ecosystem stringclasses 14 values | vuln_id stringlengths 10 19 | summary stringlengths 4 267 ⌀ | details stringlengths 9 13.5k | aliases stringlengths 17 144 ⌀ | modified_date stringdate 2010-05-27 05:47:00 2022-05-10 08:46:52 | published_date stringdate 2005-12-31 05:00:00 2022-05-10 08:46:50 | severity stringclasses 5 values | score float64 0 10 ⌀ | cwe_id stringclasses 988 values | refs stringlengths 30 17.7k ⌀ | introduced stringlengths 75 4.26k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2020-275 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-63xm-rx5p-xvqr', 'CVE-2020-15195'} | 2021-12-09T06:34:41.380854Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr'} | null |
PyPI | GHSA-r8wq-qrxc-hmcm | ReDoS in LDAP schema parser | https://github.com/python-ldap/python-ldap/issues/424
### Impact
The LDAP schema parser of python-ldap 3.3.1 and earlier are vulnerable to a regular expression denial-of-service attack. The issue affects clients that use ``ldap.schema`` package to parse LDAP schema definitions from an untrusted source.
### Patches
The upcoming release of python-ldap 3.4.0 will contain a workaround to prevent ReDoS attacks. The schema parser refuses schema definitions with an excessive amount of backslashes.
### Workarounds
As a workaround, users can check input for excessive amount of backslashes in schemas. More than a dozen backslashes per line are atypical.
### References
[CWE-1333](https://cwe.mitre.org/data/definitions/1333.html)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [python-ldap](https://github.com/python-ldap/python-ldap) tracker
| null | 2022-03-03T05:13:34.440718Z | 2021-11-29T17:58:59Z | MODERATE | null | {'CWE-1333'} | {'https://github.com/python-ldap/python-ldap/security/advisories/GHSA-r8wq-qrxc-hmcm', 'https://github.com/python-ldap/python-ldap/issues/424', 'https://github.com/python-ldap/python-ldap'} | null |
PyPI | GHSA-939m-4xpw-v34v | Arbitrary Code Execution in blazar-dashboard | An issue was discovered in OpenStack blazar-dashboard before 1.3.1, 2.0.0, and 3.0.0. A user allowed to access the Blazar dashboard in Horizon may trigger code execution on the Horizon host as the user the Horizon service runs under (because the Python eval function is used). This may result in Horizon host unauthorized access and further compromise of the Horizon service. All setups using the Horizon dashboard with the blazar-dashboard plugin are affected. | {'CVE-2020-26943'} | 2022-03-03T05:13:02.960113Z | 2020-10-27T17:55:04Z | MODERATE | null | {'CWE-94'} | {'https://review.opendev.org/755810', 'https://security.openstack.org/ossa/OSSA-2020-007.html', 'https://review.opendev.org/755812', 'https://launchpad.net/bugs/1895688', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26943', 'https://review.opendev.org/755813', 'http://www.openwall.com/lists/oss-security/2020/10/16/5', 'https://review.opendev.org/755814', 'https://review.opendev.org/756064'} | null |
PyPI | PYSEC-2019-233 | null | Google TensorFlow 1.7 and below is affected by: Buffer Overflow. The impact is: execute arbitrary code (local). | {'CVE-2018-8825'} | 2021-12-09T06:35:11.845396Z | 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-2015-20 | null | The session backends in Django before 1.4.21, 1.5.x through 1.6.x, 1.7.x before 1.7.9, and 1.8.x before 1.8.3 allows remote attackers to cause a denial of service (session store consumption) via multiple requests with unique session keys. | {'GHSA-h582-2pch-3xv3', 'CVE-2015-5143'} | 2021-08-11T21:51:02.776258Z | 2015-07-14T17:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/75666', 'http://rhn.redhat.com/errata/RHSA-2015-1686.html', 'http://www.securitytracker.com/id/1032820', 'http://www.debian.org/security/2015/dsa-3305', 'http://www.ubuntu.com/usn/USN-2671-1', 'https://github.com/advisories/GHSA-h582-2pch-3xv3', 'http://www.oracle.com/technetwork/topics/security/bulletinoct2015-2511968.html', 'https://www.djangoproject.com/weblog/2015/jul/08/security-releases/', 'http://lists.opensuse.org/opensuse-updates/2015-10/msg00043.html', 'http://rhn.redhat.com/errata/RHSA-2015-1678.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-November/172084.html', 'https://security.gentoo.org/glsa/201510-06', 'http://lists.opensuse.org/opensuse-updates/2015-10/msg00046.html'} | null |
PyPI | PYSEC-2020-330 | null | In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | {'GHSA-rrfp-j2mp-hq9c', 'CVE-2020-15265'} | 2021-12-09T06:35:15.737663Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c', 'https://github.com/tensorflow/tensorflow/issues/42105', 'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808'} | null |
PyPI | PYSEC-2021-85 | null | Plone through 5.2.4 allows XSS via the inline_diff methods in Products.CMFDiffTool. | {'CVE-2021-33513', 'GHSA-fj67-w3m4-rfmp'} | 2021-06-09T05:00:54.946731Z | 2021-05-21T22:15:00Z | null | null | null | {'https://plone.org/security/hotfix/20210518/xss-vulnerability-in-cmfdifftool', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'https://github.com/advisories/GHSA-fj67-w3m4-rfmp'} | null |
PyPI | PYSEC-2021-330 | null | Due to use of unsafe YAML deserialization logic, an attacker with the ability to modify local YAML configuration files could provide malicious input, resulting in remote code execution or similar risks. This issue affects ParlAI prior to v1.1.0. | {'GHSA-m87f-9fvv-2mgg', 'CVE-2021-24040', 'GHSA-mwgj-7x7j-6966'} | 2021-09-23T00:11:05.455785Z | 2021-09-10T22:15:00Z | null | null | null | {'http://packetstormsecurity.com/files/164136/Facebook-ParlAI-1.0.0-Code-Execution-Deserialization.html', 'https://github.com/facebookresearch/ParlAI/security/advisories/GHSA-m87f-9fvv-2mgg', 'https://github.com/advisories/GHSA-mwgj-7x7j-6966', 'https://github.com/facebookresearch/ParlAI/releases/tag/v1.1.0'} | null |
PyPI | PYSEC-2021-760 | null | TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37649', 'GHSA-6gv8-p3vj-pxvr'} | 2021-12-09T06:35:36.563048Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gv8-p3vj-pxvr', 'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} | null |
PyPI | GHSA-g5j6-r3x9-gf2m | Cross-site scripting in Contentful | Contentful through 2020-05-21 for Python allows reflected XSS, as demonstrated by the api parameter to the-example-app.py. | {'CVE-2020-13258'} | 2022-03-03T05:14:17.417572Z | 2021-06-18T18:32:20Z | MODERATE | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-13258', 'https://github.com/contentful/the-example-app.py/issues/44'} | null |
PyPI | PYSEC-2020-47 | null | In Indy Node 1.12.2, there is an Uncontrolled Resource Consumption vulnerability. Indy Node has a bug in TAA handling code. The current primary can be crashed with a malformed transaction from a client, which leads to a view change. Repeated rapid view changes have the potential of bringing down the network. This is fixed in version 1.12.3. | {'GHSA-3gw4-m5w7-v89c', 'CVE-2020-11090'} | 2020-06-22T16:36:00Z | 2020-06-11T00:15:00Z | null | null | null | {'https://github.com/hyperledger/indy-node/blob/master/CHANGELOG.md#1123', 'https://github.com/hyperledger/indy-node/security/advisories/GHSA-3gw4-m5w7-v89c', 'https://pypi.org/project/indy-node/1.12.3/'} | null |
PyPI | PYSEC-2021-449 | null | TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. 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. | {'CVE-2021-29521', 'GHSA-hr84-fqvp-48mm'} | 2021-12-09T06:34:46.609278Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm', 'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} | null |
PyPI | PYSEC-2022-155 | null | Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`. This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. 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-2022-23591', 'GHSA-247x-2f9f-5wp7'} | 2022-03-09T00:18:29.944139Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7', 'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c'} | null |
PyPI | PYSEC-2016-30 | null | MoinMoin 1.9.8 allows remote attackers to conduct "JavaScript injection" attacks by using the "page creation or crafted URL" approach, related to a "Cross Site Scripting (XSS)" issue affecting the action=fckdialog&dialog=attachment (via page name) component. | {'CVE-2016-7146'} | 2021-08-27T03:22:07.777797Z | 2016-11-10T17:59:00Z | null | null | null | {'http://www.debian.org/security/2016/dsa-3715', 'https://www.curesec.com/blog/article/blog/MoinMoin-198-XSS-175.html', 'http://www.securityfocus.com/bid/94259', 'http://www.ubuntu.com/usn/USN-3137-1'} | null |
PyPI | GHSA-m648-33qf-v3gp | CHECK-fail in LSTM with zero-length input in TensorFlow | ### Impact
Running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a `CHECK` failure when using the CUDA backend.
This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.
