ecosystem stringclasses 11
values | vuln_id stringlengths 10 19 | summary stringlengths 4 220 ⌀ | details stringlengths 34 13.5k | aliases stringlengths 17 87 ⌀ | modified_date stringdate 2019-03-26 14:13:00 2022-05-10 08:46:52 | published_date stringdate 2012-06-17 03:41:00 2022-05-10 08:46:50 | severity stringclasses 5
values | score float64 0 10 ⌀ | cwe_id stringclasses 581
values | refs stringlengths 82 11.6k | introduced stringclasses 843
values | code_refs stringlengths 46 940 | commits stringlengths 46 940 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | GHSA-4j82-5ccr-4r8v | `CHECK`-failures in `TensorByteSize` in Tensorflow | ### Impact
A malicious user can cause a denial of service by altering a `SavedModel` such that [`TensorByteSize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/attr_value_util.cc#L46-L50) would trigger `CHECK` failures.
```cc
int64_t TensorByteSize(const TensorProto& t) {
// num_elements returns -1 if shape is not fully defined.
int64_t num_elems = TensorShape(t.tensor_shape()).num_elements();
return num_elems < 0 ? -1 : num_elems * DataTypeSize(t.dtype());
}
```
`TensorShape` constructor throws a `CHECK`-fail if shape is partial or has a number of elements that would overflow the size of an `int`. The `PartialTensorShape` constructor instead does not cause a `CHECK`-abort if the shape is partial, which is exactly what this function needs to be able to return `-1`.
### Patches
We have patched the issue in GitHub commit [c2426bba00a01de6913738df8fa78e0215fcce02](https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02).
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-2022-23582'} | 2022-03-03T05:13:22.456322Z | 2022-02-10T00:34:01Z | MODERATE | null | {'CWE-617'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23582', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/attr_value_util.cc#L46-L50', 'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} | null | {'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} | {'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} |
PyPI | PYSEC-2021-739 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-vvg4-vgrv-xfr7', 'CVE-2021-29613'} | 2021-12-09T06:35:34.219918Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vvg4-vgrv-xfr7', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c'} | null | {'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c', 'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b'} | {'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c'} |
PyPI | PYSEC-2021-386 | null | JupyterHub is an open source multi-user server for Jupyter notebooks. In affected versions users who have multiple JupyterLab tabs open in the same browser session, may see incomplete logout from the single-user server, as fresh credentials (for the single-user server only, not the Hub) reinstated after logout, if another active JupyterLab session is open while the logout takes place. Upgrade to JupyterHub 1.5. For distributed deployments, it is jupyterhub in the _user_ environment that needs patching. There are no patches necessary in the Hub environment. The only workaround is to make sure that only one JupyterLab tab is open when you log out. | {'CVE-2021-41247', 'GHSA-cw7p-q79f-m2v7'} | 2021-11-10T19:23:06.087408Z | 2021-11-04T18:15:00Z | null | null | null | {'https://github.com/jupyterhub/jupyterhub/commit/5ac9e7f73a6e1020ffddc40321fc53336829fe27', 'https://github.com/jupyterhub/jupyterhub/security/advisories/GHSA-cw7p-q79f-m2v7'} | null | {'https://github.com/jupyterhub/jupyterhub/commit/5ac9e7f73a6e1020ffddc40321fc53336829fe27'} | {'https://github.com/jupyterhub/jupyterhub/commit/5ac9e7f73a6e1020ffddc40321fc53336829fe27'} |
PyPI | GHSA-g4c2-ghfg-g5rh | Cross-site Scripting and Open Redirect in Products.ATContentTypes | ### Impact
Plone is vulnerable to reflected cross site scripting and open redirect when an attacker can get a compromised version of the image_view_fullscreen page in a cache, for example in Varnish.
The technique is known as cache poisoning.
Any later visitor can get redirected when clicking on a link on this page.
Usually only anonymous users are affected, but this depends on your cache settings.
### Patches
A new version 3.0.6 of Products.ATContentTypes has been released with a fix.
This version works on Plone 5.2 (Python 2 only) and will be included in Plone 5.2.7.
