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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | GHSA-gcx2-gvj7-pxv3 | Insufficient Protection against HTTP Request Smuggling in mitmproxy | ### Impact
In mitmproxy 7.0.4 and below, a malicious client or server is able to perform [HTTP request smuggling](https://en.wikipedia.org/wiki/HTTP_request_smuggling) attacks through mitmproxy. This means that a malicious client/server could smuggle a request/response through mitmproxy as part of another request/response's HTTP message body. While mitmproxy would only see one request, the target server would see multiple requests. A smuggled request is still captured as part of another request's body, but it does not appear in the request list and does not go through the usual mitmproxy event hooks, where users may have implemented custom access control checks or input sanitization.
Unless you use mitmproxy to protect an HTTP/1 service, no action is required.
### Patches
The vulnerability has been fixed in mitmproxy 8.0.0 and above.
### Acknowledgements
We thank Zeyu Zhang (@zeyu2001) for responsibly disclosing this vulnerability to the mitmproxy team.
### Timeline
- **2022-03-15**: Received initial report.
- **2022-03-15**: Verified report and confirmed receipt.
- **2022-03-16**: Shared patch with researcher.
- **2022-03-16**: Received confirmation that patch is working.
- **2022-03-19**: Published patched release and advisory. | {'CVE-2022-24766'} | 2022-03-22T19:31:57.202873Z | 2022-03-22T19:22:59Z | CRITICAL | null | {'CWE-444'} | {'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b', 'https://mitmproxy.org/posts/releases/mitmproxy8/', 'https://github.com/mitmproxy/mitmproxy', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24766', 'https://github.com/mitmproxy/mitmproxy/security/advisories/GHSA-gcx2-gvj7-pxv3'} | null | {'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b'} | {'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b'} |
PyPI | GHSA-8phj-f9w2-cjcc | Arbitrary file reading vulnerability in Aim | ### Impact
A path traversal attack aims to access files and directories that are stored outside the web root folder. By manipulating variables that reference files with “dot-dot-slash (../)” sequences and its variations or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system including application source code or configuration and critical system files.
Vulnerable code: https://github.com/aimhubio/aim/blob/0b99c6ca08e0ba7e7011453a2f68033e9b1d1bce/aim/web/api/views.py#L9-L16
### Patches
The vulnerability issue is resolved in Aim v3.1.0.
### References
https://owasp.org/www-community/attacks/Path_Traversal
| {'CVE-2021-43775'} | 2022-03-03T05:12:59.139597Z | 2021-11-23T22:03:23Z | HIGH | null | {'CWE-22'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-43775', 'https://github.com/aimhubio/aim/blob/0b99c6ca08e0ba7e7011453a2f68033e9b1d1bce/aim/web/api/views.py#L9-L16', 'https://github.com/aimhubio/aim/pull/1003/commits/f01266a1a479ef11d7d6c539e7dd89e9d5639738', 'https://github.com/aimhubio/aim/pull/1003', 'https://github.com/aimhubio/aim/security/advisories/GHSA-8phj-f9w2-cjcc', 'https://github.com/aimhubio/aim/issues/999', 'https://github.com/aimhubio/aim'} | null | {'https://github.com/aimhubio/aim/pull/1003/commits/f01266a1a479ef11d7d6c539e7dd89e9d5639738'} | {'https://github.com/aimhubio/aim/pull/1003/commits/f01266a1a479ef11d7d6c539e7dd89e9d5639738'} |
PyPI | GHSA-43q8-3fv7-pr5x | Improper Validation of Integrity Check Value in TensorFlow | ### Impact
The implementation of [`tf.sparse.split`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_split_op.cc#L26-L102) does not fully validate the input arguments. Hence, a malicious user can trigger a denial of service via a segfault or a heap OOB read:
```python
import tensorflow as tf
data = tf.random.uniform([1, 32, 32], dtype=tf.float32)
axis = [1, 2]
x = tf.sparse.from_dense(data)
result = tf.sparse.split(x,3, axis=axis)
```
The code assumes `axis` is a scalar. This is another instance of [TFSA-2021-190](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-190.md) (CVE-2021-41206).
### Patches
We have patched the issue in GitHub commit [61bf91e768173b001d56923600b40d9a95a04ad5](https://github.com/tensorflow/tensorflow/commit/61bf91e768173b001d56923600b40d9a95a04ad5) (merging [#53695](https://github.com/tensorflow/tensorflow/pull/53695)).
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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/53660). | null | 2022-03-03T05:11:25.282486Z | 2022-02-09T23:37:55Z | HIGH | null | {'CWE-354'} | {'https://github.com/tensorflow/tensorflow/commit/61bf91e768173b001d56923600b40d9a95a04ad5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43q8-3fv7-pr5x', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69', 'https://github.com/tensorflow/tensorflow/pull/53695', 'https://github.com/tensorflow/tensorflow'} | null | {'https://github.com/tensorflow/tensorflow/commit/61bf91e768173b001d56923600b40d9a95a04ad5'} | {'https://github.com/tensorflow/tensorflow/commit/61bf91e768173b001d56923600b40d9a95a04ad5'} |
PyPI | PYSEC-2021-409 | null | TensorFlow is an open source platform for machine learning. In affected versions the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an `Enter` node) always exists when encountering the second node (e.g., an `Exit` node). When this is not the case, `parent` is `nullptr` so dereferencing it causes a crash. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41217', 'GHSA-5crj-c72x-m7gq'} | 2021-11-13T06:52:44.799831Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq'} | null | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff'} | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff'} |
PyPI | PYSEC-2022-115 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseTensorSliceDataset` has an undefined behavior: under certain condition it can be made to dereference a `nullptr` value. The 3 input arguments to `SparseTensorSliceDataset` represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation. 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-pfjj-m3jj-9jc9', 'CVE-2022-21736'} | 2022-03-09T00:18:24.478314Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L227-L292'} | null | {'https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb'} | {'https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb'} |
PyPI | GHSA-8jxq-75rw-fhj9 | Eve allows execution of arbitrary code via Code Injection in the where parameter | io/mongo/parser.py in Eve (aka pyeve) before 0.7.5 allows remote attackers to execute arbitrary code via Code Injection in the where parameter. | {'CVE-2018-8097'} | 2022-04-26T18:17:13.379436Z | 2018-07-12T20:29:35Z | CRITICAL | null | {'CWE-94'} | {'https://github.com/pyeve/eve', 'https://github.com/pyeve/eve/issues/1101', 'https://github.com/advisories/GHSA-8jxq-75rw-fhj9', 'https://github.com/pyeve/eve/commit/f8f7019ffdf9b4e05faf95e1f04e204aa4c91f98', 'https://nvd.nist.gov/vuln/detail/CVE-2018-8097'} | null | {'https://github.com/pyeve/eve/commit/f8f7019ffdf9b4e05faf95e1f04e204aa4c91f98'} | {'https://github.com/pyeve/eve/commit/f8f7019ffdf9b4e05faf95e1f04e204aa4c91f98'} |
PyPI | PYSEC-2021-235 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SVDF` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/7f283ff806b2031f407db64c4d3edcda8fb9f9f5/tensorflow/lite/kernels/svdf.cc#L99-L102). An attacker can craft a model such that `params->rank` would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29598', 'GHSA-pmpr-55fj-r229'} | 2021-08-27T03:22:38.832523Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pmpr-55fj-r229', 'https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682'} | null | {'https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682'} | {'https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682'} |
PyPI | GHSA-rww7-2gpw-fv6j | Crash when type cannot be specialized in Tensorflow | ### Impact
Under certain scenarios, TensorFlow can fail to specialize a type during [shape inference](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L168-L174):
```cc
void InferenceContext::PreInputInit(
const OpDef& op_def, const std::vector<const Tensor*>& input_tensors,
const std::vector<ShapeHandle>& input_tensors_as_shapes) {
const auto ret = full_type::SpecializeType(attrs_, op_def);
DCHECK(ret.status().ok()) << "while instantiating types: " << ret.status();
ret_types_ = ret.ValueOrDie();
// ...
}
```
However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the `ValueOrDie` line. This results in an assertion failure as `ret` contains an error `Status`, not a value. In the second case we also get a crash due to the assertion failure.
### Patches
We have patched the issue in GitHub commit [cb164786dc891ea11d3a900e90367c339305dc7b](https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b).
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.
