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 | GHSA-c9qf-r67m-p7cg | Null pointer dereference in `CompressElement` | ### Impact
It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`:
```python
import tensorflow as tf
tf.raw_ops.CompressElement(components=[[]])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid.
### Patches
We have patched the issue in GitHub commit [5dc7f6981fdaf74c8c5be41f393df705841fb7c5](https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5).
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. Concurrently, it was resolved in `master` branch as it was also discovered internally and fixed before the report was handled. | {'CVE-2021-37637'} | 2022-03-03T05:12:29.841961Z | 2021-08-25T14:44:12Z | HIGH | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9qf-r67m-p7cg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37637'} | null |
PyPI | PYSEC-2021-221 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in caused by an integer overflow in constructing a new tensor shape. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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-xvjm-fvxx-q3hv', 'CVE-2021-29584'} | 2021-08-27T03:22:36.340283Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvjm-fvxx-q3hv', 'https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60'} | null |
PyPI | GHSA-rjvg-q57v-mjjc | XSS in Apache Airflow | In Apache Airflow before 1.10.5 when running with the "classic" UI, a malicious admin user could edit the state of objects in the Airflow metadata database to execute arbitrary javascript on certain page views. The new "RBAC" UI is unaffected. | {'CVE-2019-12398'} | 2022-03-03T05:13:43.588445Z | 2020-05-06T19:51:02Z | MODERATE | null | {'CWE-79'} | {'https://lists.apache.org/thread.html/r72487ad6b23d18689896962782f8c93032afe5c72a6bfd23b253352b@%3Cdev.airflow.apache.org%3E', 'https://github.com/apache/airflow', 'https://nvd.nist.gov/vuln/detail/CVE-2019-12398', 'https://lists.apache.org/thread.html/r72487ad6b23d18689896962782f8c93032afe5c72a6bfd23b253352b%40%3Cusers.airflow.apache.org%3E', 'http://www.openwall.com/lists/oss-security/2020/01/14/2', 'https://github.com/apache/airflow/blob/1.10.5/CHANGELOG.txt'} | null |
PyPI | GHSA-5wrh-4jwv-5w78 | Open redirect via transitional IPv6 addresses on dual-stack networks | ### Impact
Requests to user provided domains were not restricted to external IP addresses when transitional IPv6 addresses were used. Outbound requests to federation, identity servers, when calculating the key validity for third-party invite events, sending push notifications, and generating URL previews are affected. This could cause Synapse to make requests to internal infrastructure on dual-stack networks.
### Patches
This issue is fixed by #9240.
### Workarounds
Outbound requests to the following address ranges can be blocked by a firewall, if unused for internal communication between systems:
* `::ffff/80`
* `::0000/80` (note that this IP range is considered deprecated by the IETF)
* `2002::/16` (note that this IP range is considered deprecated by the IETF)
### References
* [RFC3056](https://tools.ietf.org/html/rfc3056)
* [RFC4291](https://tools.ietf.org/html/rfc4291) | {'CVE-2021-21392'} | 2022-03-03T05:12:57.469796Z | 2021-04-13T15:13:08Z | MODERATE | null | {'CWE-601'} | {'https://github.com/matrix-org/synapse/pull/9240', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-5wrh-4jwv-5w78', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21392', 'https://github.com/matrix-org/synapse', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/', 'https://pypi.org/project/matrix-synapse/'} | null |
PyPI | PYSEC-2020-221 | null | A flaw was found in Ansible Base when using the aws_ssm connection plugin as there is no namespace separation for file transfers. Files are written directly to the root bucket, making possible to have collisions when running multiple ansible processes. This issue affects mainly the service availability. | {'CVE-2020-25636'} | 2022-05-04T22:49:27.747026Z | 2020-10-05T13:15:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-25636', 'https://github.com/ansible-collections/community.aws/issues/221'} | null |
PyPI | PYSEC-2021-108 | null | An issue was discovered in urllib3 before 1.26.5. When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect. | {'GHSA-q2q7-5pp4-w6pg', 'CVE-2021-33503'} | 2021-07-02T18:56:20.858344Z | 2021-06-29T11:15:00Z | null | null | null | {'https://github.com/urllib3/urllib3/commit/2d4a3fee6de2fa45eb82169361918f759269b4ec', 'https://github.com/advisories/GHSA-q2q7-5pp4-w6pg'} | null |
PyPI | PYSEC-2021-558 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, as these are also affected and still in supported range. | {'CVE-2021-37645', 'GHSA-9w2p-5mgw-p94c'} | 2021-12-09T06:35:02.832886Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9w2p-5mgw-p94c', 'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1'} | null |
PyPI | GHSA-pgcq-h79j-2f69 | Incomplete validation of shapes in multiple TF ops | ### Impact
Several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible.
We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues.
### Patches
We have patched the issue in GitHub commits [68422b215e618df5ad375bcdc6d2052e9fd3080a](https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a), [4d74d8a00b07441cba090a02e0dd9ed385145bf4](https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4), [579261dcd446385831fe4f7457d802a59685121d](https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d), [da4aad5946be30e5f049920fa076e1f7ef021261](https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261), [4dddb2fd0b01cdd196101afbba6518658a2c9e07](https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07), and [e7f497570abb6b4ae5af4970620cd880e4c0c904](https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904).
These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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. | {'CVE-2021-41206'} | 2022-03-03T05:13:43.298539Z | 2021-11-10T19:03:38Z | HIGH | null | {'CWE-354'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69', 'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07', 'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41206', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d'} | null |
PyPI | PYSEC-2014-59 | null | Multiple open redirect vulnerabilities in (1) marmoset_patch.py, (2) publish.py, and (3) principiaredirect.py in Plone 2.1 through 4.1, 4.2.x through 4.2.5, and 4.3.x through 4.3.1 allow remote attackers to redirect users to arbitrary web sites and conduct phishing attacks via unspecified vectors. | {'CVE-2013-4195'} | 2021-07-25T23:34:46.736480Z | 2014-03-11T19:37:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=978471', 'http://plone.org/products/plone-hotfix/releases/20130618', 'http://seclists.org/oss-sec/2013/q3/261', 'http://plone.org/products/plone/security/advisories/20130618-announcement'} | null |
PyPI | GHSA-3m3h-v9hv-9j4h | Cross-site Scripting in django-wiki | In Django-wiki, versions 0.0.20 to 0.7.8 are vulnerable to Stored Cross-Site Scripting (XSS) in Notifications Section. An attacker who has access to edit pages can inject JavaScript payload in the title field. When a victim gets a notification regarding the changes made in the application, the payload in the notification panel renders and loads external JavaScript. | {'CVE-2021-25986'} | 2022-03-03T05:11:38.545951Z | 2021-12-02T17:49:40Z | MODERATE | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-25986', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25986', 'https://github.com/django-wiki/django-wiki/commit/9eaccc7519e4206a4d2f22640882f0737b2da9c5', 'https://github.com/django-wiki/django-wiki/pull/1148', 'https://github.com/django-wiki/django-wiki'} | null |
PyPI | GHSA-57wx-m983-2f88 | Incomplete validation in boosted trees code | ### Impact
The [code for boosted trees in TensorFlow](https://github.com/tensorflow/tensorflow/blob/e0b6e58c328059829c3eb968136f17aa72b6c876/tensorflow/core/kernels/boosted_trees/stats_ops.cc) is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call.
**Note**: Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. Instead, please use the downstream [TensorFlow Decision Forests](https://github.com/tensorflow/decision-forests) project which is newer and supports more features. We will deprecate TensorFlow's boosted trees APIs in subsequent releases.
### Patches
We have patched the issue in GitHub commit [5c8c9a8bfe750f9743d0c859bae112060b216f5c](https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c).
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.
### 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-2021-41208'} | 2022-03-03T05:12:33.756260Z | 2021-11-10T19:37:56Z | CRITICAL | null | {'CWE-476', 'CWE-824'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41208', 'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} | null |
PyPI | PYSEC-2020-108 | null | ** DISPUTED ** svm_predict_values in svm.cpp in Libsvm v324, as used in scikit-learn 0.23.2 and other products, allows attackers to cause a denial of service (segmentation fault) via a crafted model SVM (introduced via pickle, json, or any other model permanence standard) with a large value in the _n_support array. NOTE: the scikit-learn vendor's position is that the behavior can only occur if the library's API is violated by an application that changes a private attribute. | {'CVE-2020-28975'} | 2020-12-03T18:50:00Z | 2020-11-21T21:15:00Z | null | null | null | {'http://seclists.org/fulldisclosure/2020/Nov/44', 'https://github.com/cjlin1/libsvm/blob/9a3a9708926dec87d382c43b203f2ca19c2d56a0/svm.cpp#L2501', 'https://github.com/scikit-learn/scikit-learn/issues/18891', 'http://packetstormsecurity.com/files/160281/SciKit-Learn-0.23.2-Denial-Of-Service.html'} | null |
PyPI | GHSA-q34h-97wf-8r8j | vault-cli contains possible RCE when reading user-defined data | ### Impact
_What kind of vulnerability is it? Who is impacted?_
vault-cli features the ability for rendering templated values (as explained in the [documentation](https://github.com/peopledoc/vault-cli/blob/2.2.1/docs/howto/templated_secrets.rst)). When a secret starts with the prefix `!template!`, vault-cli interprets the rest of the contents of the secret as a Jinja2 template.
Jinja2 is a powerful templating engine and it's not designed to safely render arbitrary templates. An attacker controlling a jinja2 template rendered on a machine can trigger arbitrary code, making this a Remote Code Execution (RCE) risk.