Note that the Products.CMFPlone package has the same problem in the 4.3 series.
`plone.app.contenttypes` has the same problem in all versions, see [advisory](https://github.com/plone/plone.app.contenttypes/security/advisories/GHSA-f7qw-5fgj-247x).
For all unpatched versions of the three packages, you can use the following workaround.
### Workaround
Make sure the image_view_fullscreen page is not stored in the cache.
In Plone:
* Login as Manager and go to Site Setup.
* Go to the 'Caching' control panel. If this does not exist, or 'Enable caching' is not checked, you should normally not be vulnerable.
* Click on the tab 'Caching operations'.
* Under 'Legacy template mappings' locate the ruleset 'Content item view'.
* From the last column ('Templates') remove 'image_view_fullscreen'.
* Click on Save.
### Reporter
This vulnerability was responsibly disclosed to the Plone Security Team by Gustav Hansen, F-Secure Consulting. Thank you!
### For more information
If you have any questions or comments about this advisory, email us at [security@plone.org](mailto:security@plone.org)
This is also the correct address to use when you want to report a possible vulnerability.
See [our security report policy](https://plone.org/security/report). | {'CVE-2022-23599'} | 2022-03-03T05:13:09.714324Z | 2022-01-28T23:10:37Z | MODERATE | null | {'CWE-79'} | {'https://github.com/plone/Products.ATContentTypes/security/advisories/GHSA-g4c2-ghfg-g5rh', 'https://github.com/plone/Products.ATContentTypes/', 'https://github.com/plone/Products.ATContentTypes/commit/fc793f88f35a15a68b52e4abed77af0da5fdbab8', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23599'} | null | {'https://github.com/plone/Products.ATContentTypes/commit/fc793f88f35a15a68b52e4abed77af0da5fdbab8'} | {'https://github.com/plone/Products.ATContentTypes/commit/fc793f88f35a15a68b52e4abed77af0da5fdbab8'} |
PyPI | PYSEC-2021-806 | null | TensorFlow is an open source platform for machine learning. In affected versions if `tf.tile` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow. The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process. 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-2p25-55c9-h58q', 'CVE-2021-41198'} | 2021-12-09T06:35:40.903537Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/9294094df6fea79271778eb7e7ae1bad8b5ef98f', 'https://github.com/tensorflow/tensorflow/issues/46911', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q'} | null | {'https://github.com/tensorflow/tensorflow/commit/9294094df6fea79271778eb7e7ae1bad8b5ef98f'} | {'https://github.com/tensorflow/tensorflow/commit/9294094df6fea79271778eb7e7ae1bad8b5ef98f'} |
PyPI | GHSA-mxjj-953w-2c2v | Data corruption in tensorflow-lite | ### Impact
When determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/types.h#L437-L442
Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors.
### Patches
We have patched the issue in 8ee24e7949a20 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15208'} | 2022-03-03T05:13:53.988672Z | 2020-09-25T18:28:44Z | HIGH | null | {'CWE-787', 'CWE-125'} | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15208', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d'} | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d'} |
PyPI | GHSA-vqw6-72r7-fgw7 | OOB read in `MatrixTriangularSolve` | ### Impact
The implementation of [`MatrixTriangularSolve`](https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails:
```cc
void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0,
const Tensor& in1) override {
OP_REQUIRES(
ctx, in0.dims() >= 2,
errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));
OP_REQUIRES(
ctx, in1.dims() >= 2,
errors::InvalidArgument("In[0] ndims must be >= 2: ", in1.dims()));
}
void Compute(OpKernelContext* ctx) override {
const Tensor& in0 = ctx->input(0);
const Tensor& in1 = ctx->input(1);
ValidateInputTensors(ctx, in0, in1);
MatMulBCast bcast(in0.shape().dim_sizes(), in1.shape().dim_sizes());
...