### Patches
We have patched the issue in GitHub commit [14755416e364f17fb1870882fa778c7fec7f16e3](https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3) 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-2020-26270'} | 2022-03-03T05:12:46.001558Z | 2020-12-10T19:07:31Z | LOW | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26270', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m648-33qf-v3gp'} | null |
PyPI | GHSA-gh6x-4whr-2qv4 | Null pointer dereference and heap OOB read in operations restoring tensors | ### Impact
When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer:
```python
import tensorflow as tf
tf.raw_ops.Restore(
file_pattern=['/tmp'],
tensor_name=[],
default_value=21,
dt=tf.int,
preferred_shard=1)
```
The same undefined behavior can be triggered by `tf.raw_ops.RestoreSlice`:
```python
import tensorflow as tf
tf.raw_ops.RestoreSlice(
file_pattern=['/tmp'],
tensor_name=[],
shape_and_slice='2',
dt=inp.array([tf.int]),
preferred_shard=1)
```
Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration:
```python
import tensorflow as tf
tf.raw_ops.Restore(
file_pattern=['/tmp'],
tensor_name=['x'],
default_value=21,
dt=tf.int,
preferred_shard=42)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values.
If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read.
### Patches
We have patched the issue in GitHub commit [9e82dce6e6bd1f36a57e08fa85af213e2b2f2622](https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622).
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-2021-37639'} | 2022-04-26T18:17:07.858464Z | 2021-08-25T14:44:05Z | HIGH | null | {'CWE-476', 'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37639', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh6x-4whr-2qv4'} | null |
PyPI | GHSA-gfv6-cj92-g3hx | Moderate severity vulnerability that affects pykmip | OpenKMIP PyKMIP version All versions before 0.8.0 contains a CWE 399: Resource Management Errors (similar issue to CVE-2015-5262) vulnerability in PyKMIP server that can result in DOS: the server can be made unavailable by one or more clients opening all of the available sockets. This attack appear to be exploitable via A client or clients open sockets with the server and then never close them. This vulnerability appears to have been fixed in 0.8.0. | {'CVE-2018-1000872'} | 2022-03-03T05:12:45.582837Z | 2018-12-21T17:46:39Z | MODERATE | null | {'CWE-400'} | {'https://nvd.nist.gov/vuln/detail/CVE-2018-1000872', 'https://github.com/OpenKMIP/PyKMIP/issues/430', 'https://github.com/OpenKMIP/PyKMIP', 'https://github.com/advisories/GHSA-gfv6-cj92-g3hx'} | null |
PyPI | PYSEC-2022-95 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. 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-2022-23586', 'GHSA-43jf-985q-588j'} | 2022-03-09T00:17:35.674710Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43jf-985q-588j', 'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/function.cc', 'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645'} | null |
PyPI | GHSA-4vf2-4xcg-65cx | Division by 0 in `Conv2D` | ### Impact
An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`:
```python
import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller:
```cc
const int64 patch_depth = filter.dim_size(2);
if (in_depth % patch_depth != 0) { ... }
```
### Patches
We have patched the issue in GitHub commit [b12aa1d44352de21d1a6faaf04172d8c2508b42b](https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b).
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-2021-29526'} | 2022-03-03T05:14:16.972688Z | 2021-05-21T14:21:55Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vf2-4xcg-65cx', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29526', 'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b'} | null |
PyPI | PYSEC-2008-8 | null | common.py in Paramiko 1.7.1 and earlier, when using threads or forked processes, does not properly use RandomPool, which allows one session to obtain sensitive information from another session by predicting the state of the pool. | {'CVE-2008-0299'} | 2021-08-27T03:22:10.050129Z | 2008-01-16T23:00:00Z | null | null | null | {'http://security.gentoo.org/glsa/glsa-200803-07.xml', 'https://www.redhat.com/archives/fedora-package-announce/2008-January/msg00529.html', 'http://secunia.com/advisories/28510', 'http://secunia.com/advisories/29168', 'http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=460706', 'https://www.redhat.com/archives/fedora-package-announce/2008-January/msg00594.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=428727', 'http://www.securityfocus.com/bid/27307', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/39749', 'http://secunia.com/advisories/28488', 'http://people.debian.org/~nion/nmu-diff/paramiko-1.6.4-1_1.6.4-1.1.patch', 'http://www.lag.net/pipermail/paramiko/2008-January/000599.html'} | null |
PyPI | PYSEC-2020-263 | null | A flaw was found in Django REST Framework versions before 3.12.0 and before 3.11.2. When using the browseable API viewer, Django REST Framework fails to properly escape certain strings that can come from user input. This allows a user who can control those strings to inject malicious <script> tags, leading to a cross-site-scripting (XSS) vulnerability. | {'GHSA-fx83-3ph3-9j2q', 'CVE-2020-25626'} | 2021-11-16T03:58:43.874175Z | 2020-09-30T20:15:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=1878635', 'https://github.com/advisories/GHSA-fx83-3ph3-9j2q', 'https://security.netapp.com/advisory/ntap-20201016-0003/'} | null |
PyPI | PYSEC-2021-633 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `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. | {'GHSA-rg3m-hqc5-344v', 'CVE-2021-41224'} | 2021-12-09T06:35:10.967537Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v', 'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | null |
PyPI | GHSA-j8j8-348v-wfm3 | High severity vulnerability that affects python-saml | OneLogin PythonSAML 2.3.0 and earlier may incorrectly utilize the results of XML DOM traversal and canonicalization APIs in such a way that an attacker may be able to manipulate the SAML data without invalidating the cryptographic signature, allowing the attack to potentially bypass authentication to SAML service providers. | {'CVE-2017-11427'} | 2022-03-03T05:14:18.944895Z | 2019-07-05T21:11:26Z | HIGH | null | {'CWE-287'} | {'https://www.kb.cert.org/vuls/id/475445', 'https://duo.com/blog/duo-finds-saml-vulnerabilities-affecting-multiple-implementations', 'https://github.com/advisories/GHSA-j8j8-348v-wfm3', 'https://nvd.nist.gov/vuln/detail/CVE-2017-11427'} | null |
PyPI | PYSEC-2022-9 | null | path_getbbox in path.c in Pillow before 9.0.0 has a buffer over-read during initialization of ImagePath.Path. | {'CVE-2022-22816', 'GHSA-xrcv-f9gm-v42c'} | 2022-01-24T23:48:19.735368Z | 2022-01-10T14:12:00Z | null | null | null | {'https://github.com/advisories/GHSA-xrcv-f9gm-v42c', 'https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html#fixed-imagepath-path-array-handling', 'https://lists.debian.org/debian-lts-announce/2022/01/msg00018.html', 'https://github.com/python-pillow/Pillow/blob/c5d9223a8b5e9295d15b5a9b1ef1dae44c8499f3/src/path.c#L331'} | null |
PyPI | PYSEC-2021-263 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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-2021-37641', 'GHSA-9c8h-vvrj-w2p8'} | 2021-08-27T03:22:43.190554Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8'} | null |
PyPI | PYSEC-2021-799 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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-vcjj-9vg7-vf68', 'CVE-2021-37688'} | 2021-12-09T06:35:40.029733Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vcjj-9vg7-vf68'} | null |
PyPI | GHSA-gwr8-5j83-483c | OS Command Injection and Improper Input Validation in ansible | A flaw was found in the solaris_zone module from the Ansible Community modules. When setting the name for the zone on the Solaris host, the zone name is checked by listing the process with the 'ps' bare command on the remote machine. An attacker could take advantage of this flaw by crafting the name of the zone and executing arbitrary commands in the remote host. Ansible Engine 2.7.15, 2.8.7, and 2.9.2 as well as previous versions are affected. | {'CVE-2019-14904'} | 2022-04-26T18:01:58.836525Z | 2021-04-20T16:44:22Z | HIGH | null | {'CWE-20', 'CWE-78'} | {'https://github.com/ansible/ansible/pull/65686', 'https://bugzilla.redhat.com/show_bug.cgi?id=1776944', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14904', 'https://github.com/ansible/ansible', 'https://lists.debian.org/debian-lts-announce/2021/01/msg00023.html', 'https://www.debian.org/security/2021/dsa-4950'} | null |
PyPI | PYSEC-2021-776 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. 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-2021-37665', 'GHSA-v82p-hv3v-p6qp'} | 2021-12-09T06:35:37.987590Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp', 'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9'} | null |
PyPI | PYSEC-2019-15 | null | Django 2.1 before 2.1.15 and 2.2 before 2.2.8 allows unintended model editing. A Django model admin displaying inline related models, where the user has view-only permissions to a parent model but edit permissions to the inline model, would be presented with an editing UI, allowing POST requests, for updating the inline model. Directly editing the view-only parent model was not possible, but the parent model's save() method was called, triggering potential side effects, and causing pre and post-save signal handlers to be invoked. (To resolve this, the Django admin is adjusted to require edit permissions on the parent model in order for inline models to be editable.) | {'CVE-2019-19118', 'GHSA-hvmf-r92r-27hr'} | 2020-05-01T02:15:00Z | 2019-12-02T14:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6R4HD22PVEVQ45H2JA2NXH443AYJOPL5/', 'http://www.openwall.com/lists/oss-security/2019/12/02/1', 'https://security.gentoo.org/glsa/202004-17', 'https://security.netapp.com/advisory/ntap-20191217-0003/', 'https://www.djangoproject.com/weblog/2019/dec/02/security-releases/', 'https://groups.google.com/forum/#!topic/django-announce/GjGqDvtNmWQ', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://github.com/advisories/GHSA-hvmf-r92r-27hr'} | null |
PyPI | PYSEC-2021-326 | null | The variable import endpoint was not protected by authentication in Airflow >=2.0.0, <2.1.3. This allowed unauthenticated users to hit that endpoint to add/modify Airflow variables used in DAGs, potentially resulting in a denial of service, information disclosure or remote code execution. This issue affects Apache Airflow >=2.0.0, <2.1.3. | {'CVE-2021-38540'} | 2021-09-21T14:26:17.443988Z | 2021-09-09T15:15:00Z | null | null | null | {'https://lists.apache.org/thread.html/rb34c3dd1a815456355217eef34060789f771b6f77c3a3dec77de2064%40%3Cusers.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/rac2ed9118f64733e47b4f1e82ddc8c8020774698f13328ca742b03a2@%3Cannounce.apache.org%3E'} | null |
PyPI | PYSEC-2021-93 | null | An issue was discovered in Pillow before 8.2.0. For EPS data, the readline implementation used in EPSImageFile has to deal with any combination of \r and \n as line endings. It used an accidentally quadratic method of accumulating lines while looking for a line ending. A malicious EPS file could use this to perform a DoS of Pillow in the open phase, before an image was accepted for opening. | {'CVE-2021-28677', 'GHSA-q5hq-fp76-qmrc'} | 2021-06-09T05:00:59.042287Z | 2021-06-02T16:15:00Z | null | null | null | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-28677-fix-eps-dos-on-open', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/', 'https://github.com/advisories/GHSA-q5hq-fp76-qmrc', 'https://github.com/python-pillow/Pillow/pull/5377'} | null |
PyPI | GHSA-j3jp-gvr5-7hwq | Low severity vulnerability that affects python-engineio | ## WebSocket cross-origin vulnerability
### Impact
This is a Cross-Site Request Forgery (CSRF) vulnerability. It affects Socket.IO and Engine.IO web servers that authenticate clients using cookies.