If the content of the vault can be completely trusted, then this is not a problem. Otherwise, if your threat model includes cases where an attacker can manipulate a secret value read from the vault using vault-cli, then this vulnerability may impact you.
This does not impact `vault` itself, except for the fact that the attacker, having an RCE on the machine that executes `vault-cli`, may abuse the token that `vault-cli` uses, to read, write or delete other data from the vault.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
In 3.0.0, the code related to interpreting vault templated secrets has been removed entirely.
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
Using the environment variable `VAULT_CLI_RENDER=false` or the flag `--no-render` (placed between `vault-cli` and the subcommand, e.g. `vault-cli --no-render get-all`) or adding `render: false` to the vault-cli configuration yaml file disables rendering and removes the vulnerability.
Using the python library, you can use: `vault_cli.get_client(render=False)` when creating your client to get a client that will not render templated secrets and thus operates securely.
### References
_Are there any links users can visit to find out more?_
Here's an article explaining how jinja2 templates might be exploited to have side effects: https://podalirius.net/en/publications/grehack-2021-optimizing-ssti-payloads-for-jinja2/
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the vault-cli repo](https://github.com/peopledoc/vault-cli/issues/new)
| {'CVE-2021-43837'} | 2022-03-03T05:13:34.513125Z | 2021-12-16T21:02:12Z | HIGH | null | {'CWE-74'} | {'https://github.com/peopledoc/vault-cli/security/advisories/GHSA-q34h-97wf-8r8j', 'https://podalirius.net/en/publications/grehack-2021-optimizing-ssti-payloads-for-jinja2/', 'https://github.com/peopledoc/vault-cli', 'https://github.com/peopledoc/vault-cli/commit/3ba3955887fd6b7d4d646c8b260f21cebf5db852', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43837', 'https://github.com/peopledoc/vault-cli/releases/tag/3.0.0', 'https://github.com/peopledoc/vault-cli/pull/198'} | null |
PyPI | PYSEC-2020-13 | null | A flaw was found in the Ansible Engine affecting Ansible Engine versions 2.7.x before 2.7.17 and 2.8.x before 2.8.11 and 2.9.x before 2.9.7 as well as Ansible Tower before and including versions 3.4.5 and 3.5.5 and 3.6.3 when the ldap_attr and ldap_entry community modules are used. The issue discloses the LDAP bind password to stdout or a log file if a playbook task is written using the bind_pw in the parameters field. The highest threat from this vulnerability is data confidentiality. | {'GHSA-j2h6-73x8-22c4', 'CVE-2020-1746'} | 2020-05-26T17:38:00Z | 2020-05-12T18:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-j2h6-73x8-22c4', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1746', 'https://github.com/ansible/ansible/pull/67866'} | null |
PyPI | PYSEC-2018-95 | null | An issue was discovered in Yelp OSXCollector. A maliciously crafted Universal/fat binary can evade third-party code signing checks. By not completing full inspection of the Universal/fat binary, the user of the third-party tool will believe that the code is signed by Apple, but the malicious unsigned code will execute. | {'CVE-2018-10406'} | 2021-08-27T03:22:09.893229Z | 2018-06-13T22:29:00Z | null | null | null | {'https://www.okta.com/security-blog/2018/06/issues-around-third-party-apple-code-signing-checks/'} | null |
PyPI | PYSEC-2022-101 | null | Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a `DCHECK` (which is a no-op during production). An attacker can control the `input_idx` variable such that `ix` would be larger than the number of values in `node_t.args`. The fix will be included in TensorFlow 2.8.0. This is the only affected version. | {'CVE-2022-23592', 'GHSA-vq36-27g6-p492'} | 2022-03-09T00:17:36.334274Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq36-27g6-p492', 'https://github.com/tensorflow/tensorflow/commit/c99d98cd189839dcf51aee94e7437b54b31f8abd', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L223-L229'} | null |
PyPI | PYSEC-2021-86 | null | This affects all versions of package qlib. The workflow function in cli part of qlib was using an unsafe YAML load function. | {'SNYK-PYTHON-QLIB-1054635', 'CVE-2021-23338'} | 2021-06-09T05:01:32.318077Z | 2021-02-15T16:15:00Z | null | null | null | {'https://snyk.io/vuln/SNYK-PYTHON-QLIB-1054635', 'https://github.com/418sec/huntr/pull/1329'} | null |
PyPI | PYSEC-2021-333 | null | sqlparse is a non-validating SQL parser module for Python. In sqlparse versions 0.4.0 and 0.4.1 there is a regular Expression Denial of Service in sqlparse vulnerability. The regular expression may cause exponential backtracking on strings containing many repetitions of '\r\n' in SQL comments. Only the formatting feature that removes comments from SQL statements is affected by this regular expression. As a workaround don't use the sqlformat.format function with keyword strip_comments=True or the --strip-comments command line flag when using the sqlformat command line tool. The issues has been fixed in sqlparse 0.4.2. | {'CVE-2021-32839', 'GHSA-p5w8-wqhj-9hhf'} | 2021-09-23T00:11:34.256842Z | 2021-09-20T17:15:00Z | null | null | null | {'https://github.com/andialbrecht/sqlparse/commit/8238a9e450ed1524e40cb3a8b0b3c00606903aeb', 'https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-p5w8-wqhj-9hhf'} | null |
PyPI | PYSEC-2021-763 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. 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-2021-37652', 'GHSA-m7fm-4jfh-jrg6'} | 2021-12-09T06:35:36.820839Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m7fm-4jfh-jrg6'} | null |
PyPI | GHSA-4mp3-385r-v63f | Denial of service attack due to invalid JSON | ### Impact
A denial of service attack against Matrix clients can be exploited by sending an event including invalid JSON data to Synapse. Synapse would relay the data to clients which could crash or hang. Impact is long-lasting if the event is made part of the room state.
### Patches
At a minimum #8106 and #8291 must be applied. #7372 and #8124 include additional checks.
### Workarounds
There are no known workarounds.
### Upgrading notes
If an invalid event is accepted by an earlier Synapse it can become part of the room state and will not be fixed by upgrading Synapse. Redacting the invalid event should avoid clients receiving the invalid event. | {'CVE-2020-26890'} | 2022-03-03T05:14:07.769396Z | 2020-11-24T22:58:58Z | HIGH | null | {'CWE-20'} | {'https://github.com/matrix-org/synapse/security/advisories/GHSA-4mp3-385r-v63f', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/U34DPP4ZLOEDUY2ZCWOHQPU5GA5LYNUQ/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26890', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/G7YXMMYQP46PYL664JQUXCA3LPBJU7DQ/', 'https://pypi.org/project/matrix-synapse/'} | null |
PyPI | PYSEC-2021-299 | 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'} | 2021-08-27T03:22:46.477427Z | 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-2019-230 | 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'} | 2021-12-09T06:35:11.711453Z | 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-2020-333 | null | In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. 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-2020-26267', 'GHSA-c9f3-9wfr-wgh7'} | 2021-12-09T06:35:16.197426Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7', 'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} | null |
PyPI | PYSEC-2015-23 | null | The (1) contrib.sessions.backends.base.SessionBase.flush and (2) cache_db.SessionStore.flush functions in Django 1.7.x before 1.7.10, 1.4.x before 1.4.22, and possibly other versions create empty sessions in certain circumstances, which allows remote attackers to cause a denial of service (session store consumption) via unspecified vectors. | {'CVE-2015-5964'} | 2021-07-15T02:22:10.010649Z | 2015-08-24T14:59:00Z | null | null | null | {'http://rhn.redhat.com/errata/RHSA-2015-1894.html', 'http://rhn.redhat.com/errata/RHSA-2015-1766.html', 'https://www.djangoproject.com/weblog/2015/aug/18/security-releases/', 'http://www.ubuntu.com/usn/USN-2720-1', 'http://www.oracle.com/technetwork/topics/security/bulletinoct2015-2511968.html', 'http://www.securitytracker.com/id/1033318', 'http://rhn.redhat.com/errata/RHSA-2015-1767.html', 'http://www.securityfocus.com/bid/76440', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-November/172084.html', 'http://www.debian.org/security/2015/dsa-3338'} | null |
PyPI | PYSEC-2020-299 | null | In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-26268', 'GHSA-hhvc-g5hv-48c6'} | 2021-12-09T06:34:44.590878Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hhvc-g5hv-48c6'} | null |
PyPI | PYSEC-2020-276 | null | In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'GHSA-pg59-2f92-5cph', 'CVE-2020-15196'} | 2021-12-09T06:34:41.429939Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph'} | null |
PyPI | PYSEC-2021-276 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. 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-2021-37654', 'GHSA-2r8p-fg3c-wcj4'} | 2021-08-27T03:22:44.348474Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r8p-fg3c-wcj4', 'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} | null |
PyPI | PYSEC-2021-69 | null | In Pillow before 8.1.0, PcxDecode has a buffer over-read when decoding a crafted PCX file because the user-supplied stride value is trusted for buffer calculations. | {'CVE-2020-35653', 'GHSA-f5g8-5qq7-938w'} | 2021-01-29T00:47:00Z | 2021-01-12T09:15:00Z | null | null | null | {'https://pillow.readthedocs.io/en/stable/releasenotes/index.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6BYVI5G44MRIPERKYDQEL3S3YQCZTVHE/', 'https://github.com/advisories/GHSA-f5g8-5qq7-938w', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BF553AMNNNBW7SH4IM4MNE4M6GNZQ7YD/'} | null |
PyPI | GHSA-rv62-4pmj-xw6h | Moderate severity vulnerability that affects jupyterhub and notebook | An Open Redirect vulnerability for all browsers in Jupyter Notebook before 5.7.8 and some browsers (Chrome, Firefox) in JupyterHub before 0.9.6 allows crafted links to the login page, which will redirect to a malicious site after successful login. Servers running on a base_url prefix are not affected. | {'CVE-2019-10255'} | 2022-03-03T05:13:29.561514Z | 2019-04-02T15:46:54Z | MODERATE | null | {'CWE-601'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-10255', 'https://github.com/jupyter/notebook/commit/08c4c898182edbe97aadef1815cce50448f975cb', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/VMDPJBVXOVO6LYGAT46VZNHH6JKSCURO/', 'https://github.com/jupyter/notebook/commit/70fe9f0ddb3023162ece21fbb77d5564306b913b', 'https://github.com/jupyter/notebook', 'https://github.com/jupyter/notebook/commit/d65328d4841892b412aef9015165db1eb029a8ed', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/UP5RLEES2JBBNSNLBR65XM6PCD4EMF7D/', 'https://github.com/advisories/GHSA-rv62-4pmj-xw6h', 'https://blog.jupyter.org/open-redirect-vulnerability-in-jupyter-jupyterhub-adf43583f1e4', 'https://github.com/jupyter/notebook/compare/05aa4b2...16cf97c'} | null |
PyPI | PYSEC-2021-626 | null | TensorFlow is an open source platform for machine learning. In affected versions the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an `Enter` node) always exists when encountering the second node (e.g., an `Exit` node). When this is not the case, `parent` is `nullptr` so dereferencing it causes a crash. 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-2021-41217', 'GHSA-5crj-c72x-m7gq'} | 2021-12-09T06:35:09.978431Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq'} | null |
PyPI | PYSEC-2022-96 | null | Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. 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-2022-23587', 'GHSA-8jj7-5vxc-pg2q'} | 2022-03-09T00:17:35.797116Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8jj7-5vxc-pg2q', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689', 'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f'} | null |
PyPI | GHSA-jj53-8fmw-f2w2 | Adding a private/unlisted room to a community exposes room metadata in an unauthorised manner. | ### Impact
Unauthorised users can access the name, avatar, topic and number of members of a room if they know the ID of the room. This vulnerability is limited to homeservers where:
- the vulnerable homeserver is in the room; and
- untrusted users are permitted to create groups (communities).