}
```
Since `OP_REQUIRES` only sets `ctx->status()` to a non-OK value and calls `return`, this allows malicious attackers to trigger an out of bounds read:
```python
import tensorflow as tf
import numpy as np
matrix_array = np.array([])
matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(1,0)),dtype=tf.float32)
rhs_array = np.array([])
rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(0,1)),dtype=tf.float32)
tf.raw_ops.MatrixTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor,lower=False,adjoint=False)
```
As the two input tensors are empty, the `OP_REQUIRES` in `ValidateInputTensors` should fire and interrupt execution. However, given the implementation of `OP_REQUIRES`, after the `in0.dims() >= 2` fails, execution moves to the initialization of the `bcast` object. This initialization is done with invalid data and results in heap OOB read.
### Patches
We have patched the issue in GitHub commit [480641e3599775a8895254ffbc0fc45621334f68](https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68).
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 Ye Zhang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29551'} | 2022-03-03T05:14:11.910094Z | 2021-05-21T14:23:44Z | LOW | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29551', 'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vqw6-72r7-fgw7'} | null | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68'} | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68'} |
PyPI | PYSEC-2021-555 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. 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-2021-37642', 'GHSA-ch4f-829c-v5pw'} | 2021-12-09T06:35:02.576943Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ch4f-829c-v5pw'} | null | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} |
PyPI | PYSEC-2021-410 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll` can be made to execute a division by 0. This occurs whenever the `split_count` argument is 0. 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-2021-41218', 'GHSA-9crf-c6qr-r273'} | 2021-11-13T06:52:44.955817Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9crf-c6qr-r273', 'https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc'} | null | {'https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc'} | {'https://github.com/tensorflow/tensorflow/commit/a8ad3e5e79c75f36edb81e0ba3f3c0c5442aeddc'} |
PyPI | GHSA-786j-5qwq-r36x | Segfault while copying constant resource tensor | ### Impact
During TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change.
### Patches
We have patched the issue in GitHub commit [7731e8dfbe4a56773be5dc94d631611211156659](https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659).
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 | {'CVE-2021-41204'} | 2022-03-03T05:14:03.106377Z | 2021-11-10T19:12:14Z | MODERATE | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41204'} | null | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} |
PyPI | PYSEC-2016-6 | null | Buffer overflow in the ImagingFliDecode function in libImaging/FliDecode.c in Pillow before 3.1.1 allows remote attackers to cause a denial of service (crash) via a crafted FLI file. | {'GHSA-8xjv-v9xq-m5h9', 'CVE-2016-0775'} | 2021-07-05T00:01:23.974761Z | 2016-04-13T16:59:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commit/893a40850c2d5da41537958e40569c029a6e127b', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst', 'https://github.com/advisories/GHSA-8xjv-v9xq-m5h9', 'http://www.debian.org/security/2016/dsa-3499'} | null | {'https://github.com/python-pillow/Pillow/commit/893a40850c2d5da41537958e40569c029a6e127b'} | {'https://github.com/python-pillow/Pillow/commit/893a40850c2d5da41537958e40569c029a6e127b'} |
PyPI | PYSEC-2022-23 | null | Server-Side Request Forgery (SSRF) in Pypi calibreweb prior to 0.6.16. | {'CVE-2022-0339', 'GHSA-4w8p-x6g8-fv64'} | 2022-02-04T19:19:26.704356Z | 2022-01-30T14:15:00Z | null | null | null | {'https://github.com/janeczku/calibre-web/commit/3b216bfa07ec7992eff03e55d61732af6df9bb92', 'https://huntr.dev/bounties/499688c4-6ac4-4047-a868-7922c3eab369', 'https://github.com/advisories/GHSA-4w8p-x6g8-fv64'} | null | {'https://github.com/janeczku/calibre-web/commit/3b216bfa07ec7992eff03e55d61732af6df9bb92'} | {'https://github.com/janeczku/calibre-web/commit/3b216bfa07ec7992eff03e55d61732af6df9bb92'} |
