]],
example_state_data=[[100.0,100.0,100.0,100.0]],
loss_type='logistic_loss',
l1=100.0,
l2=100.0,
num_loss_partitions=1,
num_inner_iterations=1,
adaptive=True)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples.
### Patches
We have patched the issue in GitHub commit [a4e138660270e7599793fa438cd7b2fc2ce215a6](https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37672'} | 2022-03-03T05:13:53.533223Z | 2021-08-25T14:41:39Z | MODERATE | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37672', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7', 'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | null |
PyPI | PYSEC-2020-337 | null | In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26271', 'GHSA-q263-fvxm-m5mw'} | 2021-12-09T06:35:16.854014Z | 2020-12-10T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw'} | null |
PyPI | PYSEC-2019-234 | null | In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0. | {'CVE-2019-16778', 'GHSA-844w-j86r-4x2j'} | 2021-12-09T06:35:11.891064Z | 2019-12-16T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j', 'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} | null |
PyPI | PYSEC-2018-91 | null | cext/manifest.c in Mercurial before 4.7.2 has an out-of-bounds read during parsing of a malformed manifest entry. | {'CVE-2018-17983'} | 2021-08-27T03:22:07.367975Z | 2018-10-04T23:29:00Z | null | null | null | {'https://www.mercurial-scm.org/wiki/WhatsNew#Mercurial_4.7.2_.282018-10-01.29', 'https://www.mercurial-scm.org/repo/hg/rev/5405cb1a7901'} | null |
PyPI | PYSEC-2020-17 | null | An issue was found in Apache Airflow versions 1.10.10 and below. It was discovered that many of the admin management screens in the new/RBAC UI handled escaping incorrectly, allowing authenticated users with appropriate permissions to create stored XSS attacks. | {'GHSA-q4p3-qw5c-mhpc', 'CVE-2020-11983'} | 2020-07-21T18:45:00Z | 2020-07-17T00:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-q4p3-qw5c-mhpc', 'https://lists.apache.org/thread.html/r7255cf0be3566f23a768e2a04b40fb09e52fcd1872695428ba9afe91%40%3Cusers.airflow.apache.org%3E'} | null |
PyPI | PYSEC-2022-105 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-23hm-7w47-xw72', 'CVE-2022-21726'} | 2022-03-09T00:18:23.133344Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72', 'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} | null |
PyPI | PYSEC-2021-419 | null | TensorFlow is an open source platform for machine learning. In affected versions the `ImmutableConst` operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the `tstring` TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41227', 'GHSA-j8c8-67vp-6mx7'} | 2021-11-13T06:52:46.221231Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7', 'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b'} | null |
PyPI | GHSA-q7f7-544h-67h9 | FPE in TFLite pooling operations | ### Impact
The implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0.
### Patches
We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37684'} | 2022-03-03T05:14:04.291817Z | 2021-08-25T14:40:13Z | MODERATE | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37684', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q7f7-544h-67h9'} | null |
PyPI | PYSEC-2021-675 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-x83m-p7pv-ch8v', 'CVE-2021-29549'} | 2021-12-09T06:35:23.303837Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v', 'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | null |
PyPI | PYSEC-2021-225 | null | TensorFlow is an end-to-end open source platform for machine learning. The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-vfr4-x8j2-3rf9', 'CVE-2021-29588'} | 2021-08-27T03:22:37.053061Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vfr4-x8j2-3rf9', 'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} | null |
PyPI | PYSEC-2020-225 | null | An issue was discovered in OpenStack blazar-dashboard before 1.3.1, 2.0.0, and 3.0.0. A user allowed to access the Blazar dashboard in Horizon may trigger code execution on the Horizon host as the user the Horizon service runs under (because the Python eval function is used). This may result in Horizon host unauthorized access and further compromise of the Horizon service. All setups using the Horizon dashboard with the blazar-dashboard plugin are affected. | {'GHSA-939m-4xpw-v34v', 'CVE-2020-26943'} | 2021-08-27T03:21:56.229273Z | 2020-10-16T06:15:00Z | null | null | null | {'https://review.opendev.org/755810', 'https://security.openstack.org/ossa/OSSA-2020-007.html', 'https://review.opendev.org/755812', 'https://launchpad.net/bugs/1895688', 'http://www.openwall.com/lists/oss-security/2020/10/16/5', 'https://review.opendev.org/755813', 'https://review.opendev.org/755814', 'https://review.opendev.org/756064', 'https://github.com/advisories/GHSA-939m-4xpw-v34v'} | null |
PyPI | GHSA-g2xc-35jw-c63p | HTTP Request Smuggling: Invalid Transfer-Encoding in Waitress | ### Impact
Waitress would parse the `Transfer-Encoding` header and only look for a single string value, if that value was not `chunked` it would fall through and use the `Content-Length` header instead.
According to the HTTP standard `Transfer-Encoding` should be a comma separated list, with the inner-most encoding first, followed by any further transfer codings, ending with `chunked`.
Requests sent with:
```
Transfer-Encoding: gzip, chunked
```
Would incorrectly get ignored, and the request would use a `Content-Length` header instead to determine the body size of the HTTP message.
This could allow for Waitress to treat a single request as multiple requests in the case of HTTP pipelining.
### Patches
This issue is fixed in Waitress 1.4.0. This brings a range of changes to harden Waitress against potential HTTP request confusions, and may change the behaviour of Waitress behind non-conformist proxies.
Waitress will now return a 501 Not Implemented error if the `Transfer-Encoding` is not `chunked` or contains multiple elements. Waitress does not support any transfer codings such as `gzip` or `deflate`.
The Pylons Project recommends upgrading as soon as possible, while validating that the changes in Waitress don't cause any changes in behavior.
### Workarounds
Various reverse proxies may have protections against sending potentially bad HTTP requests to the backend, and or hardening against potential issues like this. If the reverse proxy doesn't use HTTP/1.1 for connecting to the backend issues are also somewhat mitigated, as HTTP pipelining does not exist in HTTP/1.0 and Waitress will close the connection after every single request (unless the Keep Alive header is explicitly sent... so this is not a fool proof security method).
### Issues/more security issues:
* open an issue at https://github.com/Pylons/waitress/issues (if not sensitive or security related)
* email the Pylons Security mailing list: pylons-project-security@googlegroups.com (if security related) | {'CVE-2019-16786'} | 2022-04-25T23:17:07.559471Z | 2019-12-20T23:04:18Z | HIGH | null | {'CWE-444'} | {'https://github.com/Pylons/waitress/security/advisories/GHSA-g2xc-35jw-c63p', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LYEOTGWJZVKPRXX2HBNVIYWCX73QYPM5/', 'https://docs.pylonsproject.org/projects/waitress/en/latest/#security-fixes', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16786', 'https://github.com/Pylons/waitress', 'https://github.com/Pylons/waitress/commit/f11093a6b3240fc26830b6111e826128af7771c3', 'https://access.redhat.com/errata/RHSA-2020:0720', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GVDHR2DNKCNQ7YQXISJ45NT4IQDX3LJ7/'} | null |
PyPI | PYSEC-2021-730 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8rm6-75mf-7r7r', 'CVE-2021-29604'} | 2021-12-09T06:35:32.707618Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rm6-75mf-7r7r'} | null |
PyPI | GHSA-9j59-75qj-795w | Path traversal in Pillow | If the path to the temporary directory on Linux or macOS contained a space, this would break removal of the temporary image file after im.show() (and related actions), and potentially remove an unrelated file. This been present since PIL. | {'CVE-2022-24303'} | 2022-04-07T15:17:03.062945Z | 2022-03-11T23:10:32Z | CRITICAL | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/XR6UP2XONXOVXI4446VY72R63YRO2YTP/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/W4ZUXPKEX72O3E5IHBPVY5ZCPMJ4GHHV/', 'https://github.com/python-pillow/Pillow/commit/427221ef5f19157001bf8b1ad7cfe0b905ca8c26', 'https://github.com/python-pillow/Pillow', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24303', 'https://github.com/python-pillow/Pillow/pull/3450', 'https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html#security'} | null |
PyPI | PYSEC-2021-360 | null | OpenStack Neutron before 16.4.1, 17.x before 17.1.3, and 18.0.0 allows hardware address impersonation when the linuxbridge driver with ebtables-nft is used on a Netfilter-based platform. By sending carefully crafted packets, anyone in control of a server instance connected to the virtual switch can impersonate the hardware addresses of other systems on the network, resulting in denial of service or in some cases possibly interception of traffic intended for other destinations. | {'CVE-2021-38598'} | 2021-10-11T01:16:41.119513Z | 2021-08-23T05:15:00Z | null | null | null | {'https://launchpad.net/bugs/1938670'} | null |
PyPI | GHSA-4v5p-v5h9-6xjx | `CHECK`-failures in Tensorflow | ### Impact
An attacker can trigger denial of service via assertion failure by altering a `SavedModel` on disk such that `AttrDef`s of some operation are duplicated.