### Patches
python-engineio version 3.9.0 patches this vulnerability by adding server-side Origin header checks.
### Workarounds
Do not use cookies for client authentication, or else add a CSRF token to the connection URL.
### References
https://www.owasp.org/index.php/Cross-Site_Request_Forgery_(CSRF)
https://www.christian-schneider.net/CrossSiteWebSocketHijacking.html
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [python-engineio](http://github.com/miguelgrinberg/python-engineio) | {'CVE-2019-13611'} | 2022-03-03T05:13:43.000454Z | 2019-07-30T20:47:25Z | HIGH | null | {'CWE-352'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-13611', 'https://github.com/miguelgrinberg/python-engineio', 'https://github.com/miguelgrinberg/python-engineio/issues/128', 'https://github.com/advisories/GHSA-j3jp-gvr5-7hwq', 'https://github.com/miguelgrinberg/python-engineio/security/advisories/GHSA-j3jp-gvr5-7hwq'} | null |
PyPI | PYSEC-2020-326 | null | In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code. | {'GHSA-cvpc-8phh-8f45', 'CVE-2020-15211'} | 2021-12-09T06:35:15.416974Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | GHSA-frxj-5j27-f8rf | Externally Controlled Reference to a Resource in Another Sphere, Improper Input Validation, and External Control of File Name or Path in Ansible | A vulnerability was found in Ansible Engine versions 2.9.x before 2.9.3, 2.8.x before 2.8.8, 2.7.x before 2.7.16 and earlier, where in Ansible's nxos_file_copy module can be used to copy files to a flash or bootflash on NXOS devices. Malicious code could craft the filename parameter to perform OS command injections. This could result in a loss of confidentiality of the system among other issues. | {'CVE-2019-14905'} | 2022-03-03T05:13:24.964357Z | 2021-04-20T16:44:49Z | MODERATE | null | {'CWE-20', 'CWE-73', 'CWE-610', 'CWE-668'} | {'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00021.html', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14905', 'https://access.redhat.com/errata/RHSA-2020:0218', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5BNCYPQ4BY5QHBCJOAOPANB5FHATW2BR/', 'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00026.html', 'https://access.redhat.com/errata/RHSA-2020:0216', 'https://github.com/ansible/ansible', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-14905'} | null |
PyPI | PYSEC-2019-225 | null | Memcpy parameter overlap in Google Snappy library 1.1.4, as used in Google TensorFlow before 1.7.1, could result in a crash or read from other parts of process memory. | {'CVE-2018-7577'} | 2021-08-27T03:22:22.362937Z | 2019-04-24T17:29:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-005.md'} | null |
PyPI | PYSEC-2022-143 | null | Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `SafeToRemoveIdentity` would trigger `CHECK` failures. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-5f2r-qp73-37mr', 'CVE-2022-23579'} | 2022-03-09T00:18:28.283580Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/dependency_optimizer.cc#L59-L98'} | null |
PyPI | GHSA-gcv9-6737-pjqw | SSRF vulnerability in jupyter-server-proxy | ### Impact
**What kind of vulnerability is it?** Server-Side Request Forgery ( SSRF )
**Who is impacted?** Any user deploying Jupyter Server or Notebook with jupyter-proxy-server extension enabled.
A lack of input validation allowed authenticated clients to proxy requests to other hosts, bypassing the `allowed_hosts` check. Because authentication is required, which already grants permissions to make the same requests via kernel or terminal execution, this is considered low to moderate severity.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
Upgrade to 3.2.1, or apply the patch https://github.com/jupyterhub/jupyter-server-proxy/compare/v3.2.0...v3.2.1.patch
### For more information
If you have any questions or comments about this advisory:
* Open a topic [on our forum](https://discourse.jupyter.org)
* Email the Jupyter security team at [security@ipython.org](mailto:security@ipython.org)
| {'CVE-2022-21697'} | 2022-03-03T05:12:40.317434Z | 2022-01-27T16:24:26Z | MODERATE | null | {'CWE-918'} | {'https://github.com/jupyterhub/jupyter-server-proxy/compare/v3.2.0...v3.2.1.patch', 'https://github.com/jupyterhub/jupyter-server-proxy/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21697', 'https://github.com/jupyterhub/jupyter-server-proxy/commit/fd31930bacd12188c448c886e0783529436b99eb', 'https://github.com/jupyterhub/jupyter-server-proxy/security/advisories/GHSA-gcv9-6737-pjqw'} | null |
PyPI | GHSA-gh8j-2pgf-x458 | Infinite Loop in rencode | The rencode package through 1.0.6 for Python allows an infinite loop in typecode decoding (such as via ;\x2f\x7f), enabling a remote attack that consumes CPU and memory. | {'CVE-2021-40839'} | 2022-03-03T05:13:50.834851Z | 2021-09-13T20:05:51Z | HIGH | null | {'CWE-835'} | {'https://github.com/aresch/rencode/pull/29', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BMVQRPDVSVZNGGX57CFKCYT3DEYO4QB6/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-40839', 'https://github.com/aresch/rencode', 'https://security.netapp.com/advisory/ntap-20211008-0001/', 'https://pypi.org/project/rencode/#history', 'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MCLETLGVM5DBX6QNHQFW6TWGO5T3DENY/', 'https://seclists.org/fulldisclosure/2021/Sep/16'} | null |
PyPI | PYSEC-2020-51 | null | In jupyterhub-kubespawner before 0.12, certain usernames will be able to craft particular server names which will grant them access to the default server of other users who have matching usernames. This has been fixed in 0.12. | {'GHSA-v7m9-9497-p9gr', 'CVE-2020-15110'} | 2020-07-22T20:28:00Z | 2020-07-17T21:15:00Z | null | null | null | {'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0', 'https://github.com/jupyterhub/kubespawner/security/advisories/GHSA-v7m9-9497-p9gr'} | null |
PyPI | PYSEC-2021-849 | null | The bluemonday sanitizer before 1.0.16 for Go, and before 0.0.8 for Python (in pybluemonday), does not properly enforce policies associated with the SELECT, STYLE, and OPTION elements. | {'GHSA-x95h-979x-cf3j', 'CVE-2021-42576'} | 2021-12-14T08:18:23.339515Z | 2021-10-18T15:15:00Z | null | null | null | {'https://docs.google.com/document/d/11SoX296sMS0XoQiQbpxc5pNxSdbJKDJkm5BDv0zrX50/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-42576', 'https://github.com/advisories/GHSA-x95h-979x-cf3j', 'https://pypi.org/project/pybluemonday'} | null |
PyPI | PYSEC-2022-83 | null | Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. 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. | {'GHSA-77gp-3h4r-6428', 'CVE-2022-23574'} | 2022-03-09T00:17:34.161202Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L81-L102', 'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-77gp-3h4r-6428'} | null |
PyPI | PYSEC-2018-38 | null | Ansible before version 2.2.0 fails to properly sanitize fact variables sent from the Ansible controller. An attacker with the ability to create special variables on the controller could execute arbitrary commands on Ansible clients as the user Ansible runs as. | {'CVE-2016-8628', 'GHSA-jg4f-jqm5-4mgq'} | 2021-07-02T02:41:33.612895Z | 2018-07-31T20:29:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2016:2778', 'https://github.com/advisories/GHSA-jg4f-jqm5-4mgq', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2016-8628', 'http://www.securityfocus.com/bid/94109'} | null |
PyPI | GHSA-h56g-v4vp-q9q6 | Cross-site Scripting in calibreweb | calibreweb prior to version 0.6.16 contains a cross-site scripting vulnerability. | {'CVE-2022-0352'} | 2022-03-03T05:12:56.408260Z | 2022-01-29T00:00:41Z | MODERATE | null | {'CWE-79'} | {'https://huntr.dev/bounties/a577ff17-2ded-4c41-84ae-6ac02440f717', 'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1', 'https://github.com/janeczku/calibre-web', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0352'} | null |