PyPI | PYSEC-2020-69 | null | Python oic is a Python OpenID Connect implementation. In Python oic before version 1.2.1, there are several related cryptographic issues affecting client implementations that use the library. The issues are: 1) The IdToken signature algorithm was not checked automatically, but only if the expected algorithm was passed in as a kwarg. 2) JWA `none` algorithm was allowed in all flows. 3) oic.consumer.Consumer.parse_authz returns an unverified IdToken. The verification of the token was left to the discretion of the implementator. 4) iat claim was not checked for sanity (i.e. it could be in the future). These issues are patched in version 1.2.1. | {'CVE-2020-26244', 'GHSA-4fjv-pmhg-3rfg'} | 2020-12-08T02:37:00Z | 2020-12-02T20:15:00Z | null | null | null | {'https://github.com/OpenIDC/pyoidc/commit/62f8d753fa17c8b1f29f8be639cf0b33afb02498', 'https://github.com/OpenIDC/pyoidc/releases/tag/1.2.1', 'https://pypi.org/project/oic/', 'https://github.com/OpenIDC/pyoidc/security/advisories/GHSA-4fjv-pmhg-3rfg'} | null | {'https://github.com/OpenIDC/pyoidc/commit/62f8d753fa17c8b1f29f8be639cf0b33afb02498'} | {'https://github.com/OpenIDC/pyoidc/commit/62f8d753fa17c8b1f29f8be639cf0b33afb02498'} |
PyPI | PYSEC-2022-54 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalAvgPoolGrad` does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap. 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-vjg4-v33c-ggc4', 'CVE-2022-21730'} | 2022-03-09T00:17:30.562457Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360', 'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} | null | {'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} | {'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} |
PyPI | GHSA-428x-9xc2-m8mj | Division by zero in TFLite | ### Impact
An attacker can craft a TFLite model that would trigger a division by zero in [the implementation of depthwise convolutions](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96).
The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is stricly positive.
### Patches
We have patched the issue in GitHub commit [e5b0eec199c2d03de54fd6a7fd9275692218e2bc](https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team. | {'CVE-2022-21741'} | 2022-03-03T05:13:55.518660Z | 2022-02-09T23:47:30Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21741'} | null | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} |
PyPI | PYSEC-2021-467 | 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'} | 2021-12-09T06:34:49.452107Z | 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-2021-172 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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-2021-29535', 'GHSA-m3f9-w3p3-p669'} | 2021-08-27T03:22:27.629630Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669', 'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} | {'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} |
PyPI | GHSA-qhx9-7hx7-cp4r | HTTP Request smuggling in bottle | The package bottle before 0.12.19 are vulnerable to Web Cache Poisoning by using a vector called parameter cloaking. When the attacker can separate query parameters using a semicolon (;), they can cause a difference in the interpretation of the request between the proxy (running with default configuration) and the server. This can result in malicious requests being cached as completely safe ones, as the proxy would usually not see the semicolon as a separator, and therefore would not include it in a cache key of an unkeyed parameter. | {'CVE-2020-28473'} | 2022-03-03T05:12:40.159877Z | 2021-04-07T21:05:21Z | MODERATE | null | {'CWE-444'} | {'https://snyk.io/vuln/SNYK-PYTHON-BOTTLE-1017108', 'https://nvd.nist.gov/vuln/detail/CVE-2020-28473', 'https://snyk.io/blog/cache-poisoning-in-popular-open-source-packages/', 'https://lists.debian.org/debian-lts-announce/2021/01/msg00019.html', 'https://github.com/bottlepy/bottle/commit/57a2f22e0c1d2b328c4f54bf75741d74f47f1a6b'} | null | {'https://github.com/bottlepy/bottle/commit/57a2f22e0c1d2b328c4f54bf75741d74f47f1a6b'} | {'https://github.com/bottlepy/bottle/commit/57a2f22e0c1d2b328c4f54bf75741d74f47f1a6b'} |