### Patches
We have patched the issue in GitHub commit [c2b31ff2d3151acb230edc3f5b1832d2c713a9e0](https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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. | {'CVE-2022-23565'} | 2022-03-03T05:13:16.682166Z | 2022-02-09T23:49:01Z | MODERATE | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx', 'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23565'} | null |
PyPI | PYSEC-2021-674 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract(https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29548', 'GHSA-p45v-v4pw-77jr'} | 2021-12-09T06:35:23.143235Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p45v-v4pw-77jr'} | null |
PyPI | PYSEC-2021-224 | null | TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29587', 'GHSA-j7rm-8ww4-xx2g'} | 2021-08-27T03:22:36.876924Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j7rm-8ww4-xx2g'} | null |
PyPI | GHSA-v9qg-3j8p-r63v | Uncontrolled Recursion in Django | An issue was discovered in Django 1.11.x before 1.11.23, 2.1.x before 2.1.11, and 2.2.x before 2.2.4. If passed certain inputs, django.utils.encoding.uri_to_iri could lead to significant memory usage due to a recursion when repercent-encoding invalid UTF-8 octet sequences. | {'CVE-2019-14235'} | 2022-03-03T05:13:29.097450Z | 2019-08-06T01:43:31Z | HIGH | null | {'CWE-674'} | {'https://seclists.org/bugtraq/2019/Aug/15', 'https://groups.google.com/forum/#!topic/django-announce/jIoju2-KLDs', 'https://security.netapp.com/advisory/ntap-20190828-0002/', 'https://security.gentoo.org/glsa/202004-17', 'https://www.debian.org/security/2019/dsa-4498', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00006.html', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14235', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00025.html', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://www.djangoproject.com/weblog/2019/aug/01/security-releases/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/STVX7X7IDWAH5SKE6MBMY3TEI6ZODBTK/'} | null |
PyPI | PYSEC-2020-224 | null | An information disclosure issue was found in Apache Superset 0.34.0, 0.34.1, 0.35.0, and 0.35.1. Authenticated Apache Superset users are able to retrieve other users' information, including hashed passwords, by accessing an unused and undocumented API endpoint on Apache Superset. | {'CVE-2020-1932', 'GHSA-fxjm-wvj9-9c39'} | 2021-09-02T01:10:59.006282Z | 2020-01-28T01:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-fxjm-wvj9-9c39', 'https://lists.apache.org/thread.html/r4e5323c3bc786005495311a6ff53ac6d990b2c7eb52941a1a13ce227%40%3Cdev.superset.apache.org%3E'} | null |
PyPI | GHSA-mpf2-q34c-fc6j | Infinite Loop in scapy | scapy 2.4.0 is affected by Denial of Service. The impact is infinite loop, resource consumption and program unresponsive. The component is _RADIUSAttrPacketListField.getfield(self..). The attack vector is over the network or in a pcap. both work. | {'CVE-2019-1010142'} | 2022-03-03T05:13:49.825940Z | 2019-07-22T14:53:58Z | HIGH | null | {'CWE-835'} | {'https://github.com/secdev/scapy/pull/1409/files#diff-441eff981e466959968111fc6314fe93L1058', 'https://github.com/secdev/scapy/pull/1409', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/42NRPMC3NS2QVFNIXYP6WV2T3LMLLY7E/', 'http://www.securityfocus.com/bid/106674', 'https://www.imperva.com/blog/scapy-sploit-python-network-tool-is-vulnerable-to-denial-of-service-dos-attack-cve-pending/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-1010142', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/T46XW4S5BCA3VV3JT3C5Q6LBEXSIACLN/'} | null |
PyPI | GHSA-67cx-rhhq-mfhq | High severity vulnerability that affects indico | ## Local file disclosure through LaTeX injection
### Impact
An external audit of the Indico codebase has discovered a vulnerability in Indico's LaTeX sanitization code, which could have malicious users to run unsafe LaTeX commands on the server. Such commands allowed for example to read local files (e.g. `indico.conf`).
As far as we know it is not possible to write files or execute code using this vulnerability.
### Patches
You need to update to [Indico 2.2.3](https://github.com/indico/indico/releases/tag/v2.2.3) as soon as possible.
We also released [Indico 2.1.10](https://github.com/indico/indico/releases/tag/v2.1.10) in case you cannot update to 2.2 for some reason.
See https://docs.getindico.io/en/stable/installation/upgrade/ for instructions on how to update.
### Workarounds
Setting `XELATEX_PATH = None` in `indico.conf` will result in an error when building a PDF, but without being able to run xelatex, the vulnerability cannot be abused.
### For more information
If you have any questions or comments about this advisory:
* Open a thread in [our forum](https://talk.getindico.io/)
* Email us privately at [indico-team@cern.ch](mailto:indico-team@cern.ch)
| null | 2022-03-03T05:13:16.859628Z | 2019-10-11T18:28:07Z | HIGH | null | {'CWE-77'} | {'https://github.com/indico/indico', 'https://github.com/indico/indico/security/advisories/GHSA-67cx-rhhq-mfhq', 'https://github.com/advisories/GHSA-67cx-rhhq-mfhq'} | null |
PyPI | GHSA-qr82-2c78-4m8h | Reference binding to nullptr in map operations | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.Map*` and `tf.raw_ops.OrderedMap*` operations:
```python
import tensorflow as tf
tf.raw_ops.MapPeek(
key=tf.constant([8],dtype=tf.int64),
indices=[],
dtypes=[tf.int32],
capacity=8,
memory_limit=128)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that `indices` is in ascending order, but does not check that `indices` is not empty.
### Patches
We have patched the issue in GitHub commit [532f5c5a547126c634fefd43bbad1dc6417678ac](https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37671'} | 2022-03-03T05:13:59.171617Z | 2021-08-25T14:41:42Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qr82-2c78-4m8h', 'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37671', 'https://github.com/tensorflow/tensorflow'} | null |
PyPI | PYSEC-2021-731 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite code for allocating `TFLiteIntArray`s is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27). An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in invalid value being given to `malloc`(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52). In this case, `ret->size` would dereference an invalid pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-jf7h-7m85-w2v2', 'CVE-2021-29605'} | 2021-12-09T06:35:32.887706Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jf7h-7m85-w2v2', 'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} | null |
PyPI | PYSEC-2021-361 | null | An issue was discovered in OpenStack Neutron before 16.4.1, 17.x before 17.2.1, and 18.x before 18.1.1. Authenticated attackers can reconfigure dnsmasq via a crafted extra_dhcp_opts value. | {'CVE-2021-40085'} | 2021-10-11T01:16:41.242054Z | 2021-08-31T18:15:00Z | null | null | null | {'https://security.openstack.org/ossa/OSSA-2021-005.html', 'http://www.openwall.com/lists/oss-security/2021/08/31/2', 'https://launchpad.net/bugs/1939733'} | null |
PyPI | GHSA-hwvq-6gjx-j797 | Special Element Injection in notebook | ### Impact
Untrusted notebook can execute code on load. This is a remote code execution, but requires user action to open a notebook.
### Patches
5.7.11, 6.4.1
### References
[OWASP Page on Injection Prevention](https://cheatsheetseries.owasp.org/cheatsheets/Injection_Prevention_Cheat_Sheet.html#injection-prevention-rules)
### For more information
If you have any questions or comments about this advisory, or vulnerabilities to report, please email our security list security@ipython.org.
Credit: Guillaume Jeanne from Google
### Example:
A notebook with the following content in a cell and it would display an alert when opened for the first time in Notebook (in an untrusted state):
```
{ "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<select><iframe></select><img src=x: onerror=alert('xss')>\n"], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "" ] }
```` | {'CVE-2021-32798'} | 2022-03-03T05:13:42.124259Z | 2021-08-23T19:40:38Z | CRITICAL | null | {'CWE-79'} | {'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5', 'https://github.com/jupyter/notebook/security/advisories/GHSA-hwvq-6gjx-j797', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32798'} | null |
PyPI | GHSA-6gv8-p3vj-pxvr | Null pointer dereference in `UncompressElement` | ### Impact
The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference:
```python
import tensorflow as tf
data = tf.data.Dataset.from_tensors([0.0])
tf.raw_ops.UncompressElement(
compressed=tf.data.experimental.to_variant(data),
output_types=[tf.int64],
output_shapes=[2])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`.