### 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-2022-23572'} | 2022-03-03T05:13:19.339539Z | 2022-02-09T23:28:29Z | MODERATE | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L168-L174', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23572', 'https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rww7-2gpw-fv6j'} | null | {'https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b'} | {'https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b'} |
PyPI | PYSEC-2021-665 | null | TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument. | {'GHSA-g4h2-gqm3-c9wq', 'CVE-2021-29539'} | 2021-12-09T06:35:21.512777Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g4h2-gqm3-c9wq'} | null | {'https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4'} | {'https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4'} |
PyPI | PYSEC-2021-370 | null | Zope is an open-source web application server. Zope versions prior to versions 4.6.3 and 5.3 have a remote code execution security issue. In order to be affected, one must use Python 3 for one's Zope deployment, run Zope 4 below version 4.6.3 or Zope 5 below version 5.3, and have the optional `Products.PythonScripts` add-on package installed. By default, one must have the admin-level Zope "Manager" role to add or edit Script (Python) objects through the web. Only sites that allow untrusted users to add/edit these scripts through the web are at risk. Zope releases 4.6.3 and 5.3 are not vulnerable. As a workaround, a site administrator can restrict adding/editing Script (Python) objects through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing these scripts through the web should be restricted to trusted users only. This is the default configuration in Zope. | {'GHSA-g4gq-j4p2-j8fr', 'CVE-2021-32811', 'GHSA-qcx9-j53g-ccgf'} | 2021-10-12T02:55:32.357329Z | 2021-08-02T22:15:00Z | null | null | null | {'https://github.com/zopefoundation/Zope/security/advisories/GHSA-g4gq-j4p2-j8fr', 'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988', 'https://github.com/zopefoundation/AccessControl/security/advisories/GHSA-qcx9-j53g-ccgf'} | null | {'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988'} | {'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988'} |
PyPI | PYSEC-2021-720 | null | TensorFlow is an end-to-end open source platform for machine learning. TFLite's convolution code(https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc) has multiple division where the divisor is controlled by the user and not checked to be non-zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29594', 'GHSA-3qgw-p4fm-x7gf'} | 2021-12-09T06:35:31.088691Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qgw-p4fm-x7gf', 'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} | {'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} |
PyPI | PYSEC-2022-142 | null | Tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. 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-8r7c-3cm2-3h8f', 'CVE-2022-23578'} | 2022-03-09T00:18:28.101919Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8r7c-3cm2-3h8f', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262', 'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} | null | {'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} | {'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} |
PyPI | GHSA-wcv5-qrj6-9pfm | Heap buffer overflow in `Conv3DBackprop*` | ### Impact
Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows:
```python
import tensorflow as tf
input_sizes = tf.constant([1, 1, 1, 1, 2], shape=[5], dtype=tf.int32)
filter_tensor = tf.constant([734.6274508233133, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0,
-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0,
-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[4, 1, 6, 1, 1], dtype=tf.float32)
out_backprop = tf.constant([-10.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 89, 29, 89, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```
```python
import tensorflow as tf
input_values = [-10.0] * (7 * 7 * 7 * 7 * 7)
input_values[0] = 429.6491056791816
input_sizes = tf.constant(input_values, shape=[7, 7, 7, 7, 7], dtype=tf.float32)
filter_tensor = tf.constant([7, 7, 7, 1, 1], shape=[5], dtype=tf.int32)
out_backprop = tf.constant([-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], shape=[7, 1, 1, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 37, 65, 93, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel.
### Patches
We have patched the issue in GitHub commit [8f37b52e1320d8d72a9529b2468277791a261197](https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197).
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 securityguide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29520'} | 2022-04-26T18:16:56.604479Z | 2021-05-21T14:21:12Z | LOW | null | {'CWE-120', 'CWE-787'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-qrj6-9pfm', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29520', 'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | null | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} |
PyPI | GHSA-49qr-xh3w-h436 | Moderate severity vulnerability that affects notebook | Jupyter Notebook before 5.7.1 allows XSS via an untrusted notebook because nbconvert responses are considered to have the same origin as the notebook server. In other words, nbconvert endpoints can execute JavaScript with access to the server API. In notebook/nbconvert/handlers.py, NbconvertFileHandler and NbconvertPostHandler do not set a Content Security Policy to prevent this. | {'CVE-2018-19351'} | 2022-03-03T05:14:13.270276Z | 2018-11-21T22:15:47Z | MODERATE | null | {'CWE-79'} | {'https://github.com/advisories/GHSA-49qr-xh3w-h436', 'https://pypi.org/project/notebook/#history', 'https://github.com/jupyter/notebook', 'https://lists.debian.org/debian-lts-announce/2020/11/msg00033.html', 'https://nvd.nist.gov/vuln/detail/CVE-2018-19351', 'https://groups.google.com/forum/#!topic/jupyter/hWzu2BSsplY', 'https://github.com/jupyter/notebook/blob/master/docs/source/changelog.rst', 'https://github.com/jupyter/notebook/commit/107a89fce5f413fb5728c1c5d2c7788e1fb17491'} | null | {'https://github.com/jupyter/notebook/commit/107a89fce5f413fb5728c1c5d2c7788e1fb17491'} | {'https://github.com/jupyter/notebook/commit/107a89fce5f413fb5728c1c5d2c7788e1fb17491'} |
PyPI | PYSEC-2020-50 | null | The Jupyter Server provides the backend (i.e. the core services, APIs, and REST endpoints) for Jupyter web applications like Jupyter notebook, JupyterLab, and Voila. In Jupyter Server before version 1.1.1, an open redirect vulnerability could cause the jupyter server to redirect the browser to a different malicious website. All jupyter servers running without a base_url prefix 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. This same vulnerability was patched in upstream notebook v5.7.8. This is fixed in jupyter_server 1.1.1. If upgrade is not available, a workaround can be to run your server on a url prefix: "jupyter server --ServerApp.base_url=/jupyter/". | {'CVE-2020-26275', 'GHSA-9f66-54xg-pc2c'} | 2021-03-30T19:15:00Z | 2020-12-21T18:15:00Z | null | null | null | {'https://advisory.checkmarx.net/advisory/CX-2020-4291', 'https://pypi.org/project/jupyter-server/', 'https://github.com/jupyter-server/jupyter_server/security/advisories/GHSA-9f66-54xg-pc2c', 'https://github.com/jupyter-server/jupyter_server/commit/85e4abccf6ea9321d29153f73b0bd72ccb3a6bca'} | null | {'https://github.com/jupyter-server/jupyter_server/commit/85e4abccf6ea9321d29153f73b0bd72ccb3a6bca'} | {'https://github.com/jupyter-server/jupyter_server/commit/85e4abccf6ea9321d29153f73b0bd72ccb3a6bca'} |
PyPI | GHSA-vjg4-v33c-ggc4 | Out of bounds read in Tensorflow | ### Impact
The [implementation of `FractionalAvgPoolGrad`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360) does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=[2,2,2,2],
out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]],
row_pooling_sequence=[-10,1,2,3],
col_pooling_sequence=[1,2,3,4],
overlapping=True)
return y
test()
```
### Patches
We have patched the issue in GitHub commit [002408c3696b173863228223d535f9de72a101a9](https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9).
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-2022-21730'} | 2022-03-03T05:14:14.640464Z | 2022-02-09T18:29:45Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4', 'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21730'} | null | {'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} | {'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} |
PyPI | GHSA-fvgf-6h6h-3322 | Improper Limitation of a Pathname to a Restricted Directory | In Django 2.2 before 2.2.18, 3.0 before 3.0.12, and 3.1 before 3.1.6, the django.utils.archive.extract method (used by "startapp --template" and "startproject --template") allows directory traversal via an archive with absolute paths or relative paths with dot segments. | {'CVE-2021-3281'} | 2022-03-03T05:12:27.394989Z | 2021-03-18T20:29:49Z | MODERATE | null | {'CWE-22'} | {'https://docs.djangoproject.com/en/3.1/releases/3.0.12/', 'https://www.djangoproject.com/weblog/2021/feb/01/security-releases/', 'https://security.netapp.com/advisory/ntap-20210226-0004/', 'https://github.com/django/django/commit/05413afa8c18cdb978fcdf470e09f7a12b234a23', 'https://docs.djangoproject.com/en/3.1/releases/security/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3281', 'https://groups.google.com/forum/#!forum/django-announce', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YF52FKEH5S2P5CM4X7IXSYG67YY2CDOO/'} | null | {'https://github.com/django/django/commit/05413afa8c18cdb978fcdf470e09f7a12b234a23'} | {'https://github.com/django/django/commit/05413afa8c18cdb978fcdf470e09f7a12b234a23'} |
PyPI | GHSA-8434-v7xw-8m9x | Improper Neutralization of Argument Delimiters in a Decompiling Package Process in APKLeaks | APKLeaks prior to v2.0.4 allows remote authenticated attackers to execute arbitrary OS commands via package name inside application manifest.
### Impact
An attacker could include arguments that allow unintended commands or code to be executed, allow sensitive data to be read or modified or could cause other unintended behavior through malicious package name.
### Patches
The problem is fixed in version v2.0.4 and above.
### Workarounds
```bash
> git fetch --tags; git checkout v2.0.6-dev
```
Or pull to the latest version:
```bash
> git pull origin master
```
### References
- a966e781499ff6fd4eea66876d7532301b13a382
### For more information
If you have any questions or comments about this advisory:
* Email me at [me@dw1.io](mailto:me@dw1.io)
| {'CVE-2021-21386'} | 2022-01-21T21:03:01Z | 2022-01-21T23:03:39Z | CRITICAL | null | {'CWE-78', 'CWE-88'} | {'https://github.com/dwisiswant0/apkleaks', 'https://github.com/dwisiswant0/apkleaks/security/advisories/GHSA-8434-v7xw-8m9x', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21386', 'https://github.com/dwisiswant0/apkleaks/commit/a966e781499ff6fd4eea66876d7532301b13a382'} | null | {'https://github.com/dwisiswant0/apkleaks/commit/a966e781499ff6fd4eea66876d7532301b13a382'} | {'https://github.com/dwisiswant0/apkleaks/commit/a966e781499ff6fd4eea66876d7532301b13a382'} |
PyPI | GHSA-v6wp-4m6f-gcjg | Open redirect vulnerability in `aiohttp` (`normalize_path_middleware` middleware) | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Open redirect vulnerability — a maliciously crafted link to an aiohttp-based web-server could redirect the browser to a different website.
It is caused by a bug in the `aiohttp.web_middlewares.normalize_path_middleware` middleware.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
This security problem has been fixed in v3.7.4. Upgrade your dependency as follows:
[`pip install aiohttp >= 3.7.4`]
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
If upgrading is not an option for you, a workaround can be to avoid using `aiohttp.web_middlewares.normalize_path_middleware` in your applications.