By default, only homeserver administrators can create groups. However, homeserver administrators can already access this information in the database or using the admin API. As a result, only homeservers where the configuration setting `enable_group_creation` has been set to `true` are impacted.
### Patches
Server administrators should upgrade to 1.41.1 or higher.
### Workarounds
Server administrators can set `enable_group_creation` to `false` in their homeserver configuration (this is the default value) to prevent creation of groups by non-administrators.
Administrators that are using a reverse proxy could, with partial loss of group functionality, block the following endpoints:
* `/_matrix/client/r0/groups/{group_id}/rooms`
* `/_matrix/client/unstable/groups/{group_id}/rooms`
### References
n/a
### For more information
If you have any questions or comments about this advisory, e-mail us at security@matrix.org.
| {'CVE-2021-39163'} | 2022-03-03T05:13:57.111332Z | 2021-09-01T18:25:44Z | LOW | null | {'CWE-200'} | {'https://github.com/matrix-org/synapse/releases/tag/v1.41.1', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-jj53-8fmw-f2w2', 'https://nvd.nist.gov/vuln/detail/CVE-2021-39163', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2VHDEPCZ22GJFMZCWA2XZAGPOEV72POF/', 'https://github.com/matrix-org/synapse', 'https://github.com/matrix-org/synapse/commit/cb35df940a', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/PXT7ID7DNBRN2TVTETU3SYQHJKEG6PXN/'} | null |
PyPI | PYSEC-2016-33 | null | schema.py in Roundup before 1.5.1 does not properly limit attributes included in default user permissions, which might allow remote authenticated users to obtain sensitive user information by viewing user details. | {'CVE-2014-6276'} | 2021-08-27T03:22:19.738380Z | 2016-04-13T14:59:00Z | null | null | null | {'http://hg.code.sf.net/p/roundup/code/rev/a403c29ffaf9', 'https://sourceforge.net/p/roundup/code/ci/tip/tree/CHANGES.txt', 'http://www.debian.org/security/2016/dsa-3502'} | null |
PyPI | GHSA-95xm-g58g-3p88 | Integer division by 0 in sparse reshaping | ### Impact
The implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception:
```python
import tensorflow as tf
tf.raw_ops.SparseReshape(
input_indices = np.ones((1,3)),
input_shape = np.array([1,1,0]),
new_shape = np.array([1,0]))
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements.
The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0.
### Patches
We have patched the issue in GitHub commit [4923de56ec94fff7770df259ab7f2288a74feb41](https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.
### 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-2021-37640'} | 2022-03-03T05:13:02.147643Z | 2021-08-25T14:44:02Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-95xm-g58g-3p88', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37640', 'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} | null |
PyPI | GHSA-977j-xj7q-2jr9 | Segmentation faultin TensorFlow when converting a Python string to `tf.float16` | ### Impact
Converting a string (from Python) to a `tf.float16` value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode.
This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a `tf.float16` value.
Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar `tf.float16` value with a scalar string will trigger this issue due to automatic conversions.
This can be easily reproduced by `tf.constant("hello", tf.float16)`, if eager execution is enabled.
### Patches
We have patched the vulnerability in GitHub commit [5ac1b9](https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf).
We are additionally releasing TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched.
TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected.
We encourage users to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.
### For more information
Please consult [`SECURITY.md`](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-2020-5215'} | 2022-03-03T05:13:26.765830Z | 2020-01-28T21:32:29Z | HIGH | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/releases/tag/v1.15.2', 'https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-977j-xj7q-2jr9', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5215', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.0.1'} | null |
PyPI | PYSEC-2022-156 | null | Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a `DCHECK` (which is a no-op during production). An attacker can control the `input_idx` variable such that `ix` would be larger than the number of values in `node_t.args`. The fix will be included in TensorFlow 2.8.0. This is the only affected version. | {'CVE-2022-23592', 'GHSA-vq36-27g6-p492'} | 2022-03-09T00:18:30.013116Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq36-27g6-p492', 'https://github.com/tensorflow/tensorflow/commit/c99d98cd189839dcf51aee94e7437b54b31f8abd', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L223-L229'} | null |
PyPI | GHSA-r478-c2pc-m7gx | DNS reply verification issue in dnslinb | The dnslib package through 0.9.16 for Python does not verify that the ID value in a DNS reply matches an ID value in a query. | {'CVE-2022-22846'} | 2022-03-03T05:13:18.614948Z | 2022-01-12T20:07:10Z | HIGH | null | null | {'https://github.com/paulc/dnslib/', 'https://github.com/paulc/dnslib/issues/30', 'https://nvd.nist.gov/vuln/detail/CVE-2022-22846', 'https://github.com/paulc/dnslib/commit/76e8677699ed098387d502c57980f58da642aeba'} | null |
PyPI | PYSEC-2019-119 | null | SaltStack Salt 2018.3, 2019.2 is affected by: SQL Injection. The impact is: An attacker could escalate privileges on MySQL server deployed by cloud provider. It leads to RCE. The component is: The mysql.user_chpass function from the MySQL module for Salt. The attack vector is: specially crafted password string. The fixed version is: 2018.3.4. | {'CVE-2019-1010259'} | 2019-08-13T18:15:00Z | 2019-07-18T17:15:00Z | null | null | null | {'https://github.com/saltstack/salt/pull/51462', 'https://github.com/ShantonRU/salt/commit/a46c86a987c78e74e87969d8d3b27094e6544b7a', 'https://github.com/saltstack/salt/blob/f22de0887cd7167887f113bf394244b74fb36b6b/salt/modules/mysql.py#L1534'} | null |
PyPI | PYSEC-2022-79 | null | Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is guarded by a `DCHECK`. However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range. | {'CVE-2022-23570', 'GHSA-9p77-mmrw-69c7'} | 2022-03-09T00:17:33.672987Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L104-L106', 'https://github.com/tensorflow/tensorflow/commit/8a513cec4bec15961fbfdedcaa5376522980455c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9p77-mmrw-69c7'} | null |
PyPI | PYSEC-2020-44 | null | This affects the package Gerapy from 0 and before 0.9.3. The input being passed to Popen, via the project_configure endpoint, isn’t being sanitized. | {'GHSA-g57j-q48p-9vm2', 'CVE-2020-7698', 'SNYK-PYTHON-GERAPY-572470'} | 2020-07-31T14:59:00Z | 2020-07-29T13:15:00Z | null | null | null | {'https://snyk.io/vuln/SNYK-PYTHON-GERAPY-572470', 'https://github.com/advisories/GHSA-g57j-q48p-9vm2', 'https://github.com/Gerapy/Gerapy/commit/e8446605eb2424717418eae199ec7aad573da2d2'} | null |
PyPI | PYSEC-2015-35 | null | Buffer overflow in the C implementation of the apply_delta function in _pack.c in Dulwich before 0.9.9 allows remote attackers to execute arbitrary code via a crafted pack file. | {'CVE-2015-0838'} | 2021-08-27T03:22:03.427700Z | 2015-03-31T14:59:00Z | null | null | null | {'https://lists.launchpad.net/dulwich-users/msg00829.html', 'http://www.debian.org/security/2015/dsa-3206'} | null |
PyPI | PYSEC-2020-325 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15210', 'GHSA-x9j7-x98r-r4w2'} | 2021-12-09T06:35:15.211180Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2', 'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | PYSEC-2019-226 | null | Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local). | {'CVE-2018-8825'} | 2021-08-27T03:22:22.407658Z | 2019-04-23T21:29:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-003.md'} | null |