PyPI | PYSEC-2016-26 | null | Mercurial before 3.7.3 allows remote attackers to execute arbitrary code via a crafted git ext:: URL when cloning a subrepository. | {'CVE-2016-3068'} | 2021-08-27T03:22:06.833176Z | 2016-04-13T16:59:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2016-04/msg00018.html', 'http://www.debian.org/security/2016/dsa-3542', 'http://lists.opensuse.org/opensuse-security-announce/2016-04/msg00016.html', 'http://www.oracle.com/technetwork/topics/security/linuxbulletinapr2016-2952096.html', 'https://security.gentoo.org/glsa/201612-19', 'http://rhn.redhat.com/errata/RHSA-2016-0706.html', 'https://selenic.com/repo/hg-stable/rev/34d43cb85de8', 'http://www.securityfocus.com/bid/85733', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-April/181542.html', 'http://www.oracle.com/technetwork/topics/security/bulletinapr2016-2952098.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-April/181505.html', 'http://lists.opensuse.org/opensuse-security-announce/2016-04/msg00017.html', 'http://lists.opensuse.org/opensuse-security-announce/2016-04/msg00043.html', 'https://www.mercurial-scm.org/wiki/WhatsNew#Mercurial_3.7.3_.282016-3-29.29'} | null |
PyPI | PYSEC-2021-234 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SpaceToBatchNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/412c7d9bb8f8a762c5b266c9e73bfa165f29aac8/tensorflow/lite/kernels/space_to_batch_nd.cc#L82-L83). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. 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-v52p-hfjf-wg88', 'CVE-2021-29597'} | 2021-08-27T03:22:38.644851Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v52p-hfjf-wg88', 'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708'} | null |
PyPI | GHSA-p6h9-hpcg-c6gm | High severity vulnerability that affects Plone and Zope2 | Unspecified vulnerability in (1) Zope 2.12.x before 2.12.19 and 2.13.x before 2.13.8, as used in Plone 4.x and other products, and (2) PloneHotfix20110720 for Plone 3.x allows attackers to gain privileges via unspecified vectors, related to a "highly serious vulnerability." NOTE: this vulnerability exists because of an incorrect fix for CVE-2011-0720. | {'CVE-2011-2528'} | 2022-03-03T05:13:03.749125Z | 2018-07-23T19:52:02Z | HIGH | null | null | {'http://plone.org/products/plone/security/advisories/20110622', 'https://bugzilla.redhat.com/show_bug.cgi?id=718824', 'http://secunia.com/advisories/45111', 'https://nvd.nist.gov/vuln/detail/CVE-2011-2528', 'https://github.com/advisories/GHSA-p6h9-hpcg-c6gm', 'https://mail.zope.org/pipermail/zope-announce/2011-June/002260.html', 'http://secunia.com/advisories/45056', 'http://plone.org/products/plone-hotfix/releases/20110622', 'http://www.openwall.com/lists/oss-security/2011/07/12/9', 'http://www.openwall.com/lists/oss-security/2011/07/04/6'} | null |
PyPI | PYSEC-2021-664 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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-j8qc-5fqr-52fp', 'CVE-2021-29538'} | 2021-12-09T06:35:21.353144Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp'} | null |
PyPI | PYSEC-2020-234 | null | Jupyter Server before version 1.0.6 has an Open redirect vulnerability. A maliciously crafted link to a jupyter server could redirect the browser to a different website. All jupyter servers are technically affected, however, these maliciously crafted links can only be reasonably made for known jupyter server hosts. A link to your jupyter server may appear safe, but ultimately redirect to a spoofed server on the public internet. | {'GHSA-grfj-wjv9-4f9v', 'CVE-2020-26232'} | 2021-08-27T03:22:05.136094Z | 2020-11-24T21:15:00Z | null | null | null | {'https://github.com/jupyter-server/jupyter_server/blob/master/CHANGELOG.md#106---2020-11-18', 'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157', 'https://github.com/jupyter/jupyter_server/security/advisories/GHSA-grfj-wjv9-4f9v'} | null |
PyPI | GHSA-jc87-6vpp-7ff3 | Heap buffer overflow in Tensorflow | ### Impact
The `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L193-L195
Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers.
### Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 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 is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4) | {'CVE-2020-15198'} | 2021-08-26T15:11:45Z | 2020-09-25T18:28:22Z | MODERATE | null | {'CWE-119', 'CWE-122'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15198', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | null |
PyPI | GHSA-jf66-3q76-h5p5 | Tenant and Verifier might not use the same registrar data | Keylime does not enforce that the agent registrar data is the same when the tenant uses it for validation of the EK and identity quote and the verifier for validating the integrity quote. This allows an attacker to use one AK, EK pair from a real TPM to pass EK validation and give the verifier an AK of a software TPM. A successful attack breaks the entire chain of trust because a not validated AK is used by the verifier. This issue is worse if the validation happens first and then the agent gets added to the verifier because the timing is easier and the verifier does not validate the regcount entry being equal to 1. At this time, there are no known workaround. | {'CVE-2022-1053'} | 2022-05-05T16:17:52.327738Z | 2022-05-05T15:59:35Z | MODERATE | null | null | {'https://github.com/keylime/keylime/security/advisories/GHSA-jf66-3q76-h5p5', 'https://github.com/keylime/keylime'} | null |
PyPI | PYSEC-2021-721 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` 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. | {'CVE-2021-29595', 'GHSA-vf94-36g5-69v8'} | 2021-12-09T06:35:31.250576Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vf94-36g5-69v8', 'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} | null |
PyPI | PYSEC-2011-22 | null | Plone 4.1.3 and earlier computes hash values for form parameters without restricting the ability to trigger hash collisions predictably, which allows remote attackers to cause a denial of service (CPU consumption) by sending many crafted parameters. | {'CVE-2011-4462', 'GHSA-pcwm-8jc3-qxvj'} | 2021-08-27T03:22:11.387960Z | 2011-12-30T01:55:00Z | null | null | null | {'http://www.ocert.org/advisories/ocert-2011-003.html', 'http://secunia.com/advisories/47406', 'https://github.com/advisories/GHSA-pcwm-8jc3-qxvj', 'http://archives.neohapsis.com/archives/bugtraq/2011-12/0181.html', 'http://www.nruns.com/_downloads/advisory28122011.pdf', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/72018', 'http://www.kb.cert.org/vuls/id/903934'} | null |
PyPI | GHSA-98hv-qff3-8793 | Unrestricted Upload of File with Dangerous Type in django-widgy | Unrestricted Upload of File with Dangerous Type in Django-Widgy v0.8.4 allows remote attackers to execute arbitrary code via the 'image' widget in the component 'Change Widgy Page'. | {'CVE-2020-18704'} | 2022-03-03T05:13:24.988856Z | 2021-08-30T16:24:08Z | CRITICAL | null | {'CWE-434'} | {'https://github.com/fusionbox/django-widgy', 'https://github.com/fusionbox/django-widgy/issues/387', 'https://nvd.nist.gov/vuln/detail/CVE-2020-18704'} | null |
PyPI | GHSA-cq76-mxrc-vchh | Crash in `tf.math.segment_*` operations | ### Impact
The implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large.
```python
import tensorflow as tf
tf.math.segment_max(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_min(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_mean(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_sum(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_prod(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
```
This is similar to [CVE-2021-29584](https://github.com/tensorflow/tensorflow/blob/3a74f0307236fe206b046689c4d76f57c9b74eee/tensorflow/security/advisory/tfsa-2021-071.md) (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the [implementation](https://github.com/tensorflow/tensorflow/blob/dae66e518c88de9c11718cd0f8f40a0b666a90a0/tensorflow/core/kernels/segment_reduction_ops_impl.h) (both on CPU and GPU) computes the output shape using [`AddDim`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/framework/tensor_shape.cc#L395-L408). However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use [`AddDimWithStatus`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/framework/tensor_shape.cc#L410-L440).
### Patches
We have patched the issue in GitHub commit [e9c81c1e1a9cd8dd31f4e83676cab61b60658429](https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429) (merging [#51733](https://github.com/tensorflow/tensorflow/pull/51733)).