### References
_Are there any links users can visit to find out more?_
* [aiohttp @ PyPI]
* [GHSA-v6wp-4m6f-gcjg]
* [OWASP page on open redirects]
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [aiohttp repo](https://github.com/aio-libs/aiohttp/issues/new/choose)
* Email us at wk+aio-libs-security@sydorenko.org.ua and/or andrew.svetlov+aio-libs-security@gmail.com
Credit: [Jelmer Vernooij] and [Beast Glatisant].
[aiohttp @ PyPI]: https://pypi.org/p/aiohttp
[`pip install aiohttp >= 3.7.4`]: https://pypi.org/project/aiohttp/3.7.4/
[GHSA-v6wp-4m6f-gcjg]: https://github.com/aio-libs/aiohttp/security/advisories/GHSA-v6wp-4m6f-gcjg
[OWASP page on open redirects]:
https://cheatsheetseries.owasp.org/cheatsheets/Unvalidated_Redirects_and_Forwards_Cheat_Sheet.html
[Jelmer Vernooij]: https://jelmer.uk
[Beast Glatisant]: https://github.com/g147 | {'CVE-2021-21330'} | 2022-03-03T05:12:52.241808Z | 2021-02-26T02:11:57Z | LOW | null | {'CWE-601'} | {'https://pypi.org/project/aiohttp/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/FU7ENI54JNEK3PHEFGCE46DGMFNTVU6L/', 'https://www.debian.org/security/2021/dsa-4864', 'https://github.com/aio-libs/aiohttp/security/advisories/GHSA-v6wp-4m6f-gcjg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21330', 'https://github.com/aio-libs/aiohttp/blob/master/CHANGES.rst#374-2021-02-25', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/JN3V7CZJRT4QFCVXB6LDPCJH7NAOFCA5/', 'https://github.com/aio-libs/aiohttp/commit/2545222a3853e31ace15d87ae0e2effb7da0c96b'} | null | {'https://github.com/aio-libs/aiohttp/commit/2545222a3853e31ace15d87ae0e2effb7da0c96b'} | {'https://github.com/aio-libs/aiohttp/commit/2545222a3853e31ace15d87ae0e2effb7da0c96b'} |
PyPI | PYSEC-2022-82 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized 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. 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-2022-23573', 'GHSA-q85f-69q7-55h2'} | 2022-03-09T00:17:34.035553Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143', 'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} | null | {'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} | {'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} |
PyPI | GHSA-59q2-x2qc-4c97 | Heap OOB access in unicode ops | ### Impact
An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`:
```python
import tensorflow as tf
input_values = tf.constant([58], shape=[1], dtype=tf.int32)
input_splits = tf.constant([[81, 101, 0]], shape=[3], dtype=tf.int32)
output_encoding = "UTF-8"
tf.raw_ops.UnicodeEncode(
input_values=input_values, input_splits=input_splits,
output_encoding=output_encoding)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc)
assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor.
### Patches
We have patched the issue in GitHub commit [51300ba1cc2f487aefec6e6631fef03b0e08b298](https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298).
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-2021-29559'} | 2022-03-03T05:13:35.130003Z | 2021-05-21T14:24:54Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-59q2-x2qc-4c97', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29559', 'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | null | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} |
PyPI | PYSEC-2021-632 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB access. 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-2021-41223', 'GHSA-f54p-f6jp-4rhr'} | 2021-12-09T06:35:10.813181Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr'} | null | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda'} | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda'} |
PyPI | GHSA-4vrf-ff7v-hpgr | Division by zero in TFLite's implementation of `EmbeddingLookup` | The implementation of the `EmbeddingLookup` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74):
```cc
const int row_size = SizeOfDimension(value, 0);
const int row_bytes = value->bytes / row_size;
```
An attacker can craft a model such that the first dimension of the `value` input is 0.
### Patches
We have patched the issue in GitHub commit [f61c57bd425878be108ec787f4d96390579fb83e](https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e).
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-2021-29596'} | 2022-03-03T05:13:43.036660Z | 2021-05-21T14:27:51Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29596', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vrf-ff7v-hpgr'} | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} |
PyPI | PYSEC-2021-262 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version. | {'GHSA-95xm-g58g-3p88', 'CVE-2021-37640'} | 2021-08-27T03:22:43.107664Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-95xm-g58g-3p88', 'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} | null | {'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} | {'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} |
PyPI | PYSEC-2021-798 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-jwf9-w5xm-f437', 'CVE-2021-37687'} | 2021-12-09T06:35:39.946066Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jwf9-w5xm-f437', 'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | null | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} |
PyPI | GHSA-x3v8-c8qx-3j3r | Null pointer exception in `DeserializeSparse` | ### Impact
The [shape inference code for `DeserializeSparse`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/sparse_ops.cc#L152-L168) can trigger a null pointer dereference:
```python
import tensorflow as tf
dataset = tf.data.Dataset.range(3)
@tf.function
def test():
y = tf.raw_ops.DeserializeSparse(
serialized_sparse=tf.data.experimental.to_variant(dataset),
dtype=tf.int32)
test()
```
This is because the shape inference function assumes that the `serialize_sparse` tensor is a tensor with positive rank (and having `3` as the last dimension). However, in the example above, the argument is a scalar (i.e., rank 0).
### Patches
We have patched the issue in GitHub commit [d3738dd70f1c9ceb547258cbb82d853da8771850](https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850).
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-2021-41215'} | 2022-03-03T05:13:55.396022Z | 2021-11-10T18:57:45Z | MODERATE | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41215', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r', 'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} | null | {'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} | {'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} |
PyPI | PYSEC-2021-777 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToVariant`. 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. We have patched the issue in GitHub 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. | {'CVE-2021-37666', 'GHSA-w4xf-2pqw-5mq7'} | 2021-12-09T06:35:38.080205Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w4xf-2pqw-5mq7'} | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} |
PyPI | PYSEC-2020-327 | null | In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 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 the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | {'GHSA-hx2x-85gr-wrpq', 'CVE-2020-15212'} | 2021-12-09T06:35:15.513160Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | GHSA-28mg-98xm-q493 | Open Redirect in archivy | archivy prior to version 1.7.1 is vulnerable to open redirect. | {'CVE-2022-0697'} | 2022-04-05T00:15:24.394396Z | 2022-03-08T00:00:32Z | MODERATE | null | {'CWE-601'} | {'https://huntr.dev/bounties/2d0301a2-10ff-48f4-a346-5a0e8707835b', 'https://github.com/pypa/advisory-database/tree/main/vulns/archivy/PYSEC-2022-161.yaml', 'https://github.com/archivy/archivy', 'https://github.com/archivy/archivy/commit/2d8cb29853190d42572b36deb61127e68d6be574', 'https://github.com/archivy/archivy/releases/tag/v1.7.1', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0697'} | null | {'https://github.com/archivy/archivy/commit/2d8cb29853190d42572b36deb61127e68d6be574'} | {'https://github.com/archivy/archivy/commit/2d8cb29853190d42572b36deb61127e68d6be574'} |
PyPI | PYSEC-2020-46 | null | In httplib2 before version 0.18.0, an attacker controlling unescaped part of uri for `httplib2.Http.request()` could change request headers and body, send additional hidden requests to same server. This vulnerability impacts software that uses httplib2 with uri constructed by string concatenation, as opposed to proper urllib building with escaping. This has been fixed in 0.18.0. | {'CVE-2020-11078', 'GHSA-gg84-qgv9-w4pq'} | 2020-08-19T18:56:00Z | 2020-05-20T16:15:00Z | null | null | null | {'https://lists.debian.org/debian-lts-announce/2020/06/msg00000.html', 'https://lists.apache.org/thread.html/rad8872fc99f670958c2774e2bf84ee32a3a0562a0c787465cf3dfa23@%3Cissues.beam.apache.org%3E', 'https://lists.apache.org/thread.html/r23711190c2e98152cb6f216b95090d5eeb978543bb7e0bad22ce47fc@%3Cissues.beam.apache.org%3E', 'https://lists.apache.org/thread.html/rc9eff9572946142b657c900fe63ea4bbd3535911e8d4ce4d08fe4b89@%3Ccommits.allura.apache.org%3E', 'https://lists.apache.org/thread.html/r7f364000066748299b331b615ba51c62f55ab5b201ddce9a22d98202@%3Cissues.beam.apache.org%3E', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/IXCX2AWROGWGY5GXR7VN3BKF34A2FO6J/', 'https://lists.apache.org/thread.html/r69a462e690b5f2c3d418a288a2c98ae764d58587bd0b5d6ab141f25f@%3Cissues.beam.apache.org%3E', 'https://lists.apache.org/thread.html/r4d35dac106fab979f0db75a07fc4e320ad848b722103e79667ff99e1@%3Cissues.beam.apache.org%3E', 'https://github.com/httplib2/httplib2/security/advisories/GHSA-gg84-qgv9-w4pq', 'https://github.com/httplib2/httplib2/commit/a1457cc31f3206cf691d11d2bf34e98865873e9e', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/PZJ3D6JSM7CFZESZZKGUW2VX55BOSOXI/'} | null | {'https://github.com/httplib2/httplib2/commit/a1457cc31f3206cf691d11d2bf34e98865873e9e'} | {'https://github.com/httplib2/httplib2/commit/a1457cc31f3206cf691d11d2bf34e98865873e9e'} |
PyPI | PYSEC-2021-448 | null | TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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-2021-29520', 'GHSA-wcv5-qrj6-9pfm'} | 2021-12-09T06:34:46.522398Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-qrj6-9pfm', 'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | null | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} |
PyPI | GHSA-247x-2f9f-5wp7 | Stack overflow in TensorFlow | ### Impact
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`:
```
library {
function {
signature {
name: "SomeOp"
description: "Self recursive op"
}
node_def {
name: "1"
op: "SomeOp"
}
node_def {
name: "2"
op: "SomeOp"
}
}
}
```
This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes.