### Patches
We have patched the issue in GitHub commit [7bdf50bb4f5c54a4997c379092888546c97c3ebd](https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37649'} | 2022-03-03T05:13:59.627726Z | 2021-08-25T14:43:27Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37649', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gv8-p3vj-pxvr', 'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} | null |
PyPI | PYSEC-2020-16 | null | An issue was found in Apache Airflow versions 1.10.10 and below. When using CeleryExecutor, if an attack can connect to the broker (Redis, RabbitMQ) directly, it was possible to insert a malicious payload directly to the broker which could lead to a deserialization attack (and thus remote code execution) on the Worker. | {'GHSA-9g2w-5f3v-mfmm', 'CVE-2020-11982'} | 2020-07-24T18:22:00Z | 2020-07-17T00:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-9g2w-5f3v-mfmm', 'https://lists.apache.org/thread.html/r7255cf0be3566f23a768e2a04b40fb09e52fcd1872695428ba9afe91%40%3Cusers.airflow.apache.org%3E'} | null |
PyPI | PYSEC-2018-90 | null | The mpatch_decode function in mpatch.c in Mercurial before 4.6.1 mishandles certain situations where there should be at least 12 bytes remaining after the current position in the patch data, but actually are not, aka OVE-20180430-0001. | {'CVE-2018-13348'} | 2021-08-27T03:22:07.326002Z | 2018-07-06T00:29:00Z | null | null | null | {'https://www.mercurial-scm.org/repo/hg/rev/90a274965de7', 'https://www.mercurial-scm.org/wiki/WhatsNew#Mercurial_4.6.1_.282018-06-06.29', 'https://lists.debian.org/debian-lts-announce/2020/07/msg00032.html'} | null |
PyPI | GHSA-x8h6-xgqx-jqgp | Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad` | ### Impact
The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty:
```python
import tensorflow as tf
orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64)
orig_output = tf.constant([], dtype=tf.int64)
out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64)
row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
```
The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process
```python
import tensorflow as tf
orig_input = tf.constant([1], shape=[1], dtype=tf.int64)
orig_output = tf.constant([1], shape=[1], dtype=tf.int64)
out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.
### Patches
We have patched the issue in GitHub commit [32fdcbff9d06d010d908fcc4bd4b36eb3ce15925](https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29580'} | 2022-03-03T05:13:55.417003Z | 2021-05-21T14:26:26Z | LOW | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29580', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp'} | null |
PyPI | GHSA-hf64-x4gq-p99h | Out-of-bounds Read | In Pillow before 8.1.0, SGIRleDecode has a 4-byte buffer over-read when decoding crafted SGI RLE image files because offsets and length tables are mishandled. | {'CVE-2020-35655'} | 2022-03-03T05:13:58.853032Z | 2021-03-18T19:55:34Z | HIGH | null | {'CWE-125'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6BYVI5G44MRIPERKYDQEL3S3YQCZTVHE/', 'https://pillow.readthedocs.io/en/stable/releasenotes/index.html', 'https://nvd.nist.gov/vuln/detail/CVE-2020-35655', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BF553AMNNNBW7SH4IM4MNE4M6GNZQ7YD/'} | null |
PyPI | GHSA-x88j-93vc-wpmp | Moderate severity vulnerability that affects django | django.contrib.sessions in Django before 1.2.7 and 1.3.x before 1.3.1, when session data is stored in the cache, uses the root namespace for both session identifiers and application-data keys, which allows remote attackers to modify a session by triggering use of a key that is equal to that session's identifier. | {'CVE-2011-4136'} | 2022-03-03T05:13:22.530706Z | 2018-07-23T19:52:39Z | MODERATE | null | {'CWE-20'} | {'https://github.com/django/django', 'https://www.djangoproject.com/weblog/2011/sep/09/', 'http://openwall.com/lists/oss-security/2011/09/13/2', 'https://www.djangoproject.com/weblog/2011/sep/10/127/', 'http://www.debian.org/security/2011/dsa-2332', 'http://secunia.com/advisories/46614', 'https://nvd.nist.gov/vuln/detail/CVE-2011-4136', 'http://openwall.com/lists/oss-security/2011/09/11/1', 'https://bugzilla.redhat.com/show_bug.cgi?id=737366', 'https://github.com/advisories/GHSA-x88j-93vc-wpmp', 'https://hermes.opensuse.org/messages/14700881'} | null |
PyPI | PYSEC-2022-104 | null | Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-v3f7-j968-4h5f', 'CVE-2022-21725'} | 2022-03-09T00:18:22.994300Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f', 'https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198'} | null |
PyPI | PYSEC-2021-418 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41226', 'GHSA-374m-jm66-3vj8'} | 2021-11-13T06:52:46.070716Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8'} | null |
PyPI | GHSA-mh74-4m5g-fcjx | Malicious users could control the content of invitation emails | ### Impact
A malicious user could abuse Sydent to send out arbitrary emails from the Sydent email address. This could be used to construct plausible phishing emails, for example.
### Patches
Fixed in 4469d1d, 6b405a8, 65a6e91.
Note that these patches include changes to the *default* email templates. If these templates have been locally modified, they must also be updated.
### For more information
If you have any questions or comments about this advisory, email us at security@matrix.org. | {'CVE-2021-29432'} | 2022-03-03T05:12:55.741149Z | 2021-04-19T14:54:24Z | MODERATE | null | {'CWE-20'} | {'https://github.com/matrix-org/sydent/security/advisories/GHSA-mh74-4m5g-fcjx', 'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29432', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0', 'https://pypi.org/project/matrix-sydent/'} | null |
PyPI | GHSA-cfx7-2xpc-8w4h | Division by zero in TFLite's implementation of `BatchToSpaceNd` | ### Impact
The implementation of the `BatchToSpaceNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82):
```cc
TF_LITE_ENSURE_EQ(context, output_batch_size % block_shape[dim], 0);
output_batch_size = output_batch_size / block_shape[dim];
```
An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0.
### Patches
We have patched the issue in GitHub commit [2c74674348a4708ced58ad6eb1b23354df8ee044](https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29593'} | 2022-03-03T05:12:44.546198Z | 2021-05-21T14:27:01Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29593', 'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfx7-2xpc-8w4h'} | null |
PyPI | GHSA-89v2-g37m-g3ff | Improper Verification of Cryptographic Signature in aws-encryption-sdk-cli | ### Impact
This advisory addresses several LOW severity issues with streaming signed messages and restricting processing of certain types of invalid messages.
This ESDK supports a streaming mode where callers may stream the plaintext of signed messages before the ECDSA signature is validated. In addition to these signatures, the ESDK uses AES-GCM encryption and all plaintext is verified before being released to a caller. There is no impact on the integrity of the ciphertext or decrypted plaintext, however some callers may rely on the the ECDSA signature for non-repudiation. Without validating the ECDSA signature, an actor with trusted KMS permissions to decrypt a message may also be able to encrypt messages. This update introduces a new API for callers who wish to stream only unsigned messages.
For customers who process ESDK messages from untrusted sources, this update also introduces a new configuration to limit the number of Encrypted Data Keys (EDKs) that the ESDK will attempt to process per message. This configuration provides customers with a way to limit the number of AWS KMS Decrypt API calls that the ESDK will make per message. This setting will reject messages with more EDKs than the configured limit.
Finally, this update adds early rejection of invalid messages with certain invalid combinations of algorithm suite and header data.
### Patches
Fixed in versions 1.9 and 2.2. We recommend that all users upgrade to address these issues.