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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46888). | {'CVE-2021-41195'} | 2022-03-03T05:13:05.028321Z | 2021-11-10T19:36:50Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/issues/46888', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41195', 'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/pull/51733'} | null |
PyPI | PYSEC-2018-80 | null | aio-libs aiohttp-session contains a Session Fixation vulnerability in load_session function for RedisStorage (see: https://github.com/aio-libs/aiohttp-session/blob/master/aiohttp_session/redis_storage.py#L42) that can result in Session Hijacking. This attack appear to be exploitable via Any method that allows setting session cookies (?session=<>, or meta tags or script tags with Set-Cookie). | {'GHSA-fpwp-69xv-c67f', 'CVE-2018-1000519'} | 2021-08-27T03:21:52.874717Z | 2018-06-26T16:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-fpwp-69xv-c67f', 'https://github.com/aio-libs/aiohttp-session/issues/272', 'https://github.com/aio-libs/aiohttp-session/blob/master/aiohttp_session/redis_storage.py#L60'} | null |
PyPI | PYSEC-2021-408 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. 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-2021-41216', 'GHSA-3ff2-r28g-w7h9'} | 2021-11-13T06:52:44.644675Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9', 'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} | null |
PyPI | GHSA-5jq3-8437-x35p | Multiple cross-site scripting (XSS) vulnerabilities in Roundup | Multiple cross-site scripting (XSS) vulnerabilities in Roundup before 1.4.20 allow remote attackers to inject arbitrary web script or HTML via the (1) @ok_message or (2) @error_message parameter to issue*. | {'CVE-2012-6133'} | 2022-04-26T21:00:17.883293Z | 2022-04-23T00:40:39Z | MODERATE | null | {'CWE-79'} | {'https://bugzilla.redhat.com/show_bug.cgi?id=722672', 'https://pypi.python.org/pypi/roundup/1.4.20', 'http://hg.code.sf.net/p/roundup/code', 'http://www.openwall.com/lists/oss-security/2013/02/13/8', 'http://issues.roundup-tracker.org/issue2550724', 'http://www.openwall.com/lists/oss-security/2012/11/10/2', 'https://nvd.nist.gov/vuln/detail/CVE-2012-6133'} | null |
PyPI | PYSEC-2022-114 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalMaxPool` can be made to crash a TensorFlow process via a division by 0. 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-2022-21735', 'GHSA-87v6-crgm-2gfj'} | 2022-03-09T00:18:24.359111Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192', 'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj'} | null |
PyPI | GHSA-w4xf-2pqw-5mq7 | Reference binding to nullptr in `RaggedTensorToVariant` | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToVariant`:
```python
import tensorflow as tf
tf.raw_ops.RaggedTensorToVariant(
rt_nested_splits=[],
rt_dense_values=[1,2,3],
batched_input=True)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty.
### Patches
We have patched the issue in GitHub commit [be7a4de6adfbd303ce08be4332554dff70362612](https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612).
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-2021-37666'} | 2022-03-03T05:13:39.107184Z | 2021-08-25T14:42:13Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37666', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w4xf-2pqw-5mq7'} | null |
PyPI | GHSA-f8m6-h2c7-8h9x | Inefficient Regular Expression Complexity in nltk (word_tokenize, sent_tokenize) | ### Impact
The vulnerability is present in [`PunktSentenceTokenizer`](https://www.nltk.org/api/nltk.tokenize.punkt.html#nltk.tokenize.punkt.PunktSentenceTokenizer), [`sent_tokenize`](https://www.nltk.org/api/nltk.tokenize.html#nltk.tokenize.sent_tokenize) and [`word_tokenize`](https://www.nltk.org/api/nltk.tokenize.html#nltk.tokenize.word_tokenize). Any users of this class, or these two functions, are vulnerable to a Regular Expression Denial of Service (ReDoS) attack.
In short, a specifically crafted long input to any of these vulnerable functions will cause them to take a significant amount of execution time. The effect of this vulnerability is noticeable with the following example:
```python
from nltk.tokenize import word_tokenize
n = 8
for length in [10**i for i in range(2, n)]:
# Prepare a malicious input
text = "a" * length
start_t = time.time()
# Call `word_tokenize` and naively measure the execution time
word_tokenize(text)
print(f"A length of {length:<{n}} takes {time.time() - start_t:.4f}s")
```
Which gave the following output during testing:
```python
A length of 100 takes 0.0060s
A length of 1000 takes 0.0060s
A length of 10000 takes 0.6320s
A length of 100000 takes 56.3322s
...
```
I canceled the execution of the program after running it for several hours.
If your program relies on any of the vulnerable functions for tokenizing unpredictable user input, then we would strongly recommend upgrading to a version of NLTK without the vulnerability, or applying the workaround described below.
### Patches
The problem has been patched in NLTK 3.6.6. After the fix, running the above program gives the following result:
```python
A length of 100 takes 0.0070s
A length of 1000 takes 0.0010s
A length of 10000 takes 0.0060s
A length of 100000 takes 0.0400s
A length of 1000000 takes 0.3520s
A length of 10000000 takes 3.4641s
```
This output shows a linear relationship in execution time versus input length, which is desirable for regular expressions.
We recommend updating to NLTK 3.6.6+ if possible.
### Workarounds
The execution time of the vulnerable functions is exponential to the length of a malicious input. With other words, the execution time can be bounded by limiting the maximum length of an input to any of the vulnerable functions. Our recommendation is to implement such a limit.
### References
* The issue showcasing the vulnerability: https://github.com/nltk/nltk/issues/2866
* The pull request containing considerably more information on the vulnerability, and the fix: https://github.com/nltk/nltk/pull/2869
* The commit containing the fix: 1405aad979c6b8080dbbc8e0858f89b2e3690341
* Information on CWE-1333: Inefficient Regular Expression Complexity: https://cwe.mitre.org/data/definitions/1333.html
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [github.com/nltk/nltk](https://github.com/nltk/nltk)
* Email us at [nltk.team@gmail.com](mailto:nltk.team@gmail.com)
| {'CVE-2021-43854'} | 2022-03-03T05:13:02.317038Z | 2022-01-06T17:38:45Z | HIGH | null | {'CWE-400'} | {'https://github.com/nltk/nltk/issues/2866', 'https://github.com/nltk/nltk/pull/2869', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43854', 'https://github.com/nltk/nltk', 'https://github.com/nltk/nltk/commit/1405aad979c6b8080dbbc8e0858f89b2e3690341', 'https://github.com/nltk/nltk/security/advisories/GHSA-f8m6-h2c7-8h9x'} | null |
PyPI | GHSA-jf7h-7m85-w2v2 | Integer overflow in TFLite memory allocation | ### Impact
The TFLite code for allocating `TFLiteIntArray`s is [vulnerable to an integer overflow issue](https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27):
```cc
int TfLiteIntArrayGetSizeInBytes(int size) {
static TfLiteIntArray dummy;
return sizeof(dummy) + sizeof(dummy.data[0]) * size;
}
```
An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in [invalid value being given to `malloc`](https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52):
```cc
TfLiteIntArray* TfLiteIntArrayCreate(int size) {
TfLiteIntArray* ret = (TfLiteIntArray*)malloc(TfLiteIntArrayGetSizeInBytes(size));
ret->size = size;
return ret;
}
```
In this case, `ret->size` would dereference an invalid pointer.
### Patches
We have patched the issue in GitHub commit [7c8cc4ec69cd348e44ad6a2699057ca88faad3e5](https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29605'} | 2022-03-03T05:13:02.259909Z | 2021-05-21T14:28:22Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jf7h-7m85-w2v2', 'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29605'} | null |
PyPI | PYSEC-2019-209 | null | In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0. | {'CVE-2019-16778', 'GHSA-844w-j86r-4x2j'} | 2021-08-27T03:22:22.453759Z | 2019-12-16T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j', 'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} | null |
PyPI | GHSA-9gwq-6cwj-47h3 | Integer overflow in TFLite array creation | ### Impact
An attacker can craft a TFLite model that would cause an integer overflow [in `TfLiteIntArrayCreate`](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60):
```cc
TfLiteIntArray* TfLiteIntArrayCreate(int size) {
int alloc_size = TfLiteIntArrayGetSizeInBytes(size);
// ...
TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size);
// ...
}
```
The [`TfLiteIntArrayGetSizeInBytes`](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33) returns an `int` instead of a `size_t`:
```cc
int TfLiteIntArrayGetSizeInBytes(int size) {
static TfLiteIntArray dummy;
int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;
#if defined(_MSC_VER)
// Context for why this is needed is in http://b/189926408#comment21
computed_size -= sizeof(dummy.data[0]);
#endif
return computed_size;
}
```
An attacker can control model inputs such that `computed_size` overflows the size of `int` datatype.
### Patches
We have patched the issue in GitHub commit [a1e1511dde36b3f8aa27a6ec630838e7ea40e091](https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team. | {'CVE-2022-23558'} | 2022-03-03T05:12:41.971477Z | 2022-02-09T23:52:24Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23558', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | GHSA-pq7m-3gw7-gq5x | Execution with Unnecessary Privileges in ipython | We’d like to disclose an arbitrary code execution vulnerability in IPython that stems from IPython executing untrusted files in CWD. This vulnerability allows one user to run code as another.
Proof of concept
User1:
```
mkdir -m 777 /tmp/profile_default
mkdir -m 777 /tmp/profile_default/startup
echo 'print("stealing your private secrets")' > /tmp/profile_default/startup/foo.py
```
User2:
```
cd /tmp
ipython
```
User2 will see:
```
Python 3.9.7 (default, Oct 25 2021, 01:04:21)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.29.0 -- An enhanced Interactive Python. Type '?' for help.
stealing your private secrets
```
## Patched release and documentation
See https://ipython.readthedocs.io/en/stable/whatsnew/version8.html#ipython-8-0-1-cve-2022-21699,
Version 8.0.1, 7.31.1 for current Python version are recommended.