PyPI | PYSEC-2021-775 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. 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-2021-37664', 'GHSA-r4c4-5fpq-56wg'} | 2021-12-09T06:35:37.904410Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4c4-5fpq-56wg', 'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | null |
PyPI | PYSEC-2021-325 | null | Flask-RESTX (pypi package flask-restx) is a community driven fork of Flask-RESTPlus. Flask-RESTX before version 0.5.1 is vulnerable to ReDoS (Regular Expression Denial of Service) in email_regex. This is fixed in version 0.5.1. | {'CVE-2021-32838', 'GHSA-3q6g-vf58-7m4g'} | 2021-09-20T20:31:06.092661Z | 2021-09-20T18:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-3q6g-vf58-7m4g', 'https://github.com/python-restx/flask-restx/issues/372', 'https://pypi.org/project/flask-restx/', 'https://github.com/python-restx/flask-restx/blob/fd99fe11a88531f5f3441a278f7020589f9d2cc0/flask_restx/inputs.py#L51', 'https://github.com/python-restx/flask-restx/commit/bab31e085f355dd73858fd3715f7ed71849656da'} | null |
PyPI | PYSEC-2019-16 | null | Django before 1.11.27, 2.x before 2.2.9, and 3.x before 3.0.1 allows account takeover. A suitably crafted email address (that is equal to an existing user's email address after case transformation of Unicode characters) would allow an attacker to be sent a password reset token for the matched user account. (One mitigation in the new releases is to send password reset tokens only to the registered user email address.) | {'CVE-2019-19844', 'GHSA-vfq6-hq5r-27r6'} | 2020-01-08T04:15:00Z | 2019-12-18T19:15:00Z | null | null | null | {'https://www.debian.org/security/2020/dsa-4598', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HCM2DPUI7TOZWN4A6JFQFUVQ2XGE7GUD/', 'https://security.gentoo.org/glsa/202004-17', 'https://usn.ubuntu.com/4224-1/', 'https://github.com/advisories/GHSA-vfq6-hq5r-27r6', 'http://packetstormsecurity.com/files/155872/Django-Account-Hijack.html', 'https://security.netapp.com/advisory/ntap-20200110-0003/', 'https://seclists.org/bugtraq/2020/Jan/9', 'https://groups.google.com/forum/#!topic/django-announce/3oaB2rVH3a0', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://www.djangoproject.com/weblog/2019/dec/18/security-releases/'} | null |
PyPI | PYSEC-2021-90 | null | Flask-AppBuilder is a development framework, built on top of Flask. User enumeration in database authentication in Flask-AppBuilder <= 3.2.3. Allows for a non authenticated user to enumerate existing accounts by timing the response time from the server when you are logging in. Upgrade to version 3.3.0 or higher to resolve. | {'GHSA-434h-p4gx-jm89', 'CVE-2021-29621'} | 2021-06-09T05:01:12.347920Z | 2021-06-07T19:15:00Z | null | null | null | {'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-434h-p4gx-jm89', 'https://github.com/dpgaspar/Flask-AppBuilder/commit/780bd0e8fbf2d36ada52edb769477e0a4edae580', 'https://pypi.org/project/Flask-AppBuilder/'} | null |
PyPI | PYSEC-2021-630 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. 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-2021-41221', 'GHSA-cqv6-3phm-hcwx'} | 2021-12-09T06:35:10.512289Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx'} | null |
PyPI | GHSA-hgc3-hp6x-wpgx | Antilles Dependency Confusion Vulnerability | ### Potential Impact:
Remote code execution.
### Scope of Impact:
Open-source project specific.
### Summary Description:
A dependency confusion vulnerability was reported in the Antilles open-source software prior to version 1.0.1 that could allow for remote code execution during installation due to a package listed in requirements.txt not existing in the public package index (PyPi).
MITRE classifies this weakness as an Uncontrolled Search Path Element (CWE-427) in which a private package dependency may be replaced by an unauthorized package of the same name published to a well-known public repository such as PyPi.
The configuration has been updated to only install components built by Antilles, removing all other public package indexes. Additionally, the antilles-tools dependency has been published to PyPi.
### Mitigation Strategy for Customers (what you should do to protect yourself):
Remove previous versions of Antilles as a precautionary measure and Update to version 1.0.1 or later.
### Acknowledgement:
The Antilles team thanks Kotko Vladyslav for reporting this issue.
### References:
https://github.com/lenovo/Antilles/commit/c7b9c5740908b343aceefe69733d9972e64df0b9
| {'CVE-2021-3840'} | 2022-03-03T05:12:31.166669Z | 2021-11-03T17:36:22Z | HIGH | null | {'CWE-427'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-3840', 'https://github.com/lenovo/Antilles/commit/c7b9c5740908b343aceefe69733d9972e64df0b9', 'https://github.com/lenovo/Antilles/security/advisories/GHSA-hgc3-hp6x-wpgx', 'https://github.com/lenovo/Antilles'} | null |
PyPI | GHSA-976r-qfjj-c24w | Command injection via Celery broker in Apache Airflow | An issue was found in Apache Airflow versions 1.10.10 and below. When using CeleryExecutor, if an attacker can connect to the broker (Redis, RabbitMQ) directly, it is possible to inject commands, resulting in the celery worker running arbitrary commands. | {'CVE-2020-11981'} | 2022-03-21T23:01:56.303254Z | 2020-07-27T16:57:33Z | CRITICAL | null | {'CWE-78'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-11981', 'https://issues.apache.org/jira/browse/AIRFLOW-6351', 'https://github.com/apache/airflow/commit/afa4b11fddfdbadb048f742cf66d5c21c675a5c8', 'https://lists.apache.org/thread.html/r7255cf0be3566f23a768e2a04b40fb09e52fcd1872695428ba9afe91%40%3Cusers.airflow.apache.org%3E'} | null |
PyPI | PYSEC-2021-260 | null | TensorFlow is an end-to-end open source platform for machine learning. Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. We have patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314. 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-hwr7-8gxx-fj5p', 'CVE-2021-37638'} | 2021-08-27T03:22:42.935785Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hwr7-8gxx-fj5p'} | null |
PyPI | GHSA-jx8f-cpx7-fv47 | Allocation of Resources Without Limits or Throttling in nvflare | ### Impact
NVIDIA FLARE contains a vulnerability in Admin Interface, where an un-authorized attacker can cause Allocation of Resources Without Limits or Throttling, which may lead to cause system unavailable
All versions before 2.0.16 are affected.
### Patches
The patch will be included in nvflare==2.0.16.
### Workarounds
The changes in commits https://github.com/NVIDIA/NVFlare/commit/93588b3a0dff9bd4568983071b74d8b420de3a6e and https://github.com/NVIDIA/NVFlare/commit/93588b3a0dff9bd4568983071b74d8b420de3a6e can be applied to any version of the NVIDIA FLARE without any adverse effect.
### Additional information
Issue Found on: 2022.3.3
Issue Found by: Oliver Sellwood (@Nintorac) | {'CVE-2022-21822'} | 2022-03-29T21:31:50.869688Z | 2022-03-18T23:18:43Z | HIGH | null | {'CWE-770'} | {'https://github.com/NVIDIA/NVFlare/security/advisories/GHSA-jx8f-cpx7-fv47', 'https://github.com/NVIDIA/NVFlare', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21822'} | null |
PyPI | GHSA-r6jx-9g48-2r5r | Arbitrary code execution due to YAML deserialization | ### Impact
TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format.
```python
from tensorflow.keras import models
payload = '''
!!python/object/new:type
args: ['z', !!python/tuple [], {'extend': !!python/name:exec }]
listitems: "__import__('os').system('cat /etc/passwd')"
'''
models.model_from_yaml(payload)
```
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.
### Patches
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](https://github.com/tensorflow/tensorflow/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.