Customers leveraging the ESDK’s streaming features have several options to protect signature validation. One is to ensure that client code reads to the end of the stream before using released plaintext. With this release, using the new API for streaming and falling back to the non-streaming decrypt API for signed messages prevents using any plaintext from signed data before the signature is validated. See https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/about-versions.html#version2.2.x
Users processing ESDK messages from untrusted sources should use the new maximum encrypted data keys parameter. See https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/about-versions.html#version2.2.x
### Workarounds
None
### For more information
https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/concepts.html#digital-sigs
https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/about-versions.html#version2.2.x
| null | 2022-03-03T05:14:16.077268Z | 2021-06-01T21:18:53Z | MODERATE | null | {'CWE-347'} | {'https://github.com/aws/aws-encryption-sdk-cli/security/advisories/GHSA-89v2-g37m-g3ff'} | null |
PyPI | PYSEC-2020-273 | null | In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15193', 'GHSA-rjjg-hgv6-h69v'} | 2021-12-09T06:34:40.985674Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | GHSA-mj63-64x7-57xf | Path traversal in impacket | Multiple path traversal vulnerabilities exist in smbserver.py in Impacket before 0.9.23. An attacker that connects to a running smbserver instance can list and write to arbitrary files via ../ directory traversal. This could potentially be abused to achieve arbitrary code execution by replacing /etc/shadow or an SSH authorized key. | {'CVE-2021-31800'} | 2022-03-03T05:12:59.880283Z | 2021-06-18T18:43:14Z | CRITICAL | null | {'CWE-22'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/IPXDPWCAPVX3UWYZ3N2T5OLBSBBUHJP6/', 'https://github.com/SecureAuthCorp/impacket/releases', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L3485', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/UF56LYB27LHEIFJTFHU3M75NMNNK2SCG/', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L876', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L2958', 'https://nvd.nist.gov/vuln/detail/CVE-2021-31800', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/KRV2C5DATXBHG6TF6CEEX54KZ75THQS3/', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L2008', 'https://github.com/SecureAuthCorp/impacket/commit/99bd29e3995c254e2d6f6c2e3454e4271665955a', 'https://github.com/SecureAuthCorp/impacket/commit/49c643bf66620646884ed141c94e5fdd85bcdd2f', 'https://github.com/SecureAuthCorp/impacket/releases/tag/impacket_0_9_23'} | null |
PyPI | PYSEC-2021-273 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-hpv4-7p9c-mvfr', 'CVE-2021-37651'} | 2021-08-27T03:22:44.051773Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr', 'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} | null |
PyPI | PYSEC-2021-789 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37678', 'GHSA-r6jx-9g48-2r5r'} | 2021-12-09T06:35:39.175638Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r'} | null |
PyPI | PYSEC-2021-623 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` has an undefined behavior due to binding a reference to `nullptr`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-vwhq-49r4-gj9v', 'CVE-2021-41214'} | 2021-12-09T06:35:09.506027Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v'} | null |
PyPI | GHSA-65rm-h285-5cc5 | Improper Certificate Validation in Twisted | In words.protocols.jabber.xmlstream in Twisted through 19.2.1, XMPP support did not verify certificates when used with TLS, allowing an attacker to MITM connections. | {'CVE-2019-12855'} | 2022-03-03T05:14:11.620119Z | 2019-08-16T14:02:35Z | HIGH | null | {'CWE-295'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-12855', 'https://twistedmatrix.com/trac/ticket/9561', 'https://github.com/twisted/twisted/pull/1147'} | null |
PyPI | PYSEC-2021-83 | null | Plone though 5.2.4 allows SSRF via the lxml parser. This affects Diazo themes, Dexterity TTW schemas, and modeleditors in plone.app.theming, plone.app.dexterity, and plone.supermodel. | {'CVE-2021-33511', 'GHSA-gc9g-67cq-p7v4'} | 2021-06-02T03:48:05.392145Z | 2021-05-21T22:15:00Z | null | null | null | {'https://plone.org/security/hotfix/20210518/server-side-request-forgery-via-lxml-parser', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'https://github.com/advisories/GHSA-gc9g-67cq-p7v4'} | null |
PyPI | PYSEC-2021-336 | null | Unrestricted Upload of File with Dangerous Type in Django-Widgy v0.8.4 allows remote attackers to execute arbitrary code via the 'image' widget in the component 'Change Widgy Page'. | {'CVE-2020-18704', 'GHSA-98hv-qff3-8793'} | 2021-09-26T23:32:19.117278Z | 2021-08-16T18:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-98hv-qff3-8793', 'https://github.com/fusionbox/django-widgy/issues/387'} | null |
PyPI | PYSEC-2021-766 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-7fvx-3jfc-2cpc', 'CVE-2021-37655'} | 2021-12-09T06:35:37.088195Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7fvx-3jfc-2cpc', 'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f'} | null |
PyPI | PYSEC-2015-26 | null | Cross-site scripting (XSS) vulnerability in the file browser in notebook/notebookapp.py in IPython Notebook before 3.2.2 and Jupyter Notebook 4.0.x before 4.0.5 allows remote attackers to inject arbitrary web script or HTML via a folder name. NOTE: this was originally reported as a cross-site request forgery (CSRF) vulnerability, but this may be inaccurate. | {'CVE-2015-6938'} | 2021-07-15T02:22:16.172109Z | 2015-09-21T19:59:00Z | null | null | null | {'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/167670.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/166471.html', 'https://github.com/jupyter/notebook/commit/dd9876381f0ef09873d8c5f6f2063269172331e3', 'http://seclists.org/oss-sec/2015/q3/474', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/166460.html', 'http://seclists.org/oss-sec/2015/q3/544', 'http://lists.opensuse.org/opensuse-updates/2015-10/msg00016.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=1259405', 'https://github.com/jupyter/notebook/commit/35f32dd2da804d108a3a3585b69ec3295b2677ed', 'https://github.com/ipython/ipython/commit/3ab41641cf6fce3860c73d5cf4645aa12e1e5892'} | null |
PyPI | PYSEC-2020-336 | null | In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26270', 'GHSA-m648-33qf-v3gp'} | 2021-12-09T06:35:16.648712Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m648-33qf-v3gp'} | null |
PyPI | PYSEC-2019-235 | null | NULL pointer dereference in Google TensorFlow before 1.12.2 could cause a denial of service via an invalid GIF file. | {'CVE-2019-9635'} | 2021-12-09T06:35:11.945126Z | 2019-04-24T17:29:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-001.md'} | null |
PyPI | GHSA-c5x2-p679-95wc | Null pointer dereference in `SparseTensorSliceDataset` | ### Impact
When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer:
```python
import tensorflow as tf
tf.raw_ops.SparseTensorSliceDataset(
indices=[[],[],[]],
values=[1,2,3],
dense_shape=[3,3])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not.
If `indices` is empty (as in the example above), then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference:
```cc
for (int64_t i = 0; i < indices->dim_size(0); ++i) {
int64_t next_batch_index = indices->matrix<int64>()(i, 0);
...
}
```
If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector).
### Patches
We have patched the issue in GitHub commit [02cc160e29d20631de3859c6653184e3f876b9d7](https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37647'} | 2022-03-03T05:13:33.785331Z | 2021-08-25T14:43:32Z | HIGH | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c5x2-p679-95wc', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37647', 'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} | null |
PyPI | GHSA-fxq4-r6mr-9x64 | CSRF Vuln can expose user's QRcode | ### Impact
When a user is setting up two-factor authentication using an authenticator app, a QRcode is generated and made available via a GET request to /tf-qrcode. Since GETs do not have any CSRF protection, it is possible a malicious 3rd party could access the QRcode and therefore gain access to two-factor authentication codes. Note that the /tf-qrcode endpoint is ONLY accessible while the user is initially setting up their device. Once setup is complete, there is no vulnerability.
### Patches
This is fixed in the upcoming 4.0.0 release.
### Workarounds
You can provide your own URL for fetching the QRcode by defining SECURITY_TWO_FACTOR_QRCODE_URL and providing your own implementation (that presumably required a POST with CSRF protection). This would require changing the two-factor setup template as well.
### References
None.