Version 7.16.3 has also been published for Python 3.6 users,
Version 5.11 (source only, 5.x branch on github) for older Python versions. | {'CVE-2022-21699'} | 2022-03-29T22:16:59.073233Z | 2022-01-21T18:55:30Z | HIGH | null | {'CWE-279', 'CWE-250', 'CWE-269'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DZ7LVZBB4D7KVSFNEQUBEHFO3JW6D2ZK/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21699', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/CRQRTWHYXMLDJ572VGVUZMUPEOTPM3KB/', 'https://github.com/ipython/ipython/security/advisories/GHSA-pq7m-3gw7-gq5x', 'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668', 'https://lists.debian.org/debian-lts-announce/2022/01/msg00021.html', 'https://github.com/ipython/ipython', 'https://ipython.readthedocs.io/en/stable/whatsnew/version8.html#ipython-8-0-1-cve-2022-21699'} | null |
PyPI | PYSEC-2021-50 | null | An issue was discovered in through SaltStack Salt before 3002.5. salt-api does not honor eauth credentials for the wheel_async client. Thus, an attacker can remotely run any wheel modules on the master. | {'CVE-2021-25281'} | 2021-04-01T17:15:00Z | 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/', 'http://packetstormsecurity.com/files/162058/SaltStack-Salt-API-Unauthenticated-Remote-Command-Execution.html', 'https://security.gentoo.org/glsa/202103-01', 'https://www.saltstack.com/blog/active-saltstack-cve-announced-2021-jan-21/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/7GRVZ5WAEI3XFN2BDTL6DDXFS5HYSDVB/'} | null |
PyPI | GHSA-v3f7-j968-4h5f | Division by zero in Tensorflow | ### Impact
The [estimator for the cost of some convolution operations](https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198) can be made to execute a division by 0:
```python
import tensorflow as tf
@tf.function
def test():
y=tf.raw_ops.AvgPoolGrad(
orig_input_shape=[1,1,1,1],
grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
ksize=[1,1,1,1],
strides=[1,1,1,0],
padding='VALID',
data_format='NCHW')
return y
test()
```
The function fails to check that the stride argument is stricly positive:
```cc
int64_t GetOutputSize(const int64_t input, const int64_t filter,
const int64_t stride, const Padding& padding) {
// Logic for calculating output shape is from GetWindowedOutputSizeVerbose()
// function in third_party/tensorflow/core/framework/common_shape_fns.cc.
if (padding == Padding::VALID) {
return (input - filter + stride) / stride;
} else { // SAME.
return (input + stride - 1) / stride;
}
}
```
Hence, the fix is to add a check for the stride argument to ensure it is valid.
### Patches
We have patched the issue in GitHub commit [3218043d6d3a019756607643cf65574fbfef5d7a](https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21725'} | 2022-03-03T05:12:59.852211Z | 2022-02-10T00:15:07Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21725', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198'} | null |
PyPI | GHSA-wgmx-52ph-qqcw | High severity vulnerability that affects qutebrowser | qutebrowser before version 1.4.1 is vulnerable to a cross-site request forgery flaw that allows websites to access 'qute://*' URLs. A malicious website could exploit this to load a 'qute://settings/set' URL, which then sets 'editor.command' to a bash script, resulting in arbitrary code execution. | {'CVE-2018-10895'} | 2022-03-03T05:14:17.497611Z | 2018-10-10T16:05:23Z | HIGH | null | {'CWE-352'} | {'https://nvd.nist.gov/vuln/detail/CVE-2018-10895', 'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660', 'https://github.com/advisories/GHSA-wgmx-52ph-qqcw', 'https://github.com/qutebrowser/qutebrowser'} | null |
PyPI | PYSEC-2014-9 | null | Incomplete blacklist vulnerability in the lxml.html.clean module in lxml before 3.3.5 allows remote attackers to conduct cross-site scripting (XSS) attacks via control characters in the link scheme to the clean_html function. | {'CVE-2014-3146'} | 2021-07-05T00:01:22.415943Z | 2014-05-14T19:55:00Z | null | null | null | {'http://www.securityfocus.com/bid/67159', 'http://secunia.com/advisories/58744', 'http://www.debian.org/security/2014/dsa-2941', 'http://secunia.com/advisories/58013', 'http://seclists.org/fulldisclosure/2014/Apr/319', 'http://advisories.mageia.org/MGASA-2014-0218.html', 'http://secunia.com/advisories/59008', 'http://seclists.org/fulldisclosure/2014/Apr/210', 'http://lists.opensuse.org/opensuse-updates/2014-05/msg00083.html', 'http://www.mandriva.com/security/advisories?name=MDVSA-2015:112', 'http://www.ubuntu.com/usn/USN-2217-1', 'http://lxml.de/3.3/changes-3.3.5.html', 'http://www.openwall.com/lists/oss-security/2014/05/09/7', 'https://mailman-mail5.webfaction.com/pipermail/lxml/2014-April/007128.html'} | null |
PyPI | GHSA-87cj-px37-rc3x | OS Command Injection in bikeshed | This affects the package bikeshed before 3.0.0. This can occur when an untrusted source file containing Inline Tag Command metadata is processed. When an arbitrary OS command is executed, the command output would be included in the HTML output. | {'CVE-2021-23422'} | 2022-03-03T05:13:48.495615Z | 2021-08-30T16:25:35Z | HIGH | null | {'CWE-78'} | {'https://github.com/tabatkins/bikeshed', 'https://nvd.nist.gov/vuln/detail/CVE-2021-23422', 'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd', 'https://snyk.io/vuln/SNYK-PYTHON-BIKESHED-1537646'} | null |
PyPI | PYSEC-2014-37 | null | python_scripts.py in Plone before 4.2.3 and 4.3 before beta 1 allows remote attackers to execute Python code via a crafted URL, related to "go_back." | {'CVE-2012-5495'} | 2021-09-01T08:44:29.952455Z | 2014-09-30T14:55:00Z | null | null | null | {'https://github.com/plone/Products.CMFPlone/blob/4.2.3/docs/CHANGES.txt', 'https://plone.org/products/plone/security/advisories/20121106/11', 'http://www.openwall.com/lists/oss-security/2012/11/10/1', 'https://plone.org/products/plone-hotfix/releases/20121106'} | null |
PyPI | PYSEC-2021-536 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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-rgvq-pcvf-hx75', 'CVE-2021-29608'} | 2021-12-09T06:35:00.179664Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a', 'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rgvq-pcvf-hx75'} | null |
PyPI | GHSA-f54p-f6jp-4rhr | Heap OOB in `FusedBatchNorm` kernels | ### Impact
The [implementation](https://github.com/tensorflow/tensorflow/blob/e71b86d47f8bc1816bf54d7bddc4170e47670b97/tensorflow/core/kernels/fused_batch_norm_op.cc#L1292) of `FusedBatchNorm` kernels is vulnerable to a heap OOB:
```python
import tensorflow as tf
tf.raw_ops.FusedBatchNormGrad(
y_backprop=tf.constant([i for i in range(9)],shape=(1,1,3,3),dtype=tf.float32)
x=tf.constant([i for i in range(2)],shape=(1,1,1,2),dtype=tf.float32)
scale=[1,1],
reserve_space_1=[1,1],
reserve_space_2=[1,1,1],
epsilon=1.0,
data_format='NCHW',
is_training=True)
```
### Patches
We have patched the issue in GitHub commit [aab9998916c2ffbd8f0592059fad352622f89cda](https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda).
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-2021-41223'} | 2022-03-03T05:13:06.432363Z | 2021-11-10T18:46:52Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41223', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr'} | null |
PyPI | GHSA-23hm-7w47-xw72 | Out of bounds read in Tensorflow | ### Impact
The [implementation of `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153) does not fully validate the value of `axis` and can result in heap OOB accesses:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.Dequantize(
input=tf.constant([1,1],dtype=tf.qint32),
min_range=[1.0],
max_range=[10.0],
mode='MIN_COMBINED',
narrow_range=False,
axis=2**31-1,
dtype=tf.bfloat16)
return y
test()
```
The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor:
```cc
if (axis_ > -1) {
num_slices = input.dim_size(axis_);
}
// ...