### 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 Arjun Shibu. | {'CVE-2021-37678'} | 2022-03-23T23:00:12.045441Z | 2021-08-25T14:41:12Z | CRITICAL | null | {'CWE-502'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37678', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r'} | null |
PyPI | PYSEC-2020-260 | null | In Twisted Web through 19.10.0, there was an HTTP request splitting vulnerability. When presented with a content-length and a chunked encoding header, the content-length took precedence and the remainder of the request body was interpreted as a pipelined request. | {'GHSA-p5xh-vx83-mxcj', 'CVE-2020-10109'} | 2021-08-27T03:22:49.656900Z | 2020-03-12T13:15:00Z | null | null | null | {'https://usn.ubuntu.com/4308-1/', 'https://security.gentoo.org/glsa/202007-24', 'https://know.bishopfox.com/advisories', 'https://know.bishopfox.com/advisories/twisted-version-19.10.0', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6ISMZFZBWW4EV6ETJGXAYIXN3AT7GBPL/', 'https://github.com/advisories/GHSA-p5xh-vx83-mxcj', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YW3NIL7VXSGJND2Q4BSXM3CFTAFU6T7D/', 'https://usn.ubuntu.com/4308-2/'} | null |
PyPI | PYSEC-2021-149 | null | TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected. | {'GHSA-4278-2v5v-65r4', 'CVE-2021-29512'} | 2021-08-27T03:22:23.518786Z | 2021-05-14T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4', 'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | null |
PyPI | PYSEC-2021-519 | null | TensorFlow is an end-to-end open source platform for machine learning. TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the `While` implementation(https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the `Eval` function for the other and this quickly exhaust all stack space. 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. 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-2021-29591', 'GHSA-cwv3-863g-39vx'} | 2021-12-09T06:34:57.477070Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cwv3-863g-39vx'} | null |
PyPI | PYSEC-2014-18 | null | Multiple unspecified vulnerabilities in Salt (aka SaltStack) before 2014.1.10 allow local users to have an unspecified impact via vectors related to temporary file creation in (1) seed.py, (2) salt-ssh, or (3) salt-cloud. | {'CVE-2014-3563'} | 2021-07-05T00:01:26.140564Z | 2014-08-22T17:55:00Z | null | null | null | {'http://docs.saltstack.com/en/latest/topics/releases/2014.1.10.html', 'http://www.securityfocus.com/bid/69319', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/95392', 'http://seclists.org/oss-sec/2014/q3/428'} | null |
PyPI | PYSEC-2016-25 | null | flask-oidc version 0.1.2 and earlier is vulnerable to an open redirect | {'CVE-2016-1000001'} | 2021-08-27T03:22:03.924557Z | 2016-10-07T18:59:00Z | null | null | null | {'https://github.com/puiterwijk/flask-oidc/blob/master/flask_oidc/__init__.py#L293'} | null |
PyPI | GHSA-h5jv-4p7w-64jg | Uncontrolled Resource Consumption 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. Due to the behaviour of the underlying HTMLParser, django.utils.html.strip_tags would be extremely slow to evaluate certain inputs containing large sequences of nested incomplete HTML entities. | {'CVE-2019-14233'} | 2022-03-03T05:14:20.579514Z | 2019-08-06T01:43:33Z | HIGH | null | {'CWE-400'} | {'https://seclists.org/bugtraq/2019/Aug/15', 'https://groups.google.com/forum/#!topic/django-announce/jIoju2-KLDs', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://security.gentoo.org/glsa/202004-17', 'https://security.netapp.com/advisory/ntap-20190828-0002/', 'https://www.debian.org/security/2019/dsa-4498', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00006.html', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00025.html', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14233', '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-2020-149 | null | The _encode_invalid_chars function in util/url.py in the urllib3 library 1.25.2 through 1.25.7 for Python allows a denial of service (CPU consumption) because of an inefficient algorithm. The percent_encodings array contains all matches of percent encodings. It is not deduplicated. For a URL of length N, the size of percent_encodings may be up to O(N). The next step (normalize existing percent-encoded bytes) also takes up to O(N) for each step, so the total time is O(N^2). If percent_encodings were deduplicated, the time to compute _encode_invalid_chars would be O(kN), where k is at most 484 ((10+6*2)^2). | {'GHSA-hmv2-79q8-fv6g', 'CVE-2020-7212'} | 2020-03-09T16:55:00Z | 2020-03-06T20:15:00Z | null | null | null | {'https://pypi.org/project/urllib3/1.25.8/', 'https://github.com/advisories/GHSA-hmv2-79q8-fv6g', 'https://github.com/urllib3/urllib3/blob/master/CHANGES.rst', 'https://github.com/urllib3/urllib3/commit/a74c9cfbaed9f811e7563cfc3dce894928e0221a'} | null |
PyPI | GHSA-73m2-3pwg-5fgc | Catastrophic backtracking in regex allows Denial of Service in Waitress | ### Impact
When waitress receives a header that contains invalid characters it will cause the regular expression engine to catastrophically backtrack causing the process to use 100% CPU time and blocking any other interactions.
This would allow an attacker to send a single request with an invalid header and take the service offline.
Invalid header example:
```
Bad-header: xxxxxxxxxxxxxxx\x10
```
Increasing the number of `x`'s in the header will increase the amount of time Waitress spends in the regular expression engine.
This issue was introduced in version 1.4.2 when the regular expression was updated to attempt to match the behaviour required by errata associated with RFC7230.
### Patches
The regular expression that is used to validate incoming headers has been updated in version 1.4.3, it is recommended that people upgrade to the new version of Waitress as soon as possible.
### Workarounds
If you have deployed a reverse proxy in front of Waitress it may already be rejecting requests that include invalid headers.
### Thanks
The Pylons Project would like to thank [Fil Zembowicz](https://github.com/fzembow) for reaching out and disclosing this vulnerability!
### References
Catastrophic backtracking explained: https://www.regular-expressions.info/catastrophic.html
### For more information
If you have any questions or comments about this advisory:
- 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-2020-5236'} | 2022-03-24T19:27:20Z | 2020-02-04T03:07:31Z | CRITICAL | null | {'CWE-400'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-5236', 'https://github.com/Pylons/waitress', 'https://github.com/Pylons/waitress/commit/6e46f9e3f014d64dd7d1e258eaf626e39870ee1f', 'https://github.com/Pylons/waitress/security/advisories/GHSA-73m2-3pwg-5fgc'} | null |
PyPI | PYSEC-2022-80 | null | Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. 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-j3mj-fhpq-qqjj', 'CVE-2022-23571'} | 2022-03-09T00:17:33.801262Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j3mj-fhpq-qqjj', 'https://github.com/tensorflow/tensorflow/commit/5b491cd5e41ad63735161cec9c2a568172c8b6a3'} | null |
PyPI | PYSEC-2020-52 | null | jupyterhub-systemdspawner enables JupyterHub to spawn single-user notebook servers using systemd. In jupyterhub-systemdspawner before version 0.15 user API tokens issued to single-user servers are specified in the environment of systemd units. These tokens are incorrectly accessible to all users. In particular, the-littlest-jupyterhub is affected, which uses systemdspawner by default. This is patched in jupyterhub-systemdspawner v0.15 | {'GHSA-cg54-gpgr-4rm6', 'CVE-2020-26261'} | 2020-12-10T21:46:00Z | 2020-12-09T17:15:00Z | null | null | null | {'https://github.com/jupyterhub/systemdspawner/commit/a4d08fd2ade1cfd0ef2c29dc221e649345f23580', 'https://github.com/jupyterhub/systemdspawner/blob/master/CHANGELOG.md#v015', 'https://github.com/jupyterhub/systemdspawner/security/advisories/GHSA-cg54-gpgr-4rm6', 'https://pypi.org/project/jupyterhub-systemdspawner/'} | null |
PyPI | PYSEC-2022-140 | 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'} | 2022-03-09T00:18:27.816300Z | 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-6qmf-fj6m-686c | Open Redirect in Flask-Security-Too | ### Impact
Flask-Security allows redirects after many successful views (e.g. /login) by honoring the ?next query param. There is code in FS to validate that the url specified in the next parameter is either relative OR has the same netloc (network location) as the requesting URL.
This check utilizes Pythons urlsplit library. However many browsers are very lenient on the kind of URL they accept and 'fill in the blanks' when presented with a possibly incomplete URL. As a concrete example - setting http://login?next=\\\github.com
will pass FS's relative URL check however many browsers will gladly convert this to http://github.com. Thus an attacker could send such a link to an unwitting user, using a legitimate site and have it redirect to whatever site they want.
This is considered a low severity due to the fact that if Werkzeug by default ALWAYS ensures that the Location header is absolute - thus making this attack vector mute. It is possible for application writers to modify this default behavior by setting the 'autocorrect_location_header=False` which would then open up their application to this attack.
### Patches
No patches as this time
### Workarounds
If using Werkzeug, make sure to use the default Location header setting. If you can't - then use@app.after_request and write your own validation of the Location header if it is set.
### References
No.
### For more information
If you have any questions or comments about this advisory follow: https://github.com/Flask-Middleware/flask-security/issues/486
Thanks to Claroty (2021-0141) and @snoopysecurity for providing details and proof of concept.
| {'CVE-2021-32618'} | 2022-03-03T05:14:07.610508Z | 2021-05-17T20:51:27Z | LOW | null | {'CWE-601'} | {'https://github.com/Flask-Middleware/flask-security/issues/486', 'https://github.com/Flask-Middleware/flask-security/security/advisories/GHSA-6qmf-fj6m-686c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32618'} | null |
PyPI | GHSA-3h8m-483j-7xxm | Heap out of bounds read in `RequantizationRange` | ### Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:
```python
import tensorflow as tf
input = tf.constant([1], shape=[1], dtype=tf.qint32)
input_max = tf.constant([], dtype=tf.float32)
input_min = tf.constant([], dtype=tf.float32)
tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays:
```cc
const float input_min_float = ctx->input(1).flat<float>()(0);
const float input_max_float = ctx->input(2).flat<float>()(0);
```
If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.
### Patches
We have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).