### For more information
If you have any questions or comments about this advisory:
* Read this pull request: #423 | null | 2022-03-03T05:13:06.509111Z | 2021-04-08T16:46:00Z | LOW | null | {'CWE-352'} | {'https://pypi.org/project/Flask-Security-Too', 'https://github.com/Flask-Middleware/flask-security/security/advisories/GHSA-fxq4-r6mr-9x64'} | null |
PyPI | GHSA-29vr-79w7-p649 | Incorrect Authorization in Gerapy | An Access Control vunerabiity exists in Gerapy v 0.9.7 via the spider parameter in project_configure function. | {'CVE-2021-44597'} | 2022-03-28T14:30:49.779636Z | 2022-03-11T00:02:00Z | CRITICAL | null | {'CWE-863'} | {'https://github.com/Gerapy/Gerapy/releases/tag/v0.9.8', 'https://nvd.nist.gov/vuln/detail/CVE-2021-44597', 'https://github.com/Gerapy/Gerapy/issues/219'} | null |
PyPI | PYSEC-2022-153 | null | Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23589', 'GHSA-9px9-73fg-3fqp'} | 2022-03-09T00:18:29.733275Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9px9-73fg-3fqp', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L3466-L3497', 'https://github.com/tensorflow/tensorflow/commit/0a365c029e437be0349c31f8d4c9926b69fa3fa1', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/mutable_graph_view.cc#L59-L74', 'https://github.com/tensorflow/tensorflow/commit/045deec1cbdebb27d817008ad5df94d96a08b1bf'} | null |
PyPI | PYSEC-2012-9 | null | Multiple SQL injection vulnerabilities in SQLAlchemy before 0.7.0b4, as used in Keystone, allow remote attackers to execute arbitrary SQL commands via the (1) limit or (2) offset keyword to the select function, or unspecified vectors to the (3) select.limit or (4) select.offset function. | {'CVE-2012-0805'} | 2021-07-15T02:22:20.378135Z | 2012-06-05T22:55:00Z | null | null | null | {'http://www.sqlalchemy.org/changelog/CHANGES_0_7_0', 'http://www.sqlalchemy.org/trac/changeset/852b6a1a87e7/', 'http://www.mandriva.com/security/advisories?name=MDVSA-2012:059', 'http://secunia.com/advisories/48771', 'http://secunia.com/advisories/48328', 'https://bugs.launchpad.net/keystone/+bug/918608', 'http://secunia.com/advisories/48327', 'http://www.debian.org/security/2012/dsa-2449', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/73756', 'http://rhn.redhat.com/errata/RHSA-2012-0369.html'} | null |
PyPI | PYSEC-2020-41 | null | In EasyBuild before version 4.1.2, the GitHub Personal Access Token (PAT) used by EasyBuild for the GitHub integration features (like `--new-pr`, `--fro,-pr`, etc.) is shown in plain text in EasyBuild debug log files. This issue is fixed in EasyBuild v4.1.2, and in the `master`+ `develop` branches of the `easybuild-framework` repository. | {'CVE-2020-5262', 'GHSA-2wx6-wc87-rmjm'} | 2020-03-23T18:15:00Z | 2020-03-19T17:15:00Z | null | null | null | {'https://github.com/easybuilders/easybuild-framework/security/advisories/GHSA-2wx6-wc87-rmjm', 'https://github.com/easybuilders/easybuild-framework/pull/3248', 'https://github.com/easybuilders/easybuild-framework/pull/3249'} | null |
PyPI | PYSEC-2018-28 | null | The Requests package before 2.20.0 for Python sends an HTTP Authorization header to an http URI upon receiving a same-hostname https-to-http redirect, which makes it easier for remote attackers to discover credentials by sniffing the network. | {'GHSA-x84v-xcm2-53pg', 'CVE-2018-18074'} | 2021-06-16T00:03:24.800813Z | 2018-10-09T17:29:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2019-07/msg00024.html', 'https://github.com/requests/requests/pull/4718', 'https://bugs.debian.org/910766', 'https://github.com/advisories/GHSA-x84v-xcm2-53pg', 'https://github.com/requests/requests/issues/4716', 'http://docs.python-requests.org/en/master/community/updates/#release-and-version-history', 'https://access.redhat.com/errata/RHSA-2019:2035', 'https://github.com/requests/requests/commit/c45d7c49ea75133e52ab22a8e9e13173938e36ff', 'https://usn.ubuntu.com/3790-1/', 'https://usn.ubuntu.com/3790-2/'} | null |
PyPI | GHSA-p867-fxfr-ph2w | b2-sdk-python TOCTOU application key disclosure | ### Impact
Linux and Mac releases of the SDK version 1.14.0 and below contain a key disclosure vulnerability that, in certain conditions, can be exploited by local attackers through a time-of-check-time-of-use (TOCTOU) race condition.
SDK users of the `SqliteAccountInfo` format are vulnerable while users of the `InMemoryAccountInfo` format are safe. The `SqliteAccountInfo` saves API keys (and bucket name-to-id mapping) in a local database file (`$XDG_CONFIG_HOME/b2/account_info`, `~/.b2_account_info` or a user-defined path). When first created, the file is world readable and is (typically a few milliseconds) later altered to be private to the user. If the directory containing the file is readable by a local attacker then during the brief period between file creation and permission modification, a local attacker can race to open the file and maintain a handle to it. This allows the local attacker to read the contents after the file after the sensitive information has been saved to it.
Consumers of this SDK who rely on it to save data using `SqliteAccountInfo` class should upgrade to the latest version of the SDK. Those who believe a local user might have opened a handle using this race condition, should remove the affected database files and regenerate all application keys.
### Patches
Users should upgrade to b2-sdk-python 1.14.1 or later.
### For more information
See the related advisory in the [B2 Command Line Tool](https://github.com/Backblaze/B2_Command_Line_Tool), a consumer of this SDK.
If you have any questions or comments about this advisory:
* Open an issue in [b2-sdk-python](https://github.com/Backblaze/b2-sdk-python)
* Email us at [security@backblaze.com](mailto:security@backblaze.com)
| {'CVE-2022-23651'} | 2022-03-08T18:31:44.904661Z | 2022-02-24T12:08:24Z | MODERATE | null | {'CWE-367'} | {'https://github.com/Backblaze/b2-sdk-python', 'https://github.com/Backblaze/b2-sdk-python/commit/62476638986e5b6d7459aca5ef8ce220760226e0', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23651', 'https://github.com/Backblaze/b2-sdk-python/security/advisories/GHSA-p867-fxfr-ph2w', 'https://pypi.org/project/b2sdk/'} | null |
PyPI | PYSEC-2022-93 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-24x4-6qmh-88qg', 'CVE-2022-23584'} | 2022-03-09T00:17:35.438139Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L339-L346', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-24x4-6qmh-88qg', 'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b'} | null |
PyPI | PYSEC-2021-859 | null | NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Versions prior to 3.6.5 are vulnerable to regular expression denial of service (ReDoS) attacks. The vulnerability is present in PunktSentenceTokenizer, sent_tokenize and word_tokenize. Any users of this class, or these two functions, are vulnerable to the ReDoS attack. In short, a specifically crafted long input to any of these vulnerable functions will cause them to take a significant amount of execution time. If your program relies on any of the vulnerable functions for tokenizing unpredictable user input, then we would strongly recommend upgrading to a version of NLTK without the vulnerability. For users unable to upgrade the execution time can be bounded by limiting the maximum length of an input to any of the vulnerable functions. Our recommendation is to implement such a limit. | {'CVE-2021-43854', 'GHSA-f8m6-h2c7-8h9x'} | 2022-01-04T17:38:55.854845Z | 2021-12-23T18:15:00Z | null | null | null | {'https://github.com/nltk/nltk/issues/2866', 'https://github.com/nltk/nltk/pull/2869', 'https://github.com/nltk/nltk/commit/1405aad979c6b8080dbbc8e0858f89b2e3690341', 'https://github.com/nltk/nltk/security/advisories/GHSA-f8m6-h2c7-8h9x'} | null |
PyPI | GHSA-67j9-c52g-w2q9 | Authorization Bypass in I hate money | ### Impact
An authenticated member of one project can modify and delete members of another project, without knowledge of this other project's private code. This can be further exploited to access all bills of another project without knowledge of this other project's private code.
With the default configuration, anybody is allowed to create a new project. An attacker can create a new project and then use it to become authenticated and exploit this flaw. As such, the exposure is similar to an unauthenticated attack, because it is trivial to become authenticated.
### Patches
```diff
ihatemoney/models.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/ihatemoney/models.py b/ihatemoney/models.py
index fe7b519..5691c75 100644
--- a/ihatemoney/models.py
+++ b/ihatemoney/models.py
@@ -380,7 +380,7 @@ class Person(db.Model):
def get_by_name(self, name, project):
return (
Person.query.filter(Person.name == name)
- .filter(Project.id == project.id)
+ .filter(Person.project_id == project.id)
.one()
)
@@ -389,7 +389,7 @@ class Person(db.Model):
project = g.project
return (
Person.query.filter(Person.id == id)
- .filter(Project.id == project.id)
+ .filter(Person.project_id == project.id)
.one()
)
```
### Workarounds
To limit the impact, it is possible to disable public project creation by setting `ALLOW_PUBLIC_PROJECT_CREATION = False` in the configuration (see [documentation](https://ihatemoney.readthedocs.io/en/latest/configuration.html)). Existing users will still be able to exploit the flaw, but this will prevent an external attacker from creating a new project.
### For more information
`Person.query.get()` and `Person.query.get_by_name()` were mistakenly running a database join on the Project table without constraining the result.
As a result, `Person.query.get(42, "projectfoo")` would return the Person with id=42, even if it is not associated to the project "projectfoo". The only condition is that "projectfoo" must exist.
This flaw can be exploited in several places:
1) API: PUT requests to `/api/projects/<project>/members/<personID>` will succeed even though `<personID>` is not a member of `<project>`.
This allows an authenticated attacker to alter the state of a member (name, weight, activated) in any project. In addition, the altered member will no longer be associated with its original project but will be associated to the attacker project instead, breaking many features of IHateMoney. For instance, bills referencing the altered member will no longer be visible in the original project.