### Patches
We have patched the issue in GitHub commit [448a16182065bd08a202d9057dd8ca541e67996c](https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c).
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-2022-23591'} | 2022-03-03T05:14:16.294002Z | 2022-02-09T23:30:01Z | HIGH | null | {'CWE-400'} | {'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23591'} | null | {'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c'} | {'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c'} |
PyPI | PYSEC-2022-154 | null | Tensorflow is an Open Source Machine Learning Framework. A `GraphDef` from a TensorFlow `SavedModel` can be maliciously altered to cause a TensorFlow process to crash due to encountering a `StatusOr` value that is an error and forcibly extracting the value from it. We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected. | {'CVE-2022-23590', 'GHSA-pqrv-8r2f-7278'} | 2022-03-09T00:18:29.813618Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L560-L567', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pqrv-8r2f-7278'} | null | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439'} | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439'} |
PyPI | PYSEC-2022-94 | null | Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. 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-fq6p-6334-8gr4', 'CVE-2022-23585'} | 2022-03-09T00:17:35.562344Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L322-L416', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq6p-6334-8gr4', 'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} | null | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} |
PyPI | PYSEC-2021-274 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37652', 'GHSA-m7fm-4jfh-jrg6'} | 2021-08-27T03:22:44.162996Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m7fm-4jfh-jrg6'} | null | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab'} | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab'} |
PyPI | PYSEC-2021-624 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `DeserializeSparse` can trigger a null pointer dereference. This is because the shape inference function assumes that the `serialize_sparse` tensor is a tensor with positive rank (and having `3` as the last dimension). 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-x3v8-c8qx-3j3r', 'CVE-2021-41215'} | 2021-12-09T06:35:09.664583Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r', 'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} | null | {'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} | {'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} |
PyPI | PYSEC-2020-274 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. 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." | {'CVE-2020-15194', 'GHSA-9mqp-7v2h-2382'} | 2021-12-09T06:34:41.172167Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9mqp-7v2h-2382', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} |
PyPI | PYSEC-2020-331 | null | In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. 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-xwhf-g6j5-j5gc', 'CVE-2020-15266'} | 2021-12-09T06:35:15.790944Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/42129', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc', 'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | null | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} |
PyPI | GHSA-v52p-hfjf-wg88 | Division by zero in TFLite's implementation of `SpaceToBatchNd` | ### Impact
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):
```cc
TF_LITE_ENSURE_EQ(context, final_dim_size % block_shape[dim], 0);
output_size->data[dim + 1] = final_dim_size / block_shape[dim];
```
An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0.
### Patches
We have patched the issue in GitHub commit [6d36ba65577006affb272335b7c1abd829010708](https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708).
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-2021-29597'} | 2022-03-03T05:12:47.220689Z | 2021-05-21T14:27:54Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29597', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v52p-hfjf-wg88'} | null | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708'} | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708'} |
PyPI | PYSEC-2021-761 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37650', 'GHSA-f8h4-7rgh-q2gm'} | 2021-12-09T06:35:36.648389Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm', 'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} | null | {'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} | {'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} |
PyPI | PYSEC-2022-103 | null | Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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-fpcp-9h7m-ffpx', 'CVE-2022-23595'} | 2022-03-09T00:17:36.537526Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/jit/xla_platform_info.cc#L43-L104', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx', 'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} | null | {'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} | {'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} |
PyPI | GHSA-2ccw-7px8-vmpf | Open Redirect in Flask-AppBuilder | Flask-AppBuilder is an application development framework built on top of Flask. Versions prior to 3.4.5 contain an open redirect vulnerability when using the database authentication login page. There are no known workarounds. Users are recommended to upgrade to version 3.4.5 or later.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Flask-AppBuilder](https://github.com/dpgaspar/Flask-AppBuilder)
| {'CVE-2022-24776'} | 2022-04-07T00:15:32.741611Z | 2022-03-25T19:27:39Z | MODERATE | null | {'CWE-601'} | {'https://github.com/dpgaspar/Flask-AppBuilder', 'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-2ccw-7px8-vmpf', 'https://github.com/dpgaspar/Flask-AppBuilder/pull/1804/commits/5214d975ebad2ff32057443d2cc20fef1c04d0ea', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24776', 'https://github.com/dpgaspar/Flask-AppBuilder/releases/tag/v3.4.5', 'https://github.com/dpgaspar/Flask-AppBuilder/pull/1804'} | null | {'https://github.com/dpgaspar/Flask-AppBuilder/pull/1804/commits/5214d975ebad2ff32057443d2cc20fef1c04d0ea'} | {'https://github.com/dpgaspar/Flask-AppBuilder/pull/1804/commits/5214d975ebad2ff32057443d2cc20fef1c04d0ea'} |
PyPI | GHSA-cg54-gpgr-4rm6 | user-readable api tokens in systemd units for JupyterHub | ### Impact
user API tokens issued to single-user servers are specified in the environment of systemd units, which are accessible to all users.
In particular, the-littlest-jupyterhub is affected, which uses systemdspawner by default.
### Patches
Patched in jupyterhub-systemdspawner v0.15
### Workarounds
No workaround other than upgrading systemdspawner to 0.15
### For more information
If you have any questions or comments about this advisory:
* Open a thread in [the Jupyter forum](https://discourse.jupyter.org)
* Email us at [security@ipython.org](mailto:security@ipython.org) | {'CVE-2020-26261'} | 2022-03-03T05:12:51.750152Z | 2020-12-09T16:27:43Z | HIGH | null | {'CWE-668'} | {'https://pypi.org/project/jupyterhub-systemdspawner/', 'https://github.com/jupyterhub/systemdspawner/blob/master/CHANGELOG.md#v015', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26261', 'https://github.com/jupyterhub/systemdspawner/security/advisories/GHSA-cg54-gpgr-4rm6', 'https://github.com/jupyterhub/systemdspawner/commit/a4d08fd2ade1cfd0ef2c29dc221e649345f23580'} | null | {'https://github.com/jupyterhub/systemdspawner/commit/a4d08fd2ade1cfd0ef2c29dc221e649345f23580'} | {'https://github.com/jupyterhub/systemdspawner/commit/a4d08fd2ade1cfd0ef2c29dc221e649345f23580'} |
PyPI | PYSEC-2016-9 | null | Pillow before 3.3.2 allows context-dependent attackers to execute arbitrary code by using the "crafted image file" approach, related to an "Insecure Sign Extension" issue affecting the ImagingNew in Storage.c component. | {'GHSA-w4vg-rf63-f3j3', 'CVE-2016-9190'} | 2021-07-05T00:01:24.189764Z | 2016-11-04T10:59:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/pull/2146/commits/5d8a0be45aad78c5a22c8d099118ee26ef8144af', 'http://pillow.readthedocs.io/en/3.4.x/releasenotes/3.3.2.html', 'http://www.securityfocus.com/bid/94234', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/python-pillow/Pillow/issues/2105', 'http://www.debian.org/security/2016/dsa-3710', 'https://github.com/advisories/GHSA-w4vg-rf63-f3j3'} | null | {'https://github.com/python-pillow/Pillow/pull/2146/commits/5d8a0be45aad78c5a22c8d099118ee26ef8144af'} | {'https://github.com/python-pillow/Pillow/pull/2146/commits/5d8a0be45aad78c5a22c8d099118ee26ef8144af'} |
PyPI | PYSEC-2021-809 | null | TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. 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-2021-41201', 'GHSA-j86v-p27c-73fm'} | 2021-12-09T06:35:41.402625Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm'} | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} |
PyPI | GHSA-8pmx-p244-g88h | Interpreter crash from `tf.io.decode_raw` | ### Impact
The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes.
```python
import tensorflow as tf
tf.io.decode_raw(tf.constant(["1","2","3","4"]), tf.uint16, fixed_length=4)
```
The [implementation of the padded version](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules.
First, the code [computes](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument:
```cc
int width = fixed_length / sizeof(T);
```
The `fixed_length` argument is also used to determine the [size needed for the output tensor](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79):
```cc
TensorShape out_shape = input.shape();
out_shape.AddDim(width);
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output("output", out_shape, &output_tensor));
auto out = output_tensor->flat_inner_dims<T>();
T* out_data = out.data();
memset(out_data, 0, fixed_length * flat_in.size());
```
This is followed by [reencoding code](https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94):
```cc
for (int64 i = 0; i < flat_in.size(); ++i) {
const T* in_data = reinterpret_cast<const T*>(flat_in(i).data());
if (flat_in(i).size() > fixed_length) {
memcpy(out_data, in_data, fixed_length);
} else {
memcpy(out_data, in_data, flat_in(i).size());
}
out_data += fixed_length;
}
```
The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output.
Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory.
### Patches
We have patched the issue in GitHub commit [698e01511f62a3c185754db78ebce0eee1f0184d](https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d).