PyPI | PYSEC-2021-488 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. 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-8gv3-57p6-g35r', 'CVE-2021-29560'} | 2021-12-09T06:34:52.680803Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8gv3-57p6-g35r'} | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} |
PyPI | PYSEC-2021-522 | 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'} | 2021-12-09T06:34:57.958093Z | 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 | GHSA-7qqv-r2q4-jxhm | High severity vulnerability that affects privacyIDEA | privacyIDEA version 2.23.1 and earlier contains a Improper Input Validation vulnerability in token validation api that can result in Denial-of-Service. This attack appear to be exploitable via http request with user=<space>&pass= to /validate/check url. This vulnerability appears to have been fixed in 2.23.2. | {'CVE-2018-1000809'} | 2022-03-03T05:13:06.315310Z | 2019-01-14T16:19:31Z | HIGH | null | {'CWE-20'} | {'https://github.com/privacyidea/privacyidea/commit/a3edc09beffa2104f357fe24971ea3211ce40751', 'https://github.com/advisories/GHSA-7qqv-r2q4-jxhm', 'https://github.com/privacyidea/privacyidea', 'https://github.com/privacyidea/privacyidea/issues/1227', 'https://nvd.nist.gov/vuln/detail/CVE-2018-1000809'} | null | {'https://github.com/privacyidea/privacyidea/commit/a3edc09beffa2104f357fe24971ea3211ce40751'} | {'https://github.com/privacyidea/privacyidea/commit/a3edc09beffa2104f357fe24971ea3211ce40751'} |
PyPI | PYSEC-2014-23 | null | The (1) JpegImagePlugin.py and (2) EpsImagePlugin.py scripts in Python Image Library (PIL) 1.1.7 and earlier and Pillow before 2.3.1 uses the names of temporary files on the command line, which makes it easier for local users to conduct symlink attacks by listing the processes. | {'CVE-2014-1933', 'GHSA-r854-96gq-rfg3'} | 2021-07-15T02:22:17.008543Z | 2014-04-17T14:55:00Z | null | null | null | {'http://www.openwall.com/lists/oss-security/2014/02/11/1', 'http://www.openwall.com/lists/oss-security/2014/02/10/15', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/advisories/GHSA-r854-96gq-rfg3', 'http://www.ubuntu.com/usn/USN-2168-1', 'http://www.securityfocus.com/bid/65513', 'http://lists.opensuse.org/opensuse-updates/2014-05/msg00002.html', 'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} | null | {'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} | {'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} |
PyPI | GHSA-xvjm-fvxx-q3hv | CHECK-fail due to integer overflow | ### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in caused by an integer overflow in constructing a new tensor shape:
```python
import tensorflow as tf
input_layer = 2**60-1
sparse_data = tf.raw_ops.SparseSplit(
split_dim=1,
indices=[(0, 0), (0, 1), (0, 2),
(4, 3), (5, 0), (5, 1)],
values=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
shape=(input_layer, input_layer),
num_split=2,
name=None
)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow:
```cc
sparse::SparseTensor sparse_tensor;
OP_REQUIRES_OK(context,
sparse::SparseTensor::Create(
input_indices, input_values,
TensorShape(input_shape.vec<int64>()), &sparse_tensor));
```
The [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status.
```cc
template <class Shape>
TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {
set_tag(REP16);
set_data_type(DT_INVALID);
TF_CHECK_OK(InitDims(dim_sizes));
}
```
In our scenario, this occurs when adding a dimension from the argument results in overflow:
```cc
template <class Shape>
Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {
...
Status status = Status::OK();
for (int64 s : dim_sizes) {
status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));
if (!status.ok()) {
return status;
}
}
}
template <class Shape>
Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {
...
int64 new_num_elements;
if (kIsPartial && (num_elements() < 0 || size < 0)) {
new_num_elements = -1;
} else {
new_num_elements = MultiplyWithoutOverflow(num_elements(), size);
if (TF_PREDICT_FALSE(new_num_elements < 0)) {
return errors::Internal("Encountered overflow when multiplying ",
num_elements(), " with ", size,
", result: ", new_num_elements);
}
}
...
}
```
This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows.
### Patches
We have patched the issue in GitHub commit [4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60](https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60).