This causes an additional information disclosure and loss of integrity on bills: the attacker will now be able to see, edit and delete bills belonging to the altered member, because IHateMoney now believes that these bills are associated to the attacker project through the altered member.
For instance, assume that `Person(id=42)` is a member of project "targetProject", and that the attacker has access to another project "attackerProject" with the private code "attackerPassword". The attacker can modify `Person(id=42)` with this command:
$ curl -X PUT -d "name=Pwn3d&activated=1" --basic -u attackerProject:attackerPassword http://$SERVER/api/projects/attackerProject/members/42
The attacker can now see, edit and delete bills paid by `Person(id=42)` by simply browsing to http://$SERVER/attackerProject/
2) Editing a member through the web interface at `/<project>/members/<personID>/edit` will succeed even though `<personID>` is not a member of `<project>`.
This is very similar to the PUT exploit. Reusing the same example, the attacker needs to login to its "attackerProject" project with the private code "attackerPassword". It can then alter the state of `Person(id=42)` by accessing the edit form at the following URL:
http://$SERVER/attackerProject/members/42/edit
Again, as a result of the alteration, the altered member will become associated to the project "attackerProject", resulting in the same information disclosure and loss of integrity on bills.
3) API: DELETE requests to `/api/projects/<project>/members/<personID>` will similarly allow to delete the member `<personID>` even if it belongs to a different project than `<project>`.
$ curl -X DELETE --basic -u attackerProject:attackerPassword http://$SERVER/api/projects/attackerProject/members/42
The impact is less serious than with PUT, because DELETE only deactivates a member (it does not really delete it).
All these exploits require authentication: an attacker needs to know a valid project name and its associated "private code". Once this requirement is fullfilled, the attacker can exploit this flaw to alter the state of members in any other project, without needing to know the target project name or its private code.
`Person.query.get_by_name()` suffers from the same issue as `Person.query.get()`. It has an additional issue: if multiple Person objects with the same name exist (this is possible if they are associated to different projects), `get_by_name()` will crash with `MultipleResultsFound` because of the call to `one()`.
However, since `Person.query.get_by_name()` is currently not used anywhere in IHateMoney, the bug affecting this function has no impact and is not exploitable. | {'CVE-2020-15120'} | 2022-03-03T05:12:57.326270Z | 2020-07-27T17:47:52Z | MODERATE | null | {'CWE-863'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15120', 'https://github.com/spiral-project/ihatemoney/security/advisories/GHSA-67j9-c52g-w2q9', 'https://github.com/spiral-project/ihatemoney/commit/8d77cf5d5646e1d2d8ded13f0660638f57e98471'} | null |
PyPI | PYSEC-2016-36 | null | The multifilesystem storage backend in Radicale before 1.1 allows remote attackers to read or write to arbitrary files via a crafted component name. | {'CVE-2015-8747'} | 2021-12-14T08:18:58.605498Z | 2016-02-03T18:59:00Z | null | null | null | {'https://github.com/Kozea/Radicale/pull/343', 'http://www.securityfocus.com/bid/80255', 'https://github.com/Unrud/Radicale/commit/bcaf452e516c02c9bed584a73736431c5e8831f1', 'http://www.openwall.com/lists/oss-security/2016/01/06/4', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175776.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175738.html', 'https://pypi.org/project/radicale', 'https://nvd.nist.gov/vuln/detail/CVE-2015-8747', 'http://www.debian.org/security/2016/dsa-3462', 'http://www.openwall.com/lists/oss-security/2016/01/05/7', 'http://www.openwall.com/lists/oss-security/2016/01/06/7'} | null |
PyPI | PYSEC-2021-635 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41226', 'GHSA-374m-jm66-3vj8'} | 2021-12-09T06:35:11.266312Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8'} | null |
PyPI | GHSA-h29c-wcm8-883h | Incorrect Permission Assignment for Critical Resource in OnionShare | Between September 26, 2021 and October 8, 2021, [Radically Open Security](https://www.radicallyopensecurity.com/) conducted a penetration test of OnionShare 2.4, funded by the Open Technology Fund's [Red Team lab](https://www.opentech.fund/labs/red-team-lab/). This is an issue from that penetration test.
- Vulnerability ID: OTF-006
- Vulnerability type: Broken Website Hardening Control
- Threat level: Low
## Description:
The CSP can be turned on or off but not configured for the specific needs of the website.
## Technical description:
The website mode of the application allows to use a hardened CSP, which will block any scripts and external resources. It is not possible to configure this CSP for individual pages and therefore the security enhancement cannot be used for websites using javascript or external resources like fonts or images.
If CSP were configurable, the website creator could harden it accordingly to the needs of the application.
As this issue correlates with the Github issue for exposing the flask application directly (https://github.com/onionshare/ onionshare/issues/1389), it can be assumed that this can be solved by either changing to a well-known webserver, which supports this kind of configuration, or enhancing the status quo by making the CSP a configurable part of each website.
We believe that bundling the nginx or apache webserver would add complexity and dependencies to the application that could result in a larger attack surface - as these packages receive regular security updates. On the other hand it is not recommended to directly expose the flask webserver, due to lack of hardening. This is a trade-off which needs to be evaluated by the Onionshare developers, as multiple features are involved. Ideally the application user could choose between the built-in flask webserver or a system webserver of choice.
## Impact:
As this is a general weakness and not a direct vulnerability in the Onionshare application, the direct impact of this issue is rather low.
## Recommendation:
- Consider offering a configurable webserver choice
- Consider configurable CSP | {'CVE-2022-21694'} | 2022-03-03T05:12:57.237760Z | 2022-01-21T23:20:17Z | LOW | null | {'CWE-732'} | {'https://github.com/onionshare/onionshare/issues/1389', 'https://github.com/onionshare/onionshare/security/advisories/GHSA-h29c-wcm8-883h', 'https://github.com/onionshare/onionshare/releases/tag/v2.5', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21694', 'https://github.com/onionshare/onionshare'} | null |
PyPI | PYSEC-2021-265 | null | TensorFlow is an end-to-end open source platform for machine learning. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37643', 'GHSA-fcwc-p4fc-c5cc'} | 2021-08-27T03:22:43.365129Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fcwc-p4fc-c5cc'} | null |
PyPI | GHSA-m2c7-42rf-c62f | Unrestricted Upload of File with Dangerous Type in motionEye | motionEye <= 0.42.1 and motioneEyeOS <= 20200606 allow a remote attacker to upload a configuration backup file containing a malicious python pickle file. This is possible when an installation is accessible over the Internet and uses no or poor authentication credentials.