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-2021-29569'} | 2022-03-03T05:12:04.409348Z | 2021-05-21T14:25:22Z | LOW | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29569', 'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm'} | null |
PyPI | GHSA-3vcg-8p79-jpcv | Improper Restriction of XML External Entity Reference in svglib | The svglib package through 0.9.3 for Python allows XXE attacks via an svg2rlg call. | {'CVE-2020-10799'} | 2022-03-03T05:13:25.818108Z | 2021-05-06T18:52:01Z | HIGH | null | {'CWE-611'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-10799', 'https://github.com/deeplook/svglib/issues/229'} | null |
PyPI | PYSEC-2021-372 | null | OMERO.web provides a web based client and plugin infrastructure. In versions prior to 5.11.0, a variety of templates do not perform proper sanitization through HTML escaping. Due to the lack of sanitization and use of ``jQuery.html()``, there are a whole host of cross-site scripting possibilities with specially crafted input to a variety of fields. This issue is patched in version 5.11.0. There are no known workarounds aside from upgrading. | {'CVE-2021-41132', 'GHSA-g67g-hvc3-xmvf'} | 2021-10-17T22:58:18.538383Z | 2021-10-14T16:15:00Z | null | null | null | {'https://www.openmicroscopy.org/security/advisories/2021-SV3/', 'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424', 'https://github.com/ome/omero-web/security/advisories/GHSA-g67g-hvc3-xmvf'} | null |
PyPI | PYSEC-2019-41 | null | psutil (aka python-psutil) through 5.6.5 can have a double free. This occurs because of refcount mishandling within a while or for loop that converts system data into a Python object. | {'GHSA-qfc5-mcwq-26q8', 'CVE-2019-18874'} | 2019-11-18T21:15:00Z | 2019-11-12T02:15:00Z | null | null | null | {'https://github.com/giampaolo/psutil/pull/1616', 'https://lists.debian.org/debian-lts-announce/2019/11/msg00018.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2P7QI7MOTZTFXQYU23CP3RAWXCERMOAS/', 'https://usn.ubuntu.com/4204-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/OLETTJYZL2SMBUI4Q2NGBMGPDPP54SRG/', 'https://github.com/advisories/GHSA-qfc5-mcwq-26q8'} | null |
PyPI | GHSA-mcg6-h362-cmq5 | Improper Authorization in cobbler | ### Impact
If PAM is correctly configured and a user account is set to expired, the expired user-account is still able to successfully log into Cobbler in all places (Web UI, CLI & XMLRPC-API).
The same applies to user accounts with passwords set to be expired.
### Patches
There is a patch for the latest Cobbler `3.3.2` available, however a backport will be done for `3.2.x`.
### Workarounds
- Delete expired accounts which are able to access Cobbler via PAM.
- Use `chage -l <username>` to lock the account. If the account has SSH-Keys attached then remove them completely.
### References
- Originally discovered by @ysf at https://www.huntr.dev/bounties/c458b868-63df-414e-af10-47e3745caa1d/
### How to test if my Cobbler instance is affected?
The following `pytest` test assumes that your PAM setup is correct. In case the added user is not able to login, this test does not make sense to be executed.
```python
def test_pam_login_with_expired_user():
# Arrange
# create pam testuser
test_username = "expired_user"
test_password = "password"
test_api = CobblerAPI()
subprocess_1 = subprocess.run(
["perl", "-e", "'print crypt(\"%s\", \"%s\")'" % (test_username, test_password)],
stdout=subprocess.PIPE
)
subprocess.run(["useradd", "-p", subprocess_1.stdout, test_username])
# change user to be expired
subprocess.run(["chage", "-E0", test_username])
# Act
result = pam.authenticate(test_api, test_username, test_password)
# Assert - login should fail
assert not result
```
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the Cobbler repository](https://github.com/cobbler/cobbler/issues/new/choose)
* Ask in the [Gitter/Matrix Chat](https://gitter.im/cobbler/community)
* Email us at [cobbler.project@gmail.com](mailto:cobbler.project@gmail.com)
| {'CVE-2022-0860'} | 2022-04-01T20:17:01.937311Z | 2022-03-11T20:52:04Z | HIGH | null | {'CWE-863', 'CWE-285'} | {'https://github.com/cobbler/cobbler/security/advisories/GHSA-mcg6-h362-cmq5', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DYWYHWVVRUSPCV5SWBOSAMQJQLTSBTKY/', 'https://github.com/cobbler/cobbler/commit/9044aa990a94752fa5bd5a24051adde099280bfa', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0860', 'https://huntr.dev/bounties/c458b868-63df-414e-af10-47e3745caa1d', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/IYSHMF6MEIITFAG7EJ3IQKVUN7MDV2XM/', 'https://github.com/cobbler/cobbler', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/D4KCNZYBQC2FM5SEEDRQZO4LRZ4ZECMG/'} | null |
PyPI | PYSEC-2021-688 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.IRFFT`. 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-2021-29562', 'GHSA-36vm-xw34-x4pj'} | 2021-12-09T06:35:25.478301Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1c56f53be0b722ca657cbc7df461ed676c8642a2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-36vm-xw34-x4pj'} | null |
PyPI | PYSEC-2021-722 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input 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-4vrf-ff7v-hpgr', 'CVE-2021-29596'} | 2021-12-09T06:35:31.406437Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vrf-ff7v-hpgr', 'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} | null |
PyPI | PYSEC-2011-21 | null | Cross-site scripting (XSS) vulnerability in feedparser.py in Universal Feed Parser (aka feedparser or python-feedparser) 5.x before 5.0.1 allows remote attackers to inject arbitrary web script or HTML via an unexpected URI scheme, as demonstrated by a javascript: URI. | {'GHSA-4m72-rmm9-2qjr', 'CVE-2011-1158'} | 2021-08-27T03:22:03.831291Z | 2011-04-11T18:55:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-updates/2011-04/msg00026.html', 'http://secunia.com/advisories/44074', 'http://support.novell.com/security/cve/CVE-2011-1158.html', 'http://www.mandriva.com/security/advisories?name=MDVSA-2011:082', 'https://bugzilla.novell.com/show_bug.cgi?id=680074', 'http://openwall.com/lists/oss-security/2011/03/15/11', 'http://openwall.com/lists/oss-security/2011/03/14/18', 'http://secunia.com/advisories/43730', 'https://bugzilla.redhat.com/show_bug.cgi?id=684877', 'http://www.securityfocus.com/bid/46867', 'https://github.com/advisories/GHSA-4m72-rmm9-2qjr', 'https://code.google.com/p/feedparser/issues/detail?id=255'} | null |
PyPI | GHSA-hwr7-8gxx-fj5p | Null pointer dereference in `RaggedTensorToTensor` | ### Impact
Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior:
```python
import tensorflow as tf
tf.raw_ops.RaggedTensorToTensor(
shape=1,
values=10,
default_value=21,
row_partition_tensors=tf.constant([0,0,0,0]),
row_partition_types=[])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty.
### Patches
We have patched the issue in GitHub commit [301ae88b331d37a2a16159b65b255f4f9eb39314](https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314).
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-2021-37638'} | 2022-03-03T05:14:10.600697Z | 2021-08-25T14:44:09Z | HIGH | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37638', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hwr7-8gxx-fj5p'} | null |
PyPI | PYSEC-2020-237 | null | Matrix Synapse before 1.20.0 erroneously permits non-standard NaN, Infinity, and -Infinity JSON values in fields of m.room.member events, allowing remote attackers to execute a denial of service attack against the federation and common Matrix clients. If such a malformed event is accepted into the room's state, the impact is long-lasting and is not fixed by an upgrade to a newer version, requiring the event to be manually redacted instead. Since events are replicated to servers of other room members, the impact is not constrained to the server of the event sender. | {'GHSA-4mp3-385r-v63f', 'CVE-2020-26890'} | 2021-08-27T03:22:06.477416Z | 2020-11-24T03:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/security/advisories/GHSA-4mp3-385r-v63f', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/U34DPP4ZLOEDUY2ZCWOHQPU5GA5LYNUQ/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/G7YXMMYQP46PYL664JQUXCA3LPBJU7DQ/'} | null |
PyPI | GHSA-h8pj-cxx2-jfg2 | Improper Input Validation in httpx | Encode OSS httpx <=1.0.0.beta0 is affected by improper input validation in `httpx.URL`, `httpx.Client` and some functions using `httpx.URL.copy_with`. | {'CVE-2021-41945'} | 2022-04-29T23:01:59.824535Z | 2022-04-29T00:00:25Z | MODERATE | null | {'CWE-20'} | {'https://github.com/encode/httpx/pull/2185', 'https://github.com/encode/httpx', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41945', 'https://gist.github.com/lebr0nli/4edb76bbd3b5ff993cf44f2fbce5e571', 'https://github.com/encode/httpx/issues/2184', 'https://github.com/encode/httpx/discussions/1831'} | null |
PyPI | GHSA-q8gv-q7wr-9jf8 | Segfault in Tensorflow | ### Impact
In eager mode, TensorFlow does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/session_ops.cc#L45
In the above snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault.
### Patches
We have patched the issue in 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### 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-2020-15204'} | 2022-03-03T05:13:50.625303Z | 2020-09-25T18:28:41Z | MODERATE | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15204', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8', 'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | PYSEC-2021-237 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `OneHot` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72). An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 0. 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-j8qh-3xrq-c825', 'CVE-2021-29600'} | 2021-08-27T03:22:39.194303Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qh-3xrq-c825'} | null |
PyPI | GHSA-jwqp-28gf-p498 | HTTP authentication credentials potentially leaked to target websites | ### Impact
If you use [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth) (i.e. the `http_user` and `http_pass` spider attributes) for HTTP authentication, all requests will expose your credentials to the request target.
This includes requests generated by Scrapy components, such as `robots.txt` requests sent by Scrapy when the `ROBOTSTXT_OBEY` setting is set to `True`, or as requests reached through redirects.
### Patches
Upgrade to Scrapy 2.5.1 and use the new `http_auth_domain` spider attribute to control which domains are allowed to receive the configured HTTP authentication credentials.