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 researchers from University of Virginia and University of California, Santa Barbara. | {'CVE-2021-29584'} | 2022-03-03T05:13:43.995118Z | 2021-05-21T14:26:38Z | LOW | null | {'CWE-190'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29584', 'https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvjm-fvxx-q3hv'} | null | {'https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60'} | {'https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60'} |
PyPI | PYSEC-2021-44 | null | Products.PluggableAuthService is a pluggable Zope authentication and authorization framework. In Products.PluggableAuthService before version 2.6.0 there is an information disclosure vulnerability - everyone can list the names of roles defined in the ZODB Role Manager plugin if the site uses this plugin. The problem has been fixed in version 2.6.0. Depending on how you have installed Products.PluggableAuthService, you should change the buildout version pin to 2.6.0 and re-run the buildout, or if you used pip simply do `pip install "Products.PluggableAuthService>=2.6.0"`. | {'GHSA-p75f-g7gx-2r7p', 'CVE-2021-21336'} | 2021-03-12T13:22:00Z | 2021-03-08T21:15:00Z | null | null | null | {'https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p75f-g7gx-2r7p', 'https://pypi.org/project/Products.PluggableAuthService/', 'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} | null | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} |
PyPI | GHSA-xrx6-fmxq-rjj2 | Timing attacks in python-rsa | It was found that python-rsa is vulnerable to Bleichenbacher timing attacks. An attacker can use this flaw via the RSA decryption API to decrypt parts of the cipher text encrypted with RSA. | {'CVE-2020-25658'} | 2022-03-03T05:13:07.521047Z | 2021-04-30T17:35:15Z | MODERATE | null | {'CWE-385', 'CWE-327'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-25658', 'https://github.com/sybrenstuvel/python-rsa/commit/dae8ce0d85478e16f2368b2341632775313d41ed', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-25658', 'https://github.com/sybrenstuvel/python-rsa', 'https://github.com/sybrenstuvel/python-rsa/issues/165', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/APF364QJ2IYLPDNVFBOEJ24QP2WLVLJP/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QY4PJWTYSOV7ZEYZVMYIF6XRU73CY6O7/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2SAF67KDGSOHLVFTRDOHNEAFDRSSYIWA/'} | null | {'https://github.com/sybrenstuvel/python-rsa/commit/dae8ce0d85478e16f2368b2341632775313d41ed'} | {'https://github.com/sybrenstuvel/python-rsa/commit/dae8ce0d85478e16f2368b2341632775313d41ed'} |
PyPI | PYSEC-2021-430 | null | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') | {'CVE-2021-3945', 'GHSA-vx6v-xg64-pmr8'} | 2021-11-19T07:28:03.664110Z | 2021-11-13T09:15:00Z | null | null | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/2c7065e0c4296e0c692fb4a7ee19c7357583af30', 'https://github.com/advisories/GHSA-vx6v-xg64-pmr8', '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 | GHSA-6p56-wp2h-9hxr | Buffer Overflow in NumPy | A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. | {'CVE-2021-33430'} | 2022-03-03T05:13:06.724432Z | 2022-01-07T00:09:39Z | MODERATE | null | {'CWE-120'} | {'https://github.com/numpy/numpy', 'https://github.com/numpy/numpy/issues/18939', 'https://nvd.nist.gov/vuln/detail/CVE-2021-33430', 'https://github.com/numpy/numpy/commit/ae317fd9ff3e79c0eac357d723bfc29cbd625f2e'} | null | {'https://github.com/numpy/numpy/commit/ae317fd9ff3e79c0eac357d723bfc29cbd625f2e'} | {'https://github.com/numpy/numpy/commit/ae317fd9ff3e79c0eac357d723bfc29cbd625f2e'} |
PyPI | GHSA-fx5c-h9f6-rv7c | `CHECK`-fails due to attempting to build a reference tensor | ### Impact
A malicious user can cause a denial of service by altering a `SavedModel` such that [Grappler optimizer would attempt to build a tensor using a reference `dtype`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1328-L1402). This would result in a crash due to a `CHECK`-fail [in the `Tensor` constructor](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/tensor.cc#L733-L781) as reference types are not allowed.
### Patches
We have patched the issue in GitHub commit [6b5adc0877de832b2a7c189532dbbbc64622eeb6](https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6).