The GitHub repositories for motionEye and motionEyeOS are no longer being actively maintained as of January 2022, so release of a patched version is unlikely. Keeping a motionEye or motionEyeOS installation off of the Internet and/or using strong credentials provide protection against this issue. | {'CVE-2021-44255'} | 2022-03-03T05:13:25.030756Z | 2022-02-01T00:00:44Z | HIGH | null | {'CWE-434'} | {'https://www.pizzapower.me/2021/10/09/self-hosted-security-part-1-motioneye/', 'https://github.com/ccrisan/motioneye', 'https://github.com/ccrisan/motioneyeos/issues/2843', 'https://nvd.nist.gov/vuln/detail/CVE-2021-44255'} | null |
PyPI | PYSEC-2019-223 | null | Google TensorFlow 1.7.x and earlier is affected by a Buffer Overflow vulnerability. The type of exploitation is context-dependent. | {'CVE-2018-7575'} | 2021-08-27T03:22:22.242054Z | 2019-04-24T21:29:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-004.md'} | null |
PyPI | PYSEC-2020-320 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15205', 'GHSA-g7p5-5759-qv46'} | 2021-12-09T06:35:14.101977Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46', 'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | PYSEC-2015-30 | null | The s3_token middleware in OpenStack keystonemiddleware before 1.6.0 and python-keystoneclient before 1.4.0 disables certification verification when the "insecure" option is set in a paste configuration (paste.ini) file regardless of the value, which allows remote attackers to conduct man-in-the-middle attacks via a crafted certificate, a different vulnerability than CVE-2014-7144. | {'CVE-2015-1852'} | 2021-07-25T23:34:39.021539Z | 2015-04-17T17:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/74187', 'http://lists.openstack.org/pipermail/openstack-announce/2015-April/000350.html', 'https://bugs.launchpad.net/keystonemiddleware/+bug/1411063', 'http://www.ubuntu.com/usn/USN-2705-1', 'http://www.oracle.com/technetwork/topics/security/bulletinapr2015-2511959.html', 'http://rhn.redhat.com/errata/RHSA-2015-1685.html', 'http://rhn.redhat.com/errata/RHSA-2015-1677.html'} | null |
PyPI | PYSEC-2021-770 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37659', 'GHSA-q3g3-h9r4-prrc'} | 2021-12-09T06:35:37.426472Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q3g3-h9r4-prrc', 'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} | null |
PyPI | GHSA-jgpv-4h4c-xhw3 | Uncontrolled Resource Consumption in pillow | ### Impact
_Pillow before 8.1.1 allows attackers to cause a denial of service (memory consumption) because the reported size of a contained image is not properly checked for a BLP container, and thus an attempted memory allocation can be very large._
### Patches
_An issue was discovered in Pillow before 6.2.0. When reading specially crafted invalid image files, the library can either allocate very large amounts of memory or take an extremely long period of time to process the image._
### Workarounds
_An issue was discovered in Pillow before 6.2.0. When reading specially crafted invalid image files, the library can either allocate very large amounts of memory or take an extremely long period of time to process the image._
### References
https://nvd.nist.gov/vuln/detail/CVE-2021-27921
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [example link to repo](http://example.com)
* Email us at [example email address](mailto:example@example.com) | null | 2022-03-03T05:14:13.271916Z | 2021-04-23T16:54:36Z | MODERATE | null | {'CWE-400'} | {'https://github.com/calix2/pyVulApp/security/advisories/GHSA-jgpv-4h4c-xhw3'} | null |
PyPI | PYSEC-2019-13 | null | An issue was discovered in Django 1.11.x before 1.11.23, 2.1.x before 2.1.11, and 2.2.x before 2.2.4. Due to an error in shallow key transformation, key and index lookups for django.contrib.postgres.fields.JSONField, and key lookups for django.contrib.postgres.fields.HStoreField, were subject to SQL injection. This could, for example, be exploited via crafted use of "OR 1=1" in a key or index name to return all records, using a suitably crafted dictionary, with dictionary expansion, as the **kwargs passed to the QuerySet.filter() function. | {'CVE-2019-14234', 'GHSA-6r97-cj55-9hrq'} | 2019-08-28T13:15:00Z | 2019-08-09T13:15:00Z | null | null | null | {'https://seclists.org/bugtraq/2019/Aug/15', 'https://groups.google.com/forum/#!topic/django-announce/jIoju2-KLDs', 'https://www.debian.org/security/2019/dsa-4498', 'https://security.netapp.com/advisory/ntap-20190828-0002/', 'https://security.gentoo.org/glsa/202004-17', 'https://github.com/advisories/GHSA-6r97-cj55-9hrq', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00025.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/STVX7X7IDWAH5SKE6MBMY3TEI6ZODBTK/', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://www.djangoproject.com/weblog/2019/aug/01/security-releases/'} | null |
PyPI | PYSEC-2021-320 | null | Wasmtime is an open source runtime for WebAssembly & WASI. In Wasmtime from version 0.19.0 and before version 0.30.0 there was a use-after-free bug when passing `externref`s from the host to guest Wasm content. To trigger the bug, you have to explicitly pass multiple `externref`s from the host to a Wasm instance at the same time, either by passing multiple `externref`s as arguments from host code to a Wasm function, or returning multiple `externref`s to Wasm from a multi-value return function defined in the host. If you do not have host code that matches one of these shapes, then you are not impacted. If Wasmtime's `VMExternRefActivationsTable` became filled to capacity after passing the first `externref` in, then passing in the second `externref` could trigger a garbage collection. However the first `externref` is not rooted until we pass control to Wasm, and therefore could be reclaimed by the collector if nothing else was holding a reference to it or otherwise keeping it alive. Then, when control was passed to Wasm after the garbage collection, Wasm could use the first `externref`, which at this point has already been freed. We have reason to believe that the effective impact of this bug is relatively small because usage of `externref` is currently quite rare. The bug has been fixed, and users should upgrade to Wasmtime 0.30.0. If you cannot upgrade Wasmtime yet, you can avoid the bug by disabling reference types support in Wasmtime by passing `false` to `wasmtime::Config::wasm_reference_types`. | {'GHSA-v4cp-h94r-m7xf', 'CVE-2021-39216'} | 2021-09-17T22:30:49.852358Z | 2021-09-17T20:15:00Z | null | null | null | {'https://crates.io/crates/wasmtime', 'https://github.com/bytecodealliance/wasmtime/security/advisories/GHSA-v4cp-h94r-m7xf', 'https://github.com/bytecodealliance/wasmtime/commit/101998733b74624cbd348a2366d05760b40181f3'} | null |
PyPI | GHSA-5fq8-3q2f-4m5g | Session key exposure through session list in Django User Sessions | ### Impact
The views provided by django-user-sessions allow users to terminate specific sessions. The session key is used to identify sessions, and thus included in the rendered HTML. In itself this is not a problem. However if the website has an XSS vulnerability, the session key could be extracted by the attacker and a session takeover could happen.
### Patches
Patch is under way.
### Workarounds
Remove the session_key from the template.
### References
_None._
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Bouke/django-user-sessions](https://github.com/Bouke/django-user-sessions/issues)
* Email us at [bouke@haarsma.eu](mailto:bouke@haarsma.eu) | {'CVE-2020-5224'} | 2022-03-03T05:13:30.020312Z | 2020-01-24T19:56:59Z | LOW | null | {'CWE-287'} | {'https://github.com/Bouke/django-user-sessions/security/advisories/GHSA-5fq8-3q2f-4m5g', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5224', 'https://github.com/jazzband/django-user-sessions/commit/f0c4077e7d1436ba6d721af85cee89222ca5d2d9'} | null |
PyPI | PYSEC-2021-95 | null | The aaugustin websockets library before 9.1 for Python has an Observable Timing Discrepancy on servers when HTTP Basic Authentication is enabled with basic_auth_protocol_factory(credentials=...). An attacker may be able to guess a password via a timing attack. | {'CVE-2021-33880', 'GHSA-8ch4-58qp-g3mp'} | 2021-06-09T05:01:36.173811Z | 2021-06-06T15:15:00Z | null | null | null | {'https://github.com/aaugustin/websockets/commit/547a26b685d08cac0aa64e5e65f7867ac0ea9bc0', 'https://github.com/advisories/GHSA-8ch4-58qp-g3mp'} | null |
PyPI | GHSA-jjr8-m8g8-p6wv | Null pointer dereference in TFLite's `Reshape` operator | ### Impact
The fix for [CVE-2020-15209](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the [fix for the vulnerability](https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape:
```cc
if (tensor->data.raw == nullptr && tensor->bytes > 0) {
if (registration.builtin_code == kTfLiteBuiltinReshape && i == 1) {
// In general, having a tensor here with no buffer will be an error.
// However, for the reshape operator, the second input tensor is only
// used for the shape, not for the data. Thus, null buffer is ok.
continue;
} else {
// In all other cases, we need to return an error as otherwise we will
// trigger a null pointer dereference (likely).
ReportError("Input tensor %d lacks data", tensor_index);
return kTfLiteError;
}
}
```
### Patches
We have patched the issue in GitHub commit [f8378920345f4f4604202d4ab15ef64b2aceaa16](https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29592'} | 2022-03-03T05:13:19.001075Z | 2021-05-21T14:26:58Z | MODERATE | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jjr8-m8g8-p6wv', 'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29592'} | null |
PyPI | GHSA-f78g-q7r4-9wcv | Division by 0 in `FractionalAvgPool` | ### Impact
An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`:
```python
import tensorflow as tf
value = tf.constant([60], shape=[1, 1, 1, 1], dtype=tf.int32)
pooling_ratio = [1.0, 1.0000014345305555, 1.0, 1.0]
pseudo_random = False
overlapping = False
deterministic = False
seed = 0
seed2 = 0
tf.raw_ops.FractionalAvgPool(
value=value, pooling_ratio=pooling_ratio, pseudo_random=pseudo_random,
overlapping=overlapping, deterministic=deterministic, seed=seed, seed2=seed2)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values:
```cc
for (int i = 0; i < tensor_in_and_out_dims; ++i) {
output_size[i] = static_cast<int>(std::floor(input_size[i] / pooling_ratio_[i]));
DCHECK_GT(output_size[i], 0);
}
```
The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger.
Later, these computed values [are used as arguments](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to [`GeneratePoolingSequence`](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation:
```cc
std::vector<int64> GeneratePoolingSequence(int input_length, int output_length,
GuardedPhiloxRandom* generator,
bool pseudo_random) {
...
if (input_length % output_length == 0) {
diff = std::vector<int64>(output_length, input_length / output_length);
}
...
}
```
Since `output_length` can be 0, this results in runtime crashing.