int64_t pre_dim = 1, post_dim = 1;
for (int i = 0; i < axis_; ++i) {
pre_dim *= float_output.dim_size(i);
}
for (int i = axis_ + 1; i < float_output.dims(); ++i) {
post_dim *= float_output.dim_size(i);
}
```
### Patches
We have patched the issue in GitHub commit [23968a8bf65b009120c43b5ebcceaf52dbc9e943](https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21726'} | 2022-03-03T05:13:03.797193Z | 2022-02-09T18:28:54Z | HIGH | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21726', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} | null |
PyPI | GHSA-h6h9-pphv-m266 | Topydo contains a CWE-20: Improper Input Validation vulnerability | topydo contains a CWE-20: Improper Input Validation vulnerability in ListFormatParser::parse, file topydo/lib/ListFormat.py line 292 as of d4f843dac71308b2f29a7c2cdc76f055c3841523 that can result in Injection of arbitrary bytes to the terminal, including terminal escape code sequences. This attack appear to be exploitable via The victim must open a todo.txt with at least one specially crafted line.. | {'CVE-2018-1000523'} | 2022-04-26T18:48:01.403467Z | 2018-09-13T15:47:26Z | HIGH | null | {'CWE-20'} | {'https://github.com/bram85/topydo', 'https://nvd.nist.gov/vuln/detail/CVE-2018-1000523', 'https://github.com/bram85/topydo/blob/master/topydo/lib/ListFormat.py#L292', 'https://github.com/advisories/GHSA-h6h9-pphv-m266', 'https://github.com/bram85/topydo/issues/240'} | null |
PyPI | PYSEC-2021-166 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. 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-jfp7-4j67-8r3q', 'CVE-2021-29529'} | 2021-08-27T03:22:26.519373Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q'} | null |
PyPI | PYSEC-2018-14 | null | An issue was discovered in Mayan EDMS before 3.0.2. The Cabinets app has XSS via a crafted cabinet label. | {'GHSA-5r76-cjf4-c9qx', 'CVE-2018-16406'} | 2021-06-16T00:03:23.733638Z | 2018-09-03T19:29:00Z | null | null | null | {'https://gitlab.com/mayan-edms/mayan-edms/issues/495', 'https://github.com/advisories/GHSA-5r76-cjf4-c9qx', 'https://gitlab.com/mayan-edms/mayan-edms/blob/master/HISTORY.rst', 'https://gitlab.com/mayan-edms/mayan-edms/commit/48dfc06e49c7f773749e063f8cc69c95509d1c32'} | null |
PyPI | PYSEC-2020-92 | null | A denial of service via regular expression in the py.path.svnwc component of py (aka python-py) through 1.9.0 could be used by attackers to cause a compute-time denial of service attack by supplying malicious input to the blame functionality. | {'CVE-2020-29651', 'GHSA-hj5v-574p-mj7c'} | 2021-01-05T03:15:00Z | 2020-12-09T07:15:00Z | null | null | null | {'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144', 'https://github.com/advisories/GHSA-hj5v-574p-mj7c', 'https://github.com/pytest-dev/py/issues/256', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/CHDTINIBJZ67T3W74QTBIY5LPKAXEOGR/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/AYWNYEV3FGDHPIHX4DDUDMFZ6NLCQRC4/', 'https://github.com/pytest-dev/py/pull/257'} | null |
PyPI | PYSEC-2021-865 | null | In Mozilla Bleach before 3.3.0, a mutation XSS affects users calling bleach.clean with math or svg; p or br; and style, title, noscript, script, textarea, noframes, iframe, or xmp tags with strip_comments=False. | {'GHSA-vv2x-vrpj-qqpq', 'CVE-2021-23980'} | 2022-01-05T02:16:13.001009Z | 2021-02-02T17:58:00Z | null | null | null | {'https://github.com/mozilla/bleach/security/advisories/GHSA-vv2x-vrpj-qqpq', 'ttps://bugzilla.mozilla.org/show_bug.cgi?id=1689399', 'https://advisory.checkmarx.net/advisory/CX-2021-4303', 'https://github.com/mozilla/bleach/commit/79b7a3c5e56a09d1d323a5006afa59b56162eb13'} | null |
PyPI | GHSA-q85f-69q7-55h2 | Uninitialized variable access in Tensorflow | ### Impact
The [implementation of `AssignOp`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143) can result in copying unitialized data to a new tensor. This later results in undefined behavior.
The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized.
### Patches
We have patched the issue in GitHub commit [ef1d027be116f25e25bb94a60da491c2cf55bd0b](https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
| {'CVE-2022-23573'} | 2022-03-03T05:12:43.058761Z | 2022-02-09T23:26:50Z | HIGH | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2', 'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23573', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | GHSA-rxjp-mfm9-w4wr | Path Traversal in Django | In Django 2.2 before 2.2.21, 3.1 before 3.1.9, and 3.2 before 3.2.1, MultiPartParser, UploadedFile, and FieldFile allowed directory traversal via uploaded files with suitably crafted file names. | {'CVE-2021-31542'} | 2022-03-03T05:14:17.762587Z | 2021-06-04T21:15:56Z | HIGH | null | {'CWE-434', 'CWE-22'} | {'https://groups.google.com/forum/#!forum/django-announce', 'https://docs.djangoproject.com/en/3.2/releases/security/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/B4SQG2EAF4WCI2SLRL6XRDJ3RPK3ZRDV/', 'https://lists.debian.org/debian-lts-announce/2021/05/msg00005.html', 'http://www.openwall.com/lists/oss-security/2021/05/04/3', 'https://www.djangoproject.com/weblog/2021/may/04/security-releases/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZVKYPHR3TKR2ESWXBPOJEKRO2OSJRZUE/', 'https://security.netapp.com/advisory/ntap-20210618-0001/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-31542'} | null |
PyPI | PYSEC-2019-120 | null | scapy 2.4.0 is affected by: Denial of Service. The impact is: infinite loop, resource consumption and program unresponsive. The component is: _RADIUSAttrPacketListField.getfield(self..). The attack vector is: over the network or in a pcap. both work. | {'GHSA-mpf2-q34c-fc6j', 'CVE-2019-1010142'} | 2020-08-24T17:37:00Z | 2019-07-19T16:15:00Z | null | null | null | {'https://github.com/secdev/scapy/pull/1409', 'https://github.com/secdev/scapy/pull/1409/files#diff-441eff981e466959968111fc6314fe93L1058', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/42NRPMC3NS2QVFNIXYP6WV2T3LMLLY7E/', 'http://www.securityfocus.com/bid/106674', 'https://github.com/advisories/GHSA-mpf2-q34c-fc6j', 'https://www.imperva.com/blog/scapy-sploit-python-network-tool-is-vulnerable-to-denial-of-service-dos-attack-cve-pending/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/T46XW4S5BCA3VV3JT3C5Q6LBEXSIACLN/'} | null |
PyPI | PYSEC-2022-40 | null | OnionShare is an open source tool that lets you securely and anonymously share files, host websites, and chat with friends using the Tor network. In affected versions the receive mode limits concurrent uploads to 100 per second and blocks other uploads in the same second, which can be triggered by a simple script. An adversary with access to the receive mode can block file upload for others. There is no way to block this attack in public mode due to the anonymity properties of the tor network. | {'CVE-2022-21689', 'GHSA-jh82-c5jw-pxpc'} | 2022-03-09T00:16:43.116991Z | 2022-01-18T22:15:00Z | null | null | null | {'https://github.com/onionshare/onionshare/security/advisories/GHSA-jh82-c5jw-pxpc', 'https://github.com/onionshare/onionshare/releases/tag/v2.5'} | null |
PyPI | PYSEC-2021-473 | 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 converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. 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-2021-29545', 'GHSA-hmg3-c7xj-6qwm'} | 2021-12-09T06:34:50.345149Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm'} | null |
PyPI | PYSEC-2021-189 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. 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-2021-29552', 'GHSA-jhq9-wm9m-cf89'} | 2021-08-27T03:22:30.663551Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89', 'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} | null |
PyPI | GHSA-9p9m-jm8w-94p2 | Improper Handling of Highly Compressed Data (Data Amplification) and Memory Allocation with Excessive Size Value in eventlet | ### Impact
A websocket peer may exhaust memory on Eventlet side by sending very large websocket frames. Malicious peer may exhaust memory on Eventlet side by sending highly compressed data frame.
### Patches
Version 0.31.0 restricts websocket frame to reasonable limits.
### Workarounds
Restricting memory usage via OS limits would help against overall machine exhaustion. No workaround to protect Eventlet process.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [eventlet](https://github.com/eventlet/eventlet/issues)
* Contact current maintainers. At 2021-03: temotor@gmail.com or https://t.me/temotor | {'CVE-2021-21419'} | 2022-03-03T05:13:12.676371Z | 2021-05-07T15:50:36Z | MODERATE | null | {'CWE-400'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2WJFSBPLCNSZNHYQC4QDRDFRTEZRMD2L/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21419', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/R5JZP4LZOSP7CUAM3GIRW6PIAWKH5VGB/', 'https://github.com/eventlet/eventlet/security/advisories/GHSA-9p9m-jm8w-94p2'} | null |
PyPI | PYSEC-2013-33 | null | cache.py in Suds 0.4, when tempdir is set to None, allows local users to redirect SOAP queries and possibly have other unspecified impact via a symlink attack on a cache file with a predictable name in /tmp/suds/. | {'CVE-2013-2217'} | 2021-08-27T03:22:21.834987Z | 2013-09-23T20:55:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=978696', 'http://lists.opensuse.org/opensuse-updates/2013-07/msg00062.html', 'http://www.ubuntu.com/usn/USN-2008-1', 'http://www.openwall.com/lists/oss-security/2013/06/27/8'} | null |
PyPI | GHSA-3q6g-vf58-7m4g | Regular Expression Denial of Service in flask-restx | Flask RESTX contains a regular expression that is vulnerable to [ReDoS](https://owasp.org/www-community/attacks/Regular_expression_Denial_of_Service_-_ReDoS) (Regular Expression Denial of Service) in `email_regex`.
| {'CVE-2021-32838'} | 2022-03-03T05:13:37.106645Z | 2021-09-08T15:41:15Z | HIGH | null | {'CWE-400'} | {'https://github.com/python-restx/flask-restx/issues/372', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QUD6SWZLX52AAZUHDETJ2CDMQGEPGFL3/', 'https://github.com/python-restx/flask-restx', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5UCTFVDU3677B5OBGK4EF5NMUPJLL6SQ/', 'https://github.com/advisories/GHSA-3q6g-vf58-7m4g', 'https://github.com/python-restx/flask-restx/security/advisories/GHSA-3q6g-vf58-7m4g', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32838', '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-2017-70 | null | salt before 2015.5.5 leaks git usernames and passwords to the log. | {'CVE-2015-6918'} | 2021-07-25T23:34:53.773176Z | 2017-10-10T16:29:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=1257154', 'https://github.com/saltstack/salt/commit/28aa9b105804ff433d8f663b2f9b804f2b75495a'} | null |
PyPI | GHSA-8c89-2vwr-chcq | Heap buffer overflow in `QuantizedResizeBilinear` | ### Impact
An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization:
```python
import tensorflow as tf
images = tf.constant([], shape=[0], dtype=tf.qint32)
size = tf.constant([], shape=[0], dtype=tf.int32)
min = tf.constant([], dtype=tf.float32)
max = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=False, half_pixel_centers=False)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:
```cc
const float in_min = context->input(2).flat<float>()(0);
const float in_max = context->input(3).flat<float>()(0);
```
However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow.