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. | {'CVE-2021-29614'} | 2022-04-26T18:17:02.885921Z | 2021-05-21T14:28:42Z | HIGH | null | {'CWE-787', 'CWE-665'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29614', 'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h'} | null | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d'} | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d'} |
PyPI | GHSA-fxmx-pfm2-85m2 | Cross-site Scripting in Ericsson CodeChecker | In Ericsson CodeChecker prior to 6.18.2, a Stored Cross-site scripting (XSS) vulnerability in the comments component of the reports viewer allows remote attackers to inject arbitrary web script or HTML via the POST JSON data of the /CodeCheckerService API. | {'CVE-2021-44217'} | 2022-03-03T05:13:28.302927Z | 2022-01-21T23:32:32Z | MODERATE | null | {'CWE-79'} | {'https://user-images.githubusercontent.com/9525971/142965091-e118b012-a7fc-4c2f-ad0c-80aeed6f7ec9.png', 'https://github.com/Ericsson/codechecker', 'https://github.com/Ericsson/codechecker/commit/72ee51158e6d81150320223b85410c179b9ee2b1', 'https://github.com/Ericsson/codechecker/releases/tag/v6.18.2', 'https://github.com/Hyperkopite/CVE-2021-44217/blob/main/README.md', 'https://github.com/Ericsson/codechecker/pull/3549', 'https://nvd.nist.gov/vuln/detail/CVE-2021-44217', 'https://codechecker-demo.eastus.cloudapp.azure.com/'} | null | {'https://github.com/Ericsson/codechecker/commit/72ee51158e6d81150320223b85410c179b9ee2b1'} | {'https://github.com/Ericsson/codechecker/commit/72ee51158e6d81150320223b85410c179b9ee2b1'} |
PyPI | GHSA-m4hf-j54p-p353 | Type confusion leading to segfault in Tensorflow | ### Impact
The [implementation of shape inference for `ConcatV2`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/common_shape_fns.cc#L1961-L2059) can be used to trigger a denial of service attack via a segfault caused by a type confusion:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.ConcatV2(
values=[[1,2,3],[4,5,6]],
axis = 0xb500005b)
return y
test()
```
The `axis` argument is translated into `concat_dim` in the `ConcatShapeHelper` helper function. Then, a value for `min_rank` is computed based on `concat_dim`. This is then used to validate that the `values` tensor has at least the required rank:
```cc
int64_t concat_dim;
if (concat_dim_t->dtype() == DT_INT32) {
concat_dim = static_cast<int64_t>(concat_dim_t->flat<int32>()(0));
} else {
concat_dim = concat_dim_t->flat<int64_t>()(0);
}
// Minimum required number of dimensions.
const int min_rank = concat_dim < 0 ? -concat_dim : concat_dim + 1;
// ...
ShapeHandle input = c->input(end_value_index - 1);
TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, min_rank, &input));
```
However, [`WithRankAtLeast`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.cc#L345-L358) receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented:
```cc
Status InferenceContext::WithRankAtLeast(ShapeHandle shape, int64_t rank,
ShapeHandle* out) {
if (rank > kint32max) {
return errors::InvalidArgument("Rank cannot exceed kint32max");
}
// ...
}
```
Due to the fact that `min_rank` is a 32-bits value and the value of `axis`, the `rank` argument is a [negative value](https://godbolt.org/z/Gcr5haMob), so the error check is bypassed.
### Patches
We have patched the issue in GitHub commit [08d7b00c0a5a20926363849f611729f53f3ec022](https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022).
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-2022-21731'} | 2022-03-03T05:13:25.254801Z | 2022-02-10T00:19:50Z | MODERATE | null | {'CWE-843', 'CWE-754'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21731', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/common_shape_fns.cc#L1961-L2059', 'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.cc#L345-L358', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353'} | null | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022'} | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022'} |
PyPI | PYSEC-2021-673 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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-4fg4-p75j-w5xj', 'CVE-2021-29547'} | 2021-12-09T06:35:22.966902Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj', 'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} |
PyPI | PYSEC-2021-389 | null | TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. 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-m539-j985-hcr8', 'CVE-2021-41196'} | 2021-11-13T06:52:41.665281Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8', 'https://github.com/tensorflow/tensorflow/issues/51936', 'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} | null | {'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} | {'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} |
PyPI | PYSEC-2021-223 | null | TensorFlow is an end-to-end open source platform for machine learning. Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling `ComputePaddingHeightWidth`(https://github.com/tensorflow/tensorflow/blob/3f24ccd932546416ec906a02ddd183b48a1d2c83/tensorflow/lite/kernels/pooling.cc#L90). Since users can craft special models which will have `params->stride_{height,width}` be zero, this will result in a division by zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29586', 'GHSA-26j7-6w8w-7922'} | 2021-08-27T03:22:36.699869Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-26j7-6w8w-7922', 'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} | null | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} |
PyPI | PYSEC-2021-736 | null | TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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-mq5c-prh3-3f3h', 'CVE-2021-29610'} | 2021-12-09T06:35:33.722327Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mq5c-prh3-3f3h'} | null | {'https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9'} | {'https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9'} |
PyPI | GHSA-894g-6j7q-2hx6 | Cross site scripting in flask-admin | helpers.py in Flask-Admin 1.5.2 has Reflected XSS via a crafted URL. | {'CVE-2018-16516'} | 2022-03-26T01:00:11.052935Z | 2018-12-19T19:23:52Z | MODERATE | null | {'CWE-79'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/UJIYCWIH3BRLI2QNC53CQXLKVP27X7EH/', 'https://github.com/flask-admin/flask-admin/pull/1699', 'https://nvd.nist.gov/vuln/detail/CVE-2018-16516', 'https://github.com/flask-admin/flask-admin/releases/tag/v1.5.3', 'https://github.com/flask-admin/flask-admin', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZU2VKULURVXEU4YFTLMBQGYMPSXQ4MBN/', 'https://github.com/advisories/GHSA-894g-6j7q-2hx6', 'https://github.com/flask-admin/flask-admin/commit/8af10e0b022464fdcb2da3d0ea5bbd2f11c0acd1'} | null | {'https://github.com/flask-admin/flask-admin/commit/8af10e0b022464fdcb2da3d0ea5bbd2f11c0acd1'} | {'https://github.com/flask-admin/flask-admin/commit/8af10e0b022464fdcb2da3d0ea5bbd2f11c0acd1'} |
PyPI | GHSA-hvr8-466p-75rh | Integer overflow discovered in Pillow | Integer overflow in the ImagingResampleHorizontal function in libImaging/Resample.c in Pillow before 3.1.1 allows remote attackers to have unspecified impact via negative values of the new size, which triggers a heap-based buffer overflow. | {'CVE-2016-4009'} | 2022-04-26T18:33:06.960690Z | 2018-07-24T20:15:48Z | CRITICAL | null | {'CWE-119'} | {'https://security.gentoo.org/glsa/201612-52', 'https://github.com/advisories/GHSA-hvr8-466p-75rh', 'http://www.securityfocus.com/bid/86064', 'https://github.com/python-pillow/Pillow/pull/1714', 'https://github.com/python-pillow/Pillow/commit/4e0d9b0b9740d258ade40cce248c93777362ac1e', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst', 'https://nvd.nist.gov/vuln/detail/CVE-2016-4009', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/4e0d9b0b9740d258ade40cce248c93777362ac1e'} | {'https://github.com/python-pillow/Pillow/commit/4e0d9b0b9740d258ade40cce248c93777362ac1e'} |
PyPI | GHSA-pvrc-hg3f-58r6 | Heap OOB access in `Dilation2DBackpropInput` | ### Impact
An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`:
```python
import tensorflow as tf
input_tensor = tf.constant([1.1] * 81, shape=[3, 3, 3, 3], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 3], dtype=tf.float32)
out_backprop = tf.constant([1.1] * 1062, shape=[3, 2, 59, 3], dtype=tf.float32)
tf.raw_ops.Dilation2DBackpropInput(
input=input_tensor, filter=filter, out_backprop=out_backprop,
strides=[1, 40, 1, 1], rates=[1, 56, 56, 1], padding='VALID')
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array.
```cc
in_backprop(b, h_in_max, w_in_max, d) += out_backprop(b, h_out, w_out, d);
```
The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`.
### Patches
We have patched the issue in GitHub commit [3f6fe4dfef6f57e768260b48166c27d148f3015f](https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29566'} | 2022-03-03T05:12:48.377376Z | 2021-05-21T14:25:13Z | LOW | null | {'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29566', 'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pvrc-hg3f-58r6'} | null | {'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} | {'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} |
PyPI | PYSEC-2020-311 | null | In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'GHSA-pg59-2f92-5cph', 'CVE-2020-15196'} | 2021-12-09T06:35:12.960487Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-311 | 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. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. 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-wf5p-c75w-w3wh', 'CVE-2021-37689'} | 2021-08-27T03:22:47.601647Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wf5p-c75w-w3wh', 'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} |
PyPI | PYSEC-2021-741 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `ParseAttrValue`(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/framework/attr_value_util.cc#L397-L453) can be tricked into stack overflow due to recursion by giving in a specially crafted input. 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-qw5h-7f53-xrp6', 'CVE-2021-29615'} | 2021-12-09T06:35:34.555865Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qw5h-7f53-xrp6', 'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} | null | {'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} | {'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} |
PyPI | GHSA-vwcg-7xqw-qcxw | Heap Overflow in PyMiniRacer | A heap overflow in Sqreen PyMiniRacer (aka Python Mini Racer) before 0.3.0 allows remote attackers to potentially exploit heap corruption.