### Patches
We have patched the issue in GitHub commit [548b5eaf23685d86f722233d8fbc21d0a4aecb96](https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29550'} | 2022-03-03T05:13:33.593364Z | 2021-05-21T14:23:41Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29550'} | null |
PyPI | PYSEC-2022-145 | null | Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `IsSimplifiableReshape` would trigger `CHECK` failures. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23581', 'GHSA-fq86-3f29-px2c'} | 2022-03-09T00:18:28.561700Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1687-L1742'} | null |
PyPI | PYSEC-2021-459 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29531', 'GHSA-3qxp-qjq7-w4hf'} | 2021-12-09T06:34:48.199089Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qxp-qjq7-w4hf', 'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} | null |
PyPI | GHSA-9697-98pf-4rw7 | Heap OOB in `UpperBound` and `LowerBound` | ### Impact
An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`:
```python
import tensorflow as tf
tf.raw_ops.UpperBound(
sorted_input=[1,2,3],
values=tf.constant(value=[[0,0,0],[1,1,1],[2,2,2]],dtype=tf.int64),
out_type=tf.int64)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument:
```cc
void Compute(OpKernelContext* ctx) override {
const Tensor& sorted_inputs_t = ctx->input(0);
// ...
OP_REQUIRES(ctx, sorted_inputs_t.dim_size(0) == values_t.dim_size(0),
Status(error::INVALID_ARGUMENT,
"Leading dim_size of both tensors must match."));
// ...
if (output_t->dtype() == DT_INT32) {
OP_REQUIRES(ctx,
FastBoundsCheck(sorted_inputs_t.dim_size(1), ...));
// ...
}
```
As we access the first two dimensions of `sorted_inputs_t` tensor, it must have rank at least 2.
A similar issue occurs in `tf.raw_ops.LowerBound`.
### Patches
We have patched the issue in GitHub commit [42459e4273c2e47a3232cc16c4f4fff3b3a35c38](https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37670'} | 2022-03-03T05:14:12.056672Z | 2021-08-25T14:41:44Z | MODERATE | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37670', 'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} | null |
PyPI | PYSEC-2016-20 | null | The identity service in OpenStack Identity (Keystone) before 2015.1.3 (Kilo) and 8.0.x before 8.0.2 (Liberty) and keystonemiddleware (formerly python-keystoneclient) before 1.5.4 (Kilo) and Liberty before 2.3.3 does not properly invalidate authorization tokens when using the PKI or PKIZ token providers, which allows remote authenticated users to bypass intended access restrictions and gain access to cloud resources by manipulating byte fields within a revoked token. | {'CVE-2015-7546'} | 2021-07-25T23:34:39.104396Z | 2016-02-03T18:59:00Z | null | null | null | {'https://wiki.openstack.org/wiki/OSSN/OSSN-0062', 'https://bugs.launchpad.net/keystone/+bug/1490804', 'http://www.securityfocus.com/bid/80498', 'https://security.openstack.org/ossa/OSSA-2016-005.html', 'http://www.oracle.com/technetwork/topics/security/bulletinapr2016-2952098.html'} | null |
PyPI | GHSA-5qw5-89mw-wcg2 | Out of bounds write in Tensorflow | ### Impact
TensorFlow is vulnerable to a heap OOB write in [Grappler](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/graph_properties.cc#L1132-L1141):
```cc
Status SetUnknownShape(const NodeDef* node, int output_port) {
shape_inference::ShapeHandle shape =
GetUnknownOutputShape(node, output_port);
InferenceContext* ctx = GetContext(node);
if (ctx == nullptr) {
return errors::InvalidArgument("Missing context");
}
ctx->set_output(output_port, shape);
return Status::OK();
}
```
The [`set_output`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.h#L394) function writes to an array at the specified index:
```cc
void set_output(int idx, ShapeHandle shape) { outputs_.at(idx) = shape; }
```
Hence, this gives a malicious user a write primitive.
### Patches
We have patched the issue in GitHub commit [97282c6d0d34476b6ba033f961590b783fa184cd](https://github.com/tensorflow/tensorflow/commit/97282c6d0d34476b6ba033f961590b783fa184cd).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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. | {'CVE-2022-23566'} | 2022-03-03T05:12:32.668904Z | 2022-02-09T23:55:43Z | HIGH | null | {'CWE-787'} | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.h#L394', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/graph_properties.cc#L1132-L1141', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23566', 'https://github.com/tensorflow/tensorflow/commit/97282c6d0d34476b6ba033f961590b783fa184cd', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5qw5-89mw-wcg2'} | null |
PyPI | PYSEC-2022-85 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateOutputSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements. We can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-wm93-f238-7v37', 'CVE-2022-23576'} | 2022-03-09T00:17:34.416658Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1598-L1617', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wm93-f238-7v37', 'https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae'} | null |
PyPI | GHSA-r2mj-8wgq-73m6 | XML External Entity Reference in Glances | The package glances before 3.2.1 are vulnerable to XML External Entity (XXE) Injection via the use of Fault to parse untrusted XML data, which is known to be vulnerable to XML attacks.
| {'CVE-2021-23418'} | 2022-03-03T05:13:01.545369Z | 2021-08-09T20:43:14Z | MODERATE | null | {'CWE-611'} | {'https://snyk.io/vuln/SNYK-PYTHON-GLANCES-1311807', 'https://github.com/nicolargo/glances/issues/1025', 'https://github.com/nicolargo/glances', 'https://github.com/nicolargo/glances/commit/4b87e979afdc06d98ed1b48da31e69eaa3a9fb94', 'https://nvd.nist.gov/vuln/detail/CVE-2021-23418', 'https://github.com/nicolargo/glances/commit/85d5a6b4af31fcf785d5a61086cbbd166b40b07a', 'https://github.com/nicolargo/glances/commit/9d6051be4a42f692392049fdbfc85d5dfa458b32'} | null |
PyPI | PYSEC-2020-232 | null | In freewvs before 0.1.1, a user could create a large file that freewvs will try to read, which will terminate a scan process. This has been patched in 0.1.1. | {'GHSA-9cfv-9463-8gqv', 'CVE-2020-15100'} | 2021-08-27T03:22:04.199703Z | 2020-07-14T20:15:00Z | null | null | null | {'https://github.com/schokokeksorg/freewvs/commit/18bbf2043e53f69e0119d24f8ae4edb274afb9b2', 'https://github.com/schokokeksorg/freewvs/security/advisories/GHSA-9cfv-9463-8gqv'} | null |
PyPI | GHSA-c2jg-hw38-jrqq | Inconsistent Interpretation of HTTP Requests in twisted.web | The Twisted Web HTTP 1.1 server, located in the `twisted.web.http` module, parsed several HTTP request constructs more leniently than permitted by RFC 7230:
1. The Content-Length header value could have a `+` or `-` prefix.
2. Illegal characters were permitted in chunked extensions, such as the LF (`\n`) character.
3. Chunk lengths, which are expressed in hexadecimal format, could have a prefix of `0x`.
4. HTTP headers were stripped of all leading and trailing ASCII whitespace, rather than only space and HTAB (`\t`).
This non-conformant parsing can lead to desync if requests pass through multiple HTTP parsers, potentially resulting in HTTP request smuggling.
### Impact
You may be affected if:
1. You use Twisted Web's HTTP 1.1 server and/or proxy
2. You also pass requests through a different HTTP server and/or proxy
The specifics of the other HTTP parser matter. The original report notes that some versions of Apache Traffic Server and HAProxy have been vulnerable in the past. HTTP request smuggling may be a serious concern if you use a proxy to perform request validation or access control.
The Twisted Web client is not affected. The HTTP 2.0 server uses a different parser, so it is not affected.
### Patches
The issue has been addressed in Twisted 22.4.0rc1 and later.
### Workarounds
Other than upgrading Twisted, you could:
* Ensure any vulnerabilities in upstream proxies have been addressed, such as by upgrading them
* Filter malformed requests by other means, such as configuration of an upstream proxy
### Credits
This issue was initially reported by [Zhang Zeyu](https://github.com/zeyu2001). | {'CVE-2022-24801'} | 2022-05-04T04:03:05.300321Z | 2022-04-04T21:29:41Z | HIGH | null | {'CWE-444'} | {'https://github.com/twisted/twisted/releases/tag/twisted-22.4.0rc1', 'https://lists.debian.org/debian-lts-announce/2022/05/msg00003.html', 'https://github.com/twisted/twisted', 'https://github.com/twisted/twisted/commit/592217e951363d60e9cd99c5bbfd23d4615043ac', 'https://github.com/twisted/twisted/security/advisories/GHSA-c2jg-hw38-jrqq', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24801'} | null |
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