### Patches
We have patched the issue in GitHub commit [f6c40f0c6cbf00d46c7717a26419f2062f2f8694](https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694).
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-2021-29537'} | 2022-03-03T05:13:57.936315Z | 2021-05-21T14:22:35Z | LOW | null | {'CWE-787', 'CWE-131'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29537', 'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq'} | null |
PyPI | GHSA-4pwq-fj89-6rjc | Cross-site Scripting in Apache Airflow | In Apache Airflow < 1.10.12, the "origin" parameter passed to some of the endpoints like '/trigger' was vulnerable to XSS exploit. | {'CVE-2020-13944'} | 2022-03-03T05:13:09.465862Z | 2021-06-18T18:29:54Z | MODERATE | null | {'CWE-79'} | {'http://www.openwall.com/lists/oss-security/2021/05/01/2', 'http://www.openwall.com/lists/oss-security/2020/12/11/2', 'https://lists.apache.org/thread.html/r97e1b60ca508a86be58c43f405c0c8ff00ba467ba0bee68704ae7e3e%40%3Cdev.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cdev.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cannounce.apache.org%3E', 'https://github.com/apache/airflow/commit/5c2bb7b0b0e717b11f093910b443243330ad93ca', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cusers.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/r4656959c8ed06c1f6202d89aa4e67b35ad7bdba5a666caff3fea888e@%3Cusers.airflow.apache.org%3E', 'https://nvd.nist.gov/vuln/detail/CVE-2020-13944', 'https://lists.apache.org/thread.html/ra8ce70088ba291f358e077cafdb14d174b7a1ce9a9d86d1b332d6367@%3Cusers.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/rc005f4de9d9b0ba943ceb8ff5a21a5c6ff8a9df52632476698d99432@%3Cannounce.apache.org%3E'} | null |
PyPI | GHSA-jjjr-3jcw-f8v6 | Potential Observable Timing Discrepancy in Wagtail | ### Impact
A potential timing attack exists on pages or documents that have been protected with a shared password through Wagtail's "Privacy" controls. This password check is performed through a character-by-character string comparison, and so an attacker who is able to measure the time taken by this check to a high degree of accuracy could potentially use timing differences to gain knowledge of the password. (This is [understood to be feasible on a local network, but not on the public internet](https://groups.google.com/d/msg/django-developers/iAaq0pvHXuA/fpUuwjK3i2wJ).)
Privacy settings that restrict access to pages / documents on a per-user or per-group basis (as opposed to a shared password) are unaffected by this vulnerability.
### Patches
Patched versions have been released as Wagtail 2.7.3 (for the LTS 2.7 branch), Wagtail 2.8.2 and Wagtail 2.9.
### Workarounds
Site owners who are unable to upgrade to the new versions can use [user- or group-based privacy restrictions](https://docs.wagtail.io/en/stable/advanced_topics/privacy.html) to restrict access to sensitive information; these are unaffected by this vulnerability. | {'CVE-2020-11037'} | 2022-03-03T05:13:32.855711Z | 2020-05-07T18:04:53Z | MODERATE | null | {'CWE-208'} | {'https://github.com/wagtail/wagtail/security/advisories/GHSA-jjjr-3jcw-f8v6', 'https://nvd.nist.gov/vuln/detail/CVE-2020-11037'} | null |
PyPI | GHSA-f8h4-7rgh-q2gm | Segfault and heap buffer overflow in `{Experimental,}DatasetToTFRecord` | ### Impact
The implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault:
```python
import tensorflow as tf
dataset = tf.data.Dataset.range(3)
dataset = tf.data.experimental.to_variant(dataset)
tf.raw_ops.ExperimentalDatasetToTFRecord(
input_dataset=dataset,
filename='/tmp/output',
compression_type='')
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types.
### Patches
We have patched the issue in GitHub commit [e0b6e58c328059829c3eb968136f17aa72b6c876](https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37650'} | 2022-03-03T05:13:34.854257Z | 2021-08-25T14:43:24Z | HIGH | null | {'CWE-120', 'CWE-787'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37650', 'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} | null |
PyPI | PYSEC-2020-218 | null | Multiple cross-site scripting (XSS) vulnerabilities in Zope, as used in Plone 3.3.x through 3.3.6, 4.0.x through 4.0.9, 4.1.x through 4.1.6, 4.2.x through 4.2.7, and 4.3 through 4.3.2, allow remote attackers to inject arbitrary web script or HTML via unspecified input in the (1) browser_id_manager or (2) OFS.Image method. | {'CVE-2013-7062'} | 2021-07-25T23:34:47.870506Z | 2020-01-02T19:15:00Z | null | null | null | {'https://plone.org/security/20131210/zope-xss-in-browseridmanager', 'http://seclists.org/oss-sec/2013/q4/467', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/89627', 'https://plone.org/security/20131210/zope-xss-in-OFS', 'http://seclists.org/oss-sec/2013/q4/485', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/89623'} | null |
PyPI | PYSEC-2021-648 | null | TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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-2021-29522', 'GHSA-c968-pq7h-7fxv'} | 2021-12-09T06:35:18.591146Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv'} | null |
PyPI | PYSEC-2021-218 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults. 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. 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-2021-29581', 'GHSA-vq2r-5xvm-3hc3'} | 2021-08-27T03:22:35.737731Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq2r-5xvm-3hc3'} | null |
PyPI | PYSEC-2021-832 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. 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. 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-2021-41225', 'GHSA-7r94-xv9v-63jw'} | 2021-12-09T06:35:44.943479Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw'} | null |
PyPI | PYSEC-2010-23 | null | FTPServer.py in pyftpdlib before 0.2.0 allows remote attackers to cause a denial of service via a long command. | {'CVE-2007-6739'} | 2010-10-20T04:00:00Z | 2010-10-19T20:00:00Z | null | null | null | {'http://code.google.com/p/pyftpdlib/source/detail?r=20', 'http://code.google.com/p/pyftpdlib/source/browse/trunk/HISTORY', 'http://code.google.com/p/pyftpdlib/issues/detail?id=3', 'http://code.google.com/p/pyftpdlib/source/diff?spec=svn20&r=20&format=side&path=/trunk/pyftpdlib/FTPServer.py'} | null |
PyPI | PYSEC-2020-131 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-mxjj-953w-2c2v', 'CVE-2020-15208'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | PYSEC-2018-43 | null | A flaw was found in ansible. ansible.cfg is read from the current working directory which can be altered to make it point to a plugin or a module path under the control of an attacker, thus allowing the attacker to execute arbitrary code. | {'CVE-2018-10875'} | 2021-07-02T02:41:34.153569Z | 2018-07-13T22:29:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2019:0054', 'http://lists.opensuse.org/opensuse-security-announce/2019-04/msg00021.html', 'https://www.debian.org/security/2019/dsa-4396', 'https://access.redhat.com/errata/RHSA-2018:2152', 'https://access.redhat.com/errata/RHSA-2018:2585', 'http://www.securitytracker.com/id/1041396', 'https://access.redhat.com/errata/RHSA-2018:2166', 'https://usn.ubuntu.com/4072-1/', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2018-10875', 'https://access.redhat.com/errata/RHSA-2018:2321', 'https://access.redhat.com/errata/RHSA-2018:2150', 'https://lists.debian.org/debian-lts-announce/2019/09/msg00016.html', 'https://access.redhat.com/errata/RHBA-2018:3788', 'https://access.redhat.com/errata/RHSA-2018:2151'} | null |
PyPI | PYSEC-2019-198 | null | OneLogin PythonSAML 2.3.0 and earlier may incorrectly utilize the results of XML DOM traversal and canonicalization APIs in such a way that an attacker may be able to manipulate the SAML data without invalidating the cryptographic signature, allowing the attack to potentially bypass authentication to SAML service providers. | {'CVE-2017-11427', 'GHSA-j8j8-348v-wfm3'} | 2021-08-27T03:22:18.581552Z | 2019-04-17T14:29:00Z | null | null | null | {'https://duo.com/blog/duo-finds-saml-vulnerabilities-affecting-multiple-implementations', 'https://www.kb.cert.org/vuls/id/475445', 'https://github.com/advisories/GHSA-j8j8-348v-wfm3'} | null |
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