More details on https://blog.sqreen.com/vulnerability-disclosure-finding-a-vulnerability-in-sqreens-php-agent-and-how-we-fixed-it/. | {'CVE-2020-25489'} | 2022-03-03T05:12:42.509418Z | 2020-09-18T18:03:59Z | MODERATE | null | {'CWE-119'} | {'https://github.com/sqreen/PyMiniRacer/compare/v0.2.0...v0.3.0', 'https://github.com/sqreen/PyMiniRacer/commit/627b54768293ec277f1adb997c888ec524f4174d', 'https://blog.sqreen.com/vulnerability-disclosure-finding-a-vulnerability-in-sqreens-php-agent-and-how-we-fixed-it/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-25489', 'https://github.com/sqreen/PyMiniRacer/security/advisories/GHSA-vwcg-7xqw-qcxw'} | null | {'https://github.com/sqreen/PyMiniRacer/commit/627b54768293ec277f1adb997c888ec524f4174d'} | {'https://github.com/sqreen/PyMiniRacer/commit/627b54768293ec277f1adb997c888ec524f4174d'} |
PyPI | PYSEC-2021-254 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29617', 'GHSA-mmq6-q8r3-48fm'} | 2021-08-27T03:22:42.200654Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/issues/46900', 'https://github.com/tensorflow/issues/46974', 'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mmq6-q8r3-48fm'} | null | {'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f'} | {'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f'} |
PyPI | GHSA-x7gm-rfgv-w973 | Potential DoS with NumberFilter conversion to integer values. | ### Impact
Automatically generated `NumberFilter` instances, whose value was later converted to an integer, were subject to potential DoS from maliciously input using exponential format with sufficiently large exponents.
### Patches
Version 2.4.0+ applies a `MaxValueValidator` with a a default `limit_value` of 1e50 to the form field used by `NumberFilter` instances.
In addition, `NumberFilter` implements the new `get_max_validator()` which should return a configured validator instance to customise the limit, or else `None` to disable the additional validation.
### Workarounds
Users may manually apply an equivalent validator if they are not able to upgrade.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the django-filter repo](https://github.com/carltongibson/django-filter)
Thanks to Marcin Waraksa for the report.
| {'CVE-2020-15225'} | 2022-03-03T05:14:04.602145Z | 2020-09-28T19:05:29Z | HIGH | null | {'CWE-681'} | {'https://github.com/carltongibson/django-filter/commit/340cf7a23a2b3dcd7183f6a0d6c383e85b130d2b', 'https://github.com/carltongibson/django-filter', 'https://github.com/carltongibson/django-filter/releases/tag/2.4.0', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DPHENTRHRAYFXYPPBT7JRHZRWILRY44S/', 'https://security.netapp.com/advisory/ntap-20210604-0010/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/FAT2ZAEF6DM3VFSOHKB7X3ASSHGQHJAK/', 'https://github.com/carltongibson/django-filter/security/advisories/GHSA-x7gm-rfgv-w973', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15225', 'https://pypi.org/project/django-filter/'} | null | {'https://github.com/carltongibson/django-filter/commit/340cf7a23a2b3dcd7183f6a0d6c383e85b130d2b'} | {'https://github.com/carltongibson/django-filter/commit/340cf7a23a2b3dcd7183f6a0d6c383e85b130d2b'} |
PyPI | GHSA-3m93-m4q6-mc6v | Inclusion of Sensitive Information in Log Files and Improper Output Neutralization for Logs in Ansible | Ansible, versions 2.9.x before 2.9.1, 2.8.x before 2.8.7 and Ansible versions 2.7.x before 2.7.15, is not respecting the flag no_log set it to True when Sumologic and Splunk callback plugins are used send tasks results events to collectors. This would discloses and collects any sensitive data. | {'CVE-2019-14864'} | 2022-04-25T23:46:52.658702Z | 2020-02-26T19:54:31Z | MODERATE | null | {'CWE-532'} | {'https://www.debian.org/security/2021/dsa-4950', 'https://github.com/ansible/ansible/commit/75288a89d0053d6df35c90863fb6c9542d89850e', 'https://github.com/ansible/ansible/issues/63522', 'https://github.com/ansible/ansible/pull/64748', 'https://github.com/ansible/ansible/pull/64273', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14864', 'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00021.html', 'https://github.com/ansible/ansible/pull/64274', 'https://github.com/ansible/ansible/pull/63527', 'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00026.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-14864'} | null | {'https://github.com/ansible/ansible/commit/75288a89d0053d6df35c90863fb6c9542d89850e'} | {'https://github.com/ansible/ansible/commit/75288a89d0053d6df35c90863fb6c9542d89850e'} |
PyPI | PYSEC-2021-604 | 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 division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit 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-2021-37691', 'GHSA-27qf-jwm8-g7f3'} | 2021-12-09T06:35:06.759272Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27qf-jwm8-g7f3'} | null | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9'} | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9'} |
PyPI | PYSEC-2020-254 | null | In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'GHSA-qhxx-j73r-qpm2', 'CVE-2020-26266'} | 2021-08-27T03:22:22.698179Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2'} | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2'} |
PyPI | GHSA-6f84-42vf-ppwp | Division by 0 in `QuantizedMul` | ### Impact
An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`:
```python
import tensorflow as tf
x = tf.zeros([4, 1], dtype=tf.quint8)
y = tf.constant([], dtype=tf.quint8)
min_x = tf.constant(0.0)
max_x = tf.constant(0.0010000000474974513)
min_y = tf.constant(0.0)
max_y = tf.constant(0.0010000000474974513)
tf.raw_ops.QuantizedMul(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that is controlled by the caller:
```cc
template <class T, class Toutput>
void VectorTensorMultiply(const T* vector_data, int32 vector_offset,
int64 vector_num_elements, const T* tensor_data,
int32 tensor_offset, int64 tensor_num_elements,
Toutput* output) {
for (int i = 0; i < tensor_num_elements; ++i) {
const int64 vector_i = i % vector_num_elements;
...
}
}
```
### Patches
We have patched the issue in GitHub commit [a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6](https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6).
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-2021-29528'} | 2022-03-03T05:13:53.881160Z | 2021-05-21T14:22:02Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29528', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f84-42vf-ppwp', 'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} | {'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} |
PyPI | PYSEC-2017-103 | null | An incorrect implementation of "XEP-0280: Message Carbons" in multiple XMPP clients allows a remote attacker to impersonate any user, including contacts, in the vulnerable application's display. This allows for various kinds of social engineering attacks. This CVE is for SleekXMPP up to 1.3.1 and Slixmpp all versions up to 1.2.3, as bundled in poezio (0.8 - 0.10) and other products. | {'CVE-2017-5591'} | 2021-12-14T08:19:29.328413Z | 2017-02-09T20:59:00Z | null | null | null | {'https://github.com/poezio/slixmpp/commit/22664ee7b86c8e010f312b66d12590fb47160ad8', 'http://www.securityfocus.com/bid/96166', 'https://pypi.org/project/sleekxmpp', 'https://rt-solutions.de/en/2017/02/CVE-2017-5589_xmpp_carbons/', 'https://rt-solutions.de/wp-content/uploads/2017/02/CVE-2017-5589_xmpp_carbons.pdf', 'https://nvd.nist.gov/vuln/detail/CVE-2017-5591', 'http://openwall.com/lists/oss-security/2017/02/09/29'} | null | {'https://github.com/poezio/slixmpp/commit/22664ee7b86c8e010f312b66d12590fb47160ad8'} | {'https://github.com/poezio/slixmpp/commit/22664ee7b86c8e010f312b66d12590fb47160ad8'} |
PyPI | PYSEC-2021-487 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. 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-2021-29559', 'GHSA-59q2-x2qc-4c97'} | 2021-12-09T06:34:52.523360Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-59q2-x2qc-4c97', 'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | null | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} |
PyPI | GHSA-cvpc-8phh-8f45 | Out of bounds access in tensorflow-lite | ### Impact
In TensorFlow Lite, 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:https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/kernel_util.cc#L36
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:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/c/common.h#L82
This results in special casing during validation at model loading time: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/core/subgraph.cc#L566-L580
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.
### Patches
We have patched the issue in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83). We will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### Workarounds
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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15211'} | 2022-03-03T05:13:25.109992Z | 2020-09-25T18:28:49Z | MODERATE | null | {'CWE-787', 'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15211', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859'} | {'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9'} |
PyPI | PYSEC-2020-66 | null | MISP MISP-maltego 1.4.4 incorrectly shares a MISP connection across users in a remote-transform use case. | {'CVE-2020-12889'} | 2020-05-19T13:38:00Z | 2020-05-15T18:15:00Z | null | null | null | {'https://github.com/MISP/MISP-maltego/commit/3ccde66dab4096ab5663e69f352992cc73e1160b'} | null | {'https://github.com/MISP/MISP-maltego/commit/3ccde66dab4096ab5663e69f352992cc73e1160b'} | {'https://github.com/MISP/MISP-maltego/commit/3ccde66dab4096ab5663e69f352992cc73e1160b'} |
PyPI | PYSEC-2021-468 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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-xgc3-m89p-vr3x', 'CVE-2021-29540'} | 2021-12-09T06:34:49.596439Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x', 'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} |
PyPI | PYSEC-2021-192 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor. Since this is controlled by the user, an attacker can trigger 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. | {'CVE-2021-29555', 'GHSA-r35g-4525-29fq'} | 2021-08-27T03:22:31.200110Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r35g-4525-29fq'} | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} |
PyPI | PYSEC-2021-716 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds. 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-24x6-8c7m-hv3f', 'CVE-2021-29590'} | 2021-12-09T06:35:30.403366Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-24x6-8c7m-hv3f'} | null | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578'} | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578'} |
PyPI | GHSA-v6r6-84gr-92rm | Heap buffer overflow in `AvgPool3DGrad` | ### Impact
The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32)
grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.AvgPool3DGrad(
orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides,
padding=padding)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated.
### Patches
We have patched the issue in GitHub commit [6fc9141f42f6a72180ecd24021c3e6b36165fe0d](https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d).