If you are using Scrapy 1.8 or a lower version, and upgrading to Scrapy 2.5.1 is not an option, you may upgrade to Scrapy 1.8.1 instead.
### Workarounds
If you cannot upgrade, set your HTTP authentication credentials on a per-request basis, using for example the [`w3lib.http.basic_auth_header`](https://w3lib.readthedocs.io/en/latest/w3lib.html#w3lib.http.basic_auth_header) function to convert your credentials into a value that you can assign to the `Authorization` header of your request, instead of defining your credentials globally using [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth).
### For more information
If you have any questions or comments about this advisory:
* [Open an issue](https://github.com/scrapy/scrapy/issues)
* [Email us](mailto:opensource@zyte.com)
| {'CVE-2021-41125'} | 2022-04-25T23:46:57.785253Z | 2021-10-06T17:46:22Z | MODERATE | null | {'CWE-522', 'CWE-200'} | {'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6', 'https://github.com/scrapy/scrapy', 'https://lists.debian.org/debian-lts-announce/2022/03/msg00021.html', 'https://w3lib.readthedocs.io/en/latest/w3lib.html#w3lib.http.basic_auth_header', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41125', 'https://github.com/scrapy/scrapy/security/advisories/GHSA-jwqp-28gf-p498', 'http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth'} | null |
PyPI | PYSEC-2021-667 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in `ngrams_data`(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. 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-2021-29541', 'GHSA-xqfj-35wv-m3cr'} | 2021-12-09T06:35:21.834705Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xqfj-35wv-m3cr'} | null |
PyPI | PYSEC-2013-8 | null | pip before 1.3 uses HTTP to retrieve packages from the PyPI repository, and does not perform integrity checks on package contents, which allows man-in-the-middle attackers to execute arbitrary code via a crafted response to a "pip install" operation. | {'CVE-2013-1629'} | 2021-07-05T00:01:24.339020Z | 2013-08-06T02:52:00Z | null | null | null | {'https://github.com/pypa/pip/pull/791/files', 'http://www.pip-installer.org/en/latest/news.html#changelog', 'https://github.com/pypa/pip/issues/425', 'https://bugzilla.redhat.com/show_bug.cgi?id=968059', 'http://www.pip-installer.org/en/latest/installing.html', 'http://www.reddit.com/r/Python/comments/17rfh7/warning_dont_use_pip_in_an_untrusted_network_a/'} | null |
PyPI | GHSA-ffq8-576r-v26g | High severity vulnerability that affects hpack | A HTTP/2 implementation built using any version of the Python HPACK library between v1.0.0 and v2.2.0 could be targeted for a denial of service attack, specifically a so-called "HPACK Bomb" attack. This attack occurs when an attacker inserts a header field that is exactly the size of the HPACK dynamic header table into the dynamic header table. The attacker can then send a header block that is simply repeated requests to expand that field in the dynamic table. This can lead to a gigantic compression ratio of 4,096 or better, meaning that 16kB of data can decompress to 64MB of data on the target machine. | {'CVE-2016-6581'} | 2022-03-03T05:13:58.431512Z | 2019-07-05T21:11:05Z | HIGH | null | null | {'http://www.securityfocus.com/bid/92315', 'https://github.com/advisories/GHSA-ffq8-576r-v26g', 'https://nvd.nist.gov/vuln/detail/CVE-2016-6581', 'https://python-hyper.org/hpack/en/latest/security/CVE-2016-6581.html'} | null |
PyPI | GHSA-qhxx-j73r-qpm2 | Uninitialized memory access in TensorFlow | ### Impact
Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to [default initialize the quantized floating point types in Eigen](https://github.com/tensorflow/tensorflow/blob/f70160322a579144950dff1537dcbe3c7c09d6f5/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h#L61-L104):
```cc
struct QUInt8 {
QUInt8() {}
// ...
uint8_t value;
};
struct QInt16 {
QInt16() {}
// ...
int16_t value;
};
struct QUInt16 {
QUInt16() {}
// ...
uint16_t value;
};
struct QInt32 {
QInt32() {}
// ...
int32_t value;
};
```
### Patches
We have patched the issue in GitHub commit [ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2](https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
### 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-2020-26266'} | 2022-03-03T05:13:51.980464Z | 2020-12-10T19:07:24Z | LOW | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26266', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2'} | null |
PyPI | GHSA-2hwx-mjrm-v3g8 | Denial of service attack via .well-known lookups | ### Impact
A malicious homeserver could redirect requests to their .well-known file to a large file. This can lead to a denial of service attack where homeservers will consume significantly more resources when requesting the .well-known file of a malicious homeserver.
This affects any server which accepts federation requests from untrusted servers.
### Patches
Issue is resolved by #8950. A bug not affecting the security aspects of this was fixed in #9108.
### Workarounds
The `federation_domain_whitelist` setting can be used to restrict the homeservers communicated with over federation. | {'CVE-2021-21274'} | 2022-03-03T05:13:28.871198Z | 2021-03-01T19:34:54Z | MODERATE | null | {'CWE-400'} | {'https://github.com/matrix-org/synapse/pull/8950', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21274', 'https://github.com/matrix-org/synapse/releases/tag/v1.25.0', 'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6', 'https://github.com/matrix-org/synapse', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-2hwx-mjrm-v3g8'} | null |
PyPI | PYSEC-2022-117 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation. 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-x4qx-4fjv-hmw6', 'CVE-2022-21738'} | 2022-03-09T00:18:24.751245Z | 2022-02-03T14:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273', 'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a'} | null |
PyPI | GHSA-7r94-xv9v-63jw | A use of uninitialized value vulnerability in Tensorflow | ### Impact
TensorFlow's Grappler optimizer has a [use of unitialized variable](https://github.com/tensorflow/tensorflow/blob/3457a2b122e50b4d44ceaaed5a663d635e5c22df/tensorflow/core/grappler/optimizers/auto_parallel.cc#L155-L164):
```cc
const NodeDef* dequeue_node;
for (const auto& train_node : train_nodes) {
if (IsDequeueOp(*train_node)) {
dequeue_node = train_node;
break;
}
}
if (dequeue_node) {
...
}
```
If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized.
### Patches
We have patched the issue in GitHub commit [68867bf01239d9e1048f98cbad185bf4761bedd3](https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3).
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.
### 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 Qian Feng from Baidu Security Team.
| {'CVE-2021-41225'} | 2022-03-03T05:12:47.207669Z | 2021-11-10T18:44:11Z | MODERATE | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41225'} | null |
PyPI | PYSEC-2019-158 | null | In Jupyter Notebook before 5.7.8, an open redirect can occur via an empty netloc. This issue exists because of an incomplete fix for CVE-2019-10255. | {'GHSA-rcx2-m7jp-p9wj', 'CVE-2019-10856'} | 2021-07-15T02:22:16.309397Z | 2019-04-04T16:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-rcx2-m7jp-p9wj', 'https://github.com/jupyter/notebook/compare/16cf97c...b8e30ea', 'https://blog.jupyter.org/open-redirect-vulnerability-in-jupyter-jupyterhub-adf43583f1e4'} | null |
PyPI | GHSA-vq2r-5xvm-3hc3 | Segfault in `CTCBeamSearchDecoder` | ### Impact
Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults:
```python
import tensorflow as tf
inputs = tf.constant([], shape=[18, 8, 0], dtype=tf.float32)
sequence_length = tf.constant([11, -43, -92, 11, -89, -83, -35, -100],
shape=[8], dtype=tf.int32)
beam_width = 10
top_paths = 3
merge_repeated = True
tf.raw_ops.CTCBeamSearchDecoder(
inputs=inputs, sequence_length=sequence_length, beam_width=beam_width,
top_paths=top_paths, merge_repeated=merge_repeated)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7/tensorflow/core/kernels/ctc_decoder_ops.cc#L68-L79) fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer.