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-2021-29577'} | 2022-03-03T05:11:42.423697Z | 2021-05-21T14:26:18Z | LOW | null | {'CWE-787', 'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29577', 'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm'} | null | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d'} | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d'} |
PyPI | GHSA-69q2-p9xp-739v | XML Injection in petl | petl before 1.68, in some configurations, allows resolution of entities in an XML document. | {'CVE-2020-29128'} | 2022-03-22T22:32:01.161329Z | 2021-04-20T16:32:08Z | CRITICAL | null | {'CWE-91'} | {'https://petl.readthedocs.io/en/stable/changes.html', 'https://github.com/petl-developers/petl/pull/527', 'https://nvd.nist.gov/vuln/detail/CVE-2020-29128', 'https://github.com/petl-developers/petl/compare/v1.6.7...v1.6.8', 'https://github.com/petl-developers/petl/issues/526', 'https://github.com/petl-developers/petl/pull/527/commits/1b0a09f08c3cdfe2e69647bd02f97c1367a5b5f8', 'https://github.com/petl-developers/petl', 'https://github.com/petl-developers/petl/security/advisories/GHSA-f5gc-p5m3-v347', 'https://github.com/nvn1729/advisories/blob/master/cve-2020-29128.md'} | null | {'https://github.com/petl-developers/petl/pull/527/commits/1b0a09f08c3cdfe2e69647bd02f97c1367a5b5f8'} | {'https://github.com/petl-developers/petl/pull/527/commits/1b0a09f08c3cdfe2e69647bd02f97c1367a5b5f8'} |
PyPI | PYSEC-2017-84 | null | An issue was discovered in middleware.py in OpenStack Swauth through 1.2.0 when used with OpenStack Swift through 2.15.1. The Swift object store and proxy server are saving (unhashed) tokens retrieved from the Swauth middleware authentication mechanism to a log file as part of a GET URI. This allows attackers to bypass authentication by inserting a token into an X-Auth-Token header of a new request. NOTE: github.com/openstack/swauth URLs do not mean that Swauth is maintained by an official OpenStack project team. | {'CVE-2017-16613'} | 2021-08-25T04:30:33.081491Z | 2017-11-21T13:29:00Z | null | null | null | {'https://github.com/openstack/swauth/commit/70af7986265a3defea054c46efc82d0698917298', 'https://www.debian.org/security/2017/dsa-4044', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=882314', 'https://bugs.launchpad.net/swift/+bug/1655781', 'http://www.securityfocus.com/bid/101926'} | null | {'https://github.com/openstack/swauth/commit/70af7986265a3defea054c46efc82d0698917298'} | {'https://github.com/openstack/swauth/commit/70af7986265a3defea054c46efc82d0698917298'} |
PyPI | PYSEC-2020-203 | null | The safe_eval function in Ansible before 1.6.4 does not properly restrict the code subset, which allows remote attackers to execute arbitrary code via crafted instructions. NOTE: this vulnerability exists because of an incomplete fix for CVE-2014-4657. | {'CVE-2014-4678'} | 2021-07-02T02:41:33.286907Z | 2020-02-20T03:15:00Z | null | null | null | {'https://security-tracker.debian.org/tracker/CVE-2014-4678', 'https://www.openwall.com/lists/oss-security/2014/07/02/2', 'https://groups.google.com/forum/message/raw?msg=ansible-announce/ieV1vZvcTXU/5Q93ThkY9rIJ', 'https://www.rapid7.com/db/vulnerabilities/gentoo-linux-cve-2014-4678', 'https://github.com/ansible/ansible/commit/5429b85b9f6c2e640074176f36ff05fd5e4d1916', 'https://www.openwall.com/lists/oss-security/2014/06/26/30', 'https://www.rapid7.com/db/vulnerabilities/freebsd-vid-2c493ac8-205e-11e5-a4a5-002590263bf5'} | null | {'https://github.com/ansible/ansible/commit/5429b85b9f6c2e640074176f36ff05fd5e4d1916'} | {'https://github.com/ansible/ansible/commit/5429b85b9f6c2e640074176f36ff05fd5e4d1916'} |
PyPI | PYSEC-2021-653 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller. 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-x4g7-fvjj-prg8', 'CVE-2021-29527'} | 2021-12-09T06:35:19.411969Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4g7-fvjj-prg8'} | null | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} |
PyPI | PYSEC-2021-203 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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-pvrc-hg3f-58r6', 'CVE-2021-29566'} | 2021-08-27T03:22:33.149908Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pvrc-hg3f-58r6', 'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} | null | {'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} | {'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} |
PyPI | PYSEC-2021-829 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SplitV` can trigger a segfault is an attacker supplies negative arguments. This occurs whenever `size_splits` contains more than one value and at least one value is negative. 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-cpf4-wx82-gxp6', 'CVE-2021-41222'} | 2021-12-09T06:35:44.467539Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6', 'https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6'} | null | {'https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6'} | {'https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6'} |
PyPI | GHSA-hmg3-c7xj-6qwm | Heap buffer overflow in `SparseTensorToCSRSparseMatrix` | ### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices:
```python
import tensorflow as tf
import numpy as np
from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops
indices_array = np.array([[0, 0]])
value_array = np.array([0.0], dtype=np.float32)
dense_shape = [0, 0]
st = tf.SparseTensor(indices_array, value_array, dense_shape)
values_tensor = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
st.indices, st.values, st.dense_shape)
```
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:
```cc
csr_row_ptr(indices(i, 0) + 1) += 1;
```
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.
### Patches
We have patched the issue in GitHub commit [1e922ccdf6bf46a3a52641f99fd47d54c1decd13](https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29545'} | 2022-03-03T05:14:12.303446Z | 2021-05-21T14:23:25Z | LOW | null | {'CWE-131'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29545', 'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13'} | null | {'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13'} | {'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13'} |
PyPI | PYSEC-2022-123 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version. | {'GHSA-98p5-x8x4-c9m5', 'CVE-2022-23559'} | 2022-03-09T00:18:25.518342Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4', 'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5'} | null | {'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4'} | {'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4'} |
PyPI | GHSA-hjfx-8p6c-g7gx | Insufficient Verification of Data Authenticity in Pillow | An issue was discovered in Pillow before 8.2.0. For BLP data, BlpImagePlugin did not properly check that reads (after jumping to file offsets) returned data. This could lead to a DoS where the decoder could be run a large number of times on empty data. | {'CVE-2021-28678'} | 2022-03-03T05:12:01.145169Z | 2021-06-08T18:49:20Z | MODERATE | null | {'CWE-345'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-28678', 'https://github.com/python-pillow/Pillow/pull/5377', 'https://github.com/python-pillow/Pillow/pull/5377/commits/496245aa4365d0827390bd0b6fbd11287453b3a1', 'https://security.gentoo.org/glsa/202107-33', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/', 'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-28678-fix-blp-dos'} | null | {'https://github.com/python-pillow/Pillow/pull/5377/commits/496245aa4365d0827390bd0b6fbd11287453b3a1'} | {'https://github.com/python-pillow/Pillow/pull/5377/commits/496245aa4365d0827390bd0b6fbd11287453b3a1'} |
PyPI | PYSEC-2021-595 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200). The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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-2021-37682', 'GHSA-4c4g-crqm-xrxw'} | 2021-12-09T06:35:05.976648Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887'} | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538'} |
PyPI | GHSA-47wv-vhj2-g66m | Use of insecure temporary file in Horovod | ### Impact
The insecure `tempfile.mktemp()` is used when Horovod is run in an LSF job with `jsrun`. In that situation, a jsrun rank file is created with `mktemp`, which could be hijacked by another process to read or manipulate the content.
This issue does not impact the use of MPI, Gloo, Spark or Ray.
### Patches
The problem has been fixed in [b96ecae4](https://github.com/horovod/horovod/commit/b96ecae4dc69fc0a83c7c2d3f1dde600c20a1b41).
### Workarounds
The rank file is not created when `binding_args` are provided in the `Settings` instance.
### References
Please see https://github.com/horovod/horovod/pull/3358 for details.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [https://github.com/horovod/horovod](https://github.com/horovod/horovod/issues/new/choose) | {'CVE-2022-0315'} | 2022-04-05T00:15:26.847759Z | 2022-03-29T19:18:32Z | HIGH | null | {'CWE-668', 'CWE-377'} | {'https://huntr.dev/bounties/7e50397b-dd63-4bb5-b56d-704094a7da45', 'https://github.com/advisories/GHSA-47wv-vhj2-g66m', 'https://github.com/horovod/horovod', 'https://github.com/horovod/horovod/commit/b96ecae4dc69fc0a83c7c2d3f1dde600c20a1b41', 'https://github.com/horovod/horovod/pull/3358', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0315', 'https://github.com/horovod/horovod/security/advisories/GHSA-47wv-vhj2-g66m', 'https://github.com/pypa/advisory-database/tree/main/vulns/horovod/PYSEC-2022-175.yaml'} | null | {'https://github.com/horovod/horovod/commit/b96ecae4dc69fc0a83c7c2d3f1dde600c20a1b41'} | {'https://github.com/horovod/horovod/commit/b96ecae4dc69fc0a83c7c2d3f1dde600c20a1b41'} |
PyPI | GHSA-8cxv-76p7-jxwr | Null-dereference in Tensorflow | ### Impact
The [implementation of `GetInitOp`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/cc/saved_model/loader_util.cc#L31-L61) is vulnerable to a crash caused by dereferencing a null pointer:
```cc
const auto& init_op_sig_it =
meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey);
if (init_op_sig_it != sig_def_map.end()) {
*init_op_name = init_op_sig_it->second.outputs()
.find(kSavedModelInitOpSignatureKey)
->second.name();
return Status::OK();
}
```
Here, we have a nested map and we assume that if the first `.find` succeeds then so would be the search in the internal map. However, the maps are built based on the `SavedModel` protobuf format and a malicious user can alter that on disk before loading to cause the second `.find` to return `nullptr`.
### Patches
We have patched the issue in GitHub commit [4f38b1ac8e42727e18a2f0bde06d3bee8e77b250](https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250).
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-2022-23577'} | 2022-03-03T05:12:44.907686Z | 2022-02-10T00:32:29Z | MODERATE | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23577', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/cc/saved_model/loader_util.cc#L31-L61', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8cxv-76p7-jxwr', 'https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250'} | null | {'https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250'} | {'https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250'} |
PyPI | PYSEC-2021-350 | null | In Shuup, versions 1.6.0 through 2.10.8 are vulnerable to reflected Cross-Site Scripting (XSS) that allows execution of arbitrary javascript code on a victim browser. This vulnerability exists due to the error page contents not escaped. | {'GHSA-5pcx-vqjp-p34w', 'CVE-2021-25963'} | 2021-09-30T10:32:36.636402Z | 2021-09-30T08:15:00Z | null | null | null | {'https://github.com/shuup/shuup/commit/75714c37e32796eb7cbb0d977af5bcaa26573588', 'https://github.com/advisories/GHSA-5pcx-vqjp-p34w', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25963'} | null | {'https://github.com/shuup/shuup/commit/75714c37e32796eb7cbb0d977af5bcaa26573588'} | {'https://github.com/shuup/shuup/commit/75714c37e32796eb7cbb0d977af5bcaa26573588'} |
PyPI | PYSEC-2021-700 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-828x-qc2p-wprq', 'CVE-2021-29574'} | 2021-12-09T06:35:27.544548Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq'} | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} |
PyPI | GHSA-vx6v-xg64-pmr8 | Cross-site Scripting in django-helpdesk | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting'). | {'CVE-2021-3945'} | 2022-03-03T05:13:26.671191Z | 2021-11-15T23:12:41Z | HIGH | null | {'CWE-79'} | {'https://github.com/django-helpdesk/django-helpdesk', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3945', 'https://github.com/django-helpdesk/django-helpdesk/commit/2c7065e0c4296e0c692fb4a7ee19c7357583af30', 'https://huntr.dev/bounties/745f483c-70ed-441f-ab2e-7ac1305439a4'} | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/2c7065e0c4296e0c692fb4a7ee19c7357583af30'} | {'https://github.com/django-helpdesk/django-helpdesk/commit/2c7065e0c4296e0c692fb4a7ee19c7357583af30'} |
PyPI | PYSEC-2021-215 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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-2021-29578', 'GHSA-6f89-8j54-29xf'} | 2021-08-27T03:22:35.223640Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f89-8j54-29xf'} | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} |
PyPI | PYSEC-2021-645 | null | TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. 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-772j-h9xw-ffp5', 'CVE-2021-29519'} | 2021-12-09T06:35:18.174701Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772j-h9xw-ffp5'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} |
PyPI | PYSEC-2020-215 | null | Jupyter Notebook before version 6.1.5 has an Open redirect vulnerability. A maliciously crafted link to a notebook server could redirect the browser to a different website. All notebook servers are technically affected, however, these maliciously crafted links can only be reasonably made for known notebook server hosts. A link to your notebook server may appear safe, but ultimately redirect to a spoofed server on the public internet. The issue is patched in version 6.1.5. | {'GHSA-c7vm-f5p4-8fqh', 'CVE-2020-26215'} | 2021-08-11T11:14:19.531087Z | 2020-11-18T22:15:00Z | null | null | null | {'https://lists.debian.org/debian-lts-announce/2020/12/msg00004.html', 'https://github.com/jupyter/notebook/commit/3cec4bbe21756de9f0c4bccf18cf61d840314d74', 'https://github.com/jupyter/notebook/security/advisories/GHSA-c7vm-f5p4-8fqh'} | null | {'https://github.com/jupyter/notebook/commit/3cec4bbe21756de9f0c4bccf18cf61d840314d74'} | {'https://github.com/jupyter/notebook/commit/3cec4bbe21756de9f0c4bccf18cf61d840314d74'} |
PyPI | GHSA-4f99-p9c2-3j8x | Undefined behavior via `nullptr` reference binding in sparse matrix multiplication | ### Impact
The [code for sparse matrix multiplication](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/sparse_matmul_op.cc#L954-L1086) is vulnerable to undefined behavior via binding a reference to `nullptr`:
```python
import tensorflow as tf
tf.raw_ops.SparseMatMul(
a=[[1.0,1.0,1.0]],
b=[[],[],[]],
transpose_a=False,
transpose_b=False,
a_is_sparse=False,
b_is_sparse=True)
```
This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access.
### Patches
We have patched the issue in GitHub commit [e6cf28c72ba2eb949ca950d834dd6d66bb01cfae](https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae).
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-2021-41219'} | 2022-03-03T05:12:41.464426Z | 2021-11-10T18:51:51Z | HIGH | null | {'CWE-824', 'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41219'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} |
PyPI | PYSEC-2014-82 | null | FileSystemBytecodeCache in Jinja2 2.7.2 does not properly create temporary directories, which allows local users to gain privileges by pre-creating a temporary directory with a user's uid. NOTE: this vulnerability exists because of an incomplete fix for CVE-2014-1402. | {'CVE-2014-0012'} | 2021-08-27T03:22:05.027573Z | 2014-05-19T14:55:00Z | null | null | null | {'http://secunia.com/advisories/56328', 'http://www.gentoo.org/security/en/glsa/glsa-201408-13.xml', 'https://github.com/mitsuhiko/jinja2/pull/296', 'https://github.com/mitsuhiko/jinja2/commit/acb672b6a179567632e032f547582f30fa2f4aa7', 'http://seclists.org/oss-sec/2014/q1/73', 'https://bugzilla.redhat.com/show_bug.cgi?id=1051421', 'https://github.com/mitsuhiko/jinja2/pull/292', 'http://secunia.com/advisories/60738'} | null | {'https://github.com/mitsuhiko/jinja2/commit/acb672b6a179567632e032f547582f30fa2f4aa7'} | {'https://github.com/mitsuhiko/jinja2/commit/acb672b6a179567632e032f547582f30fa2f4aa7'} |
PyPI | GHSA-5pcx-vqjp-p34w | Cross-site Scripting in shuup | In Shuup, versions 1.6.0 through 2.10.8 are vulnerable to reflected Cross-Site Scripting (XSS) that allows execution of arbitrary javascript code on a victim browser. This vulnerability exists due to the error page contents not escaped. | {'CVE-2021-25963'} | 2022-03-03T05:13:23.214501Z | 2021-10-04T20:11:03Z | MODERATE | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-25963', 'https://github.com/shuup/shuup/commit/75714c37e32796eb7cbb0d977af5bcaa26573588', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25963', 'https://github.com/shuup/shuup'} | null | {'https://github.com/shuup/shuup/commit/75714c37e32796eb7cbb0d977af5bcaa26573588'} | {'https://github.com/shuup/shuup/commit/75714c37e32796eb7cbb0d977af5bcaa26573588'} |
PyPI | PYSEC-2021-583 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument. A similar issue occurs in `tf.raw_ops.LowerBound`. We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-9697-98pf-4rw7', 'CVE-2021-37670'} | 2021-12-09T06:35:04.970183Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7', 'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} | null | {'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} | {'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} |
PyPI | PYSEC-2022-135 | null | Tensorflow is an Open Source Machine Learning Framework. When decoding a tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments, if the tensors have an invalid `dtype` and 0 elements or an invalid shape. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-j3mj-fhpq-qqjj', 'CVE-2022-23571'} | 2022-03-09T00:18:27.136787Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5b491cd5e41ad63735161cec9c2a568172c8b6a3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j3mj-fhpq-qqjj'} | null | {'https://github.com/tensorflow/tensorflow/commit/5b491cd5e41ad63735161cec9c2a568172c8b6a3'} | {'https://github.com/tensorflow/tensorflow/commit/5b491cd5e41ad63735161cec9c2a568172c8b6a3'} |
PyPI | PYSEC-2021-757 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5. 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-2021-37646', 'GHSA-h6jh-7gv5-28vg'} | 2021-12-09T06:35:36.306207Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6jh-7gv5-28vg'} | null | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} |
PyPI | PYSEC-2021-307 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. 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-2021-37685', 'GHSA-c545-c4f9-rf6v'} | 2021-08-27T03:22:47.234797Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c545-c4f9-rf6v', 'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | null | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} |
PyPI | PYSEC-2020-307 | null | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15192', 'GHSA-8fxw-76px-3rxv'} | 2021-12-09T06:35:12.356862Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | PYSEC-2015-17 | null | The resolve_redirects function in sessions.py in requests 2.1.0 through 2.5.3 allows remote attackers to conduct session fixation attacks via a cookie without a host value in a redirect. | {'CVE-2015-2296'} | 2021-07-05T00:01:25.716066Z | 2015-03-18T16:59:00Z | null | null | null | {'https://github.com/kennethreitz/requests/commit/3bd8afbff29e50b38f889b2f688785a669b9aafc', 'http://www.openwall.com/lists/oss-security/2015/03/15/1', 'http://advisories.mageia.org/MGASA-2015-0120.html', 'https://warehouse.python.org/project/requests/2.6.0/', 'http://www.mandriva.com/security/advisories?name=MDVSA-2015:133', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-March/153594.html', 'http://www.ubuntu.com/usn/USN-2531-1', 'http://www.openwall.com/lists/oss-security/2015/03/14/4'} | null | {'https://github.com/kennethreitz/requests/commit/3bd8afbff29e50b38f889b2f688785a669b9aafc'} | {'https://github.com/kennethreitz/requests/commit/3bd8afbff29e50b38f889b2f688785a669b9aafc'} |
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