### Patches
We have patched the issue in GitHub commit [b1b323042264740c398140da32e93fb9c2c9f33e](https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29581'} | 2022-03-03T05:13:03.293126Z | 2021-05-21T14:26:30Z | LOW | null | {'CWE-908'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29581', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq2r-5xvm-3hc3', 'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | null |
PyPI | PYSEC-2018-83 | null | diffoscope before 77 writes to arbitrary locations on disk based on the contents of an untrusted archive. | {'CVE-2017-0359', 'GHSA-8p5c-f328-9fvv'} | 2021-08-27T03:21:57.266779Z | 2018-04-13T16:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-8p5c-f328-9fvv', 'https://bugs.debian.org/854723', 'https://security-tracker.debian.org/tracker/CVE-2017-0359'} | null |
PyPI | PYSEC-2022-38 | null | An issue was discovered in Cobbler before 3.3.1. Files in /etc/cobbler are world readable. Two of those files contain some sensitive information that can be exposed to a local user who has non-privileged access to the server. The users.digest file contains the sha2-512 digest of users in a Cobbler local installation. In the case of an easy-to-guess password, it's trivial to obtain the plaintext string. The settings.yaml file contains secrets such as the hashed default password. | {'CVE-2021-45083', 'GHSA-5946-mpw5-pqxx'} | 2022-03-09T00:15:58.984313Z | 2022-02-20T18:15:00Z | null | null | null | {'https://www.openwall.com/lists/oss-security/2022/02/18/3', 'https://github.com/cobbler/cobbler/releases', 'https://bugzilla.suse.com/show_bug.cgi?id=1193671', 'https://github.com/advisories/GHSA-5946-mpw5-pqxx'} | null |
PyPI | GHSA-hxfw-jm98-v4mq | Divide By Zero in OpenCV. | An issue was discovered in OpenCV 4.1.0 (OpenCV-Python 4.1.0.25). There is a divide-by-zero error in cv::HOGDescriptor::getDescriptorSize in modules/objdetect/src/hog.cpp. | {'CVE-2019-15939'} | 2022-03-03T05:12:53.464356Z | 2021-10-12T22:21:56Z | MODERATE | null | {'CWE-369'} | {'https://github.com/opencv/opencv/pull/15382', 'https://lists.debian.org/debian-lts-announce/2021/10/msg00028.html', 'https://github.com/OpenCV/opencv/issues/15287', 'http://lists.opensuse.org/opensuse-security-announce/2019-12/msg00025.html', 'https://nvd.nist.gov/vuln/detail/CVE-2019-15939', 'https://github.com/opencv/opencv-python'} | null |
PyPI | PYSEC-2021-53 | null | An issue was discovered in through SaltStack Salt before 3002.5. salt.modules.cmdmod can log credentials to the info or error log level. | {'CVE-2021-25284'} | 2021-03-31T14:15:00Z | 2021-02-27T05:15:00Z | null | null | null | {'https://github.com/saltstack/salt/releases', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YOGNT2XWPOYV7YT75DN7PS4GIYWFKOK5/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/FUGLOJ6NXLCIFRD2JTXBYQEMAEF2B6XH/', 'https://saltproject.io/security_announcements/active-saltstack-cve-release-2021-feb-25/', 'https://security.gentoo.org/glsa/202103-01', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/7GRVZ5WAEI3XFN2BDTL6DDXFS5HYSDVB/'} | null |
PyPI | GHSA-7r7m-5h27-29hp | Potential infinite loop in Pillow | An issue was discovered in Pillow before 8.2.0. For FLI data, FliDecode did not properly check that the block advance was non-zero, potentially leading to an infinite loop on load. | {'CVE-2021-28676'} | 2022-03-03T05:13:18.846871Z | 2021-06-08T18:48:53Z | HIGH | null | {'CWE-835'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-28676', 'https://lists.debian.org/debian-lts-announce/2021/07/msg00018.html', 'https://security.gentoo.org/glsa/202107-33', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/', 'https://github.com/python-pillow/Pillow', 'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-28676-fix-fli-dos', 'https://github.com/python-pillow/Pillow/pull/5377'} | null |
PyPI | PYSEC-2021-309 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. 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-jwf9-w5xm-f437', 'CVE-2021-37687'} | 2021-08-27T03:22:47.431884Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jwf9-w5xm-f437', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d', 'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f'} | null |
PyPI | GHSA-qm57-vhq3-3fwf | Header injection possible in Django | In Django 2.2 before 2.2.22, 3.1 before 3.1.10, and 3.2 before 3.2.2 (with Python 3.9.5+), URLValidator does not prohibit newlines and tabs (unless the URLField form field is used). If an application uses values with newlines in an HTTP response, header injection can occur. Django itself is unaffected because HttpResponse prohibits newlines in HTTP headers. | {'CVE-2021-32052'} | 2022-03-03T05:12:23.541212Z | 2021-06-09T17:14:51Z | MODERATE | null | {'CWE-88', 'CWE-79'} | {'https://groups.google.com/forum/#!forum/django-announce', 'https://docs.djangoproject.com/en/3.2/releases/security/', 'http://www.openwall.com/lists/oss-security/2021/05/06/1', 'https://github.com/django/django/commit/e1e81aa1c4427411e3c68facdd761229ffea6f6f', 'https://security.netapp.com/advisory/ntap-20210611-0002/', 'https://www.djangoproject.com/weblog/2021/may/06/security-releases/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZVKYPHR3TKR2ESWXBPOJEKRO2OSJRZUE/', 'https://bugzilla.redhat.com/show_bug.cgi?id=1944801', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32052'} | null |
PyPI | GHSA-rhm9-p9w5-fwm7 | Symmetrically encrypting large values can lead to integer overflow | cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. When certain sequences of `update()` calls with large values (multiple GBs) for symetric encryption or decryption occur, it's possible for an integer overflow to happen, leading to mishandling of buffers. This is patched in version 3.3.2 and newer.
| {'CVE-2020-36242'} | 2022-04-22T18:45:09.196238Z | 2021-02-10T01:32:27Z | CRITICAL | null | {'CWE-190', 'CWE-787'} | {'https://github.com/pyca/cryptography/issues/5615', 'https://github.com/pyca/cryptography/compare/3.3.1...3.3.2', 'https://github.com/pyca/cryptography/blob/master/CHANGELOG.rst', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/L7RGQLK4J5ZQFRLKCHVVG6BKZTUQMG7E/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-36242', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://github.com/pyca/cryptography', 'https://github.com/pyca/cryptography/security/advisories/GHSA-rhm9-p9w5-fwm7', 'https://github.com/pyca/cryptography/commit/82b6ce28389f0a317bc55ba2091a74b346db7cae'} | null |
PyPI | GHSA-93xj-8mrv-444m | Regular Expression Denial of Service (REDoS) in httplib2 | ### Impact
A malicious server which responds with long series of `\xa0` characters in the `www-authenticate` header may cause Denial of Service (CPU burn while parsing header) of the httplib2 client accessing said server.
### Patches
Version 0.19.0 contains new implementation of auth headers parsing, using pyparsing library.
https://github.com/httplib2/httplib2/pull/182
### Workarounds
```py
import httplib2
httplib2.USE_WWW_AUTH_STRICT_PARSING = True
```
### Technical Details
The vulnerable regular expression is https://github.com/httplib2/httplib2/blob/595e248d0958c00e83cb28f136a2a54772772b50/python3/httplib2/__init__.py#L336-L338
The section before the equals sign contains multiple overlapping groups. Ignoring the optional part containing a comma, we have:
\s*[^ \t\r\n=]+\s*=
Since all three infinitely repeating groups accept the non-breaking space character `\xa0`, a long string of `\xa0` causes catastrophic backtracking.
The complexity is cubic, so doubling the length of the malicious string of `\xa0` makes processing take 8 times as long.
### Reproduction Steps
Run a malicious server which responds with
www-authenticate: x \xa0\xa0\xa0\xa0x
but with many more `\xa0` characters.
An example malicious python server is below:
```py
from http.server import BaseHTTPRequestHandler, HTTPServer
def make_header_value(n_spaces):
repeat = "\xa0" * n_spaces
return f"x {repeat}x"
class Handler(BaseHTTPRequestHandler):
def do_GET(self):
self.log_request(401)
self.send_response_only(401) # Don't bother sending Server and Date
n_spaces = (
int(self.path[1:]) # Can GET e.g. /100 to test shorter sequences
if len(self.path) > 1 else
65512 # Max header line length 65536
)
value = make_header_value(n_spaces)
self.send_header("www-authenticate", value) # This header can actually be sent multiple times
self.end_headers()
if __name__ == "__main__":
HTTPServer(("", 1337), Handler).serve_forever()
```
Connect to the server with httplib2:
```py
import httplib2
httplib2.Http(".cache").request("http://localhost:1337", "GET")
```
To benchmark performance with shorter strings, you can set the path to a number e.g. http://localhost:1337/1000
### References
Thanks to [Ben Caller](https://github.com/b-c-ds) ([Doyensec](https://doyensec.com)) for finding vulnerability and discrete notification.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [httplib2](https://github.com/httplib2/httplib2/issues/new)
* Email [current maintainer at 2021-01](mailto:temotor@gmail.com) | {'CVE-2021-21240'} | 2022-03-03T05:13:25.212878Z | 2021-02-08T19:41:59Z | LOW | null | {'CWE-400'} | {'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc', 'https://pypi.org/project/httplib2', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21240', 'https://github.com/httplib2/httplib2/pull/182', 'https://github.com/httplib2/httplib2/security/advisories/GHSA-93xj-8mrv-444m'} | null |
PyPI | PYSEC-2021-759 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. 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-2021-37648', 'GHSA-wp77-4gmm-7cq8'} | 2021-12-09T06:35:36.478576Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp77-4gmm-7cq8', 'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | null |
PyPI | GHSA-6r3c-8xf3-ggrr | Directory traversal outside of SENDFILE_ROOT in django-sendfile2 | django-sendfile2 currently relies on the backend to correctly limit file paths to `SENDFILE_ROOT`. This is not the case for the `simple` and `development` backends, it is also not necessarily the case for any of the other backends either (it's just an assumption that was made by the original author).
This will be fixed in 0.6.0 which is to be released the same day as this advisory is made public.
When upgrading, you will need to make sure `SENDFILE_ROOT` is set in your settings module if it wasn't already. | null | 2022-03-03T05:14:17.489854Z | 2020-06-24T17:15:26Z | MODERATE | null | {'CWE-22'} | {'https://github.com/moggers87/django-sendfile2', 'https://github.com/moggers87/django-sendfile2/commit/f870c52398a55b9b5189932dd8caa24efb4bc1e1', 'https://github.com/moggers87/django-sendfile2/security/advisories/GHSA-6r3c-8xf3-ggrr'} | null |
PyPI | PYSEC-2015-19 | null | The session.flush function in the cached_db backend in Django 1.8.x before 1.8.2 does not properly flush the session, which allows remote attackers to hijack user sessions via an empty string in the session key. | {'CVE-2015-3982'} | 2021-07-15T02:22:09.577532Z | 2015-06-02T14:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/74960', 'https://www.djangoproject.com/weblog/2015/may/20/security-release/'} | null |
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