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-2022-23588'} | 2022-03-03T05:13:57.640120Z | 2022-02-09T23:28:07Z | MODERATE | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1328-L1402', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/tensor.cc#L733-L781', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23588', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fx5c-h9f6-rv7c'} | null | {'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6'} | {'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6'} |
PyPI | GHSA-gf88-j2mg-cc82 | Crash caused by integer conversion to unsigned | ### Impact
An attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments:
```python
import tensorflow as tf
from tensorflow.python.ops import gen_boosted_trees_ops
import numpy as np
v= tf.Variable([0.0, 0.0, 0.0, 0.0, 0.0])
gen_boosted_trees_ops.boosted_trees_create_quantile_stream_resource(
quantile_stream_resource_handle = v.handle,
epsilon = [74.82224],
num_streams = [-49],
max_elements = np.int32(586))
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40):
```cc
class BoostedTreesQuantileStreamResource : public ResourceBase {
public:
BoostedTreesQuantileStreamResource(const float epsilon,
const int64 max_elements,
const int64 num_streams)
: are_buckets_ready_(false),
epsilon_(epsilon),
num_streams_(num_streams),
max_elements_(max_elements) {
streams_.reserve(num_streams_);
...
}
}
```
However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library.
### Patches
We have patched the issue in GitHub commit [8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992](https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37661'} | 2022-03-03T05:13:57.633880Z | 2021-08-25T14:42:28Z | MODERATE | null | {'CWE-681'} | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37661', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} |
PyPI | PYSEC-2020-125 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15202', 'GHSA-h6fg-mjxg-hqq4'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832'} | {'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832'} |
PyPI | PYSEC-2021-826 | null | TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. 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. 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-2021-41219', 'GHSA-4f99-p9c2-3j8x'} | 2021-12-09T06:35:44.063409Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} |
PyPI | PYSEC-2021-575 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. 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-2021-37662', 'GHSA-f5cx-5wr3-5qrc'} | 2021-12-09T06:35:04.272005Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad', 'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f5cx-5wr3-5qrc'} | null | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} |
PyPI | GHSA-pfjj-m3jj-9jc9 | Undefined behavior in `SparseTensorSliceDataset` | ### Impact
The [implementation of `SparseTensorSliceDataset`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L227-L292) has an undefined behavior: under certain condition it can be made to dereference a `nullptr` value:
```python
import tensorflow as tf
import numpy as np
tf.raw_ops.SparseTensorSliceDataset(
indices=[[]],
values=[],
dense_shape=[1,1])
```
The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.
### Patches
We have patched the issue in GitHub commit [965b97e4a9650495cda5a8c210ef6684b4b9eceb](https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb).
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 Faysal Hossain Shezan from University of Virginia. | {'CVE-2022-21736'} | 2022-03-03T05:13:47.536491Z | 2022-02-09T23:43:27Z | HIGH | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L227-L292', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pfjj-m3jj-9jc9', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21736', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb'} | null | {'https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb'} | {'https://github.com/tensorflow/tensorflow/commit/965b97e4a9650495cda5a8c210ef6684b4b9eceb'} |
PyPI | GHSA-qhmp-h54x-38qr | CWE-730 Regex injection with IFTTT Plugin | ### Impact
Anyone _publicly_ hosting the Apprise library and granting them access to the IFTTT notification service.
### Patches
Update to Apprise v0.9.5.1
```bash
# Install Apprise v0.9.5.1 from PyPI
pip install apprise==0.9.5.1
```
The patch to the problem was performed [here](https://github.com/caronc/apprise/pull/436/files).
### Workarounds
Alternatively, if upgrading is not an option, you can safely remove the following file:
- `apprise/plugins/NotifyIFTTT.py`
The above will eliminate the ability to use IFTTT, but everything else will work smoothly.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Apprise](https://github.com/caronc/apprise/issues)
* Email me at [lead2gold@gmail.com](mailto:lead2gold@gmail.com)
### Additional Credit
Github would not allow me to additionally credit **Rasmus Petersen**, but I would like to put that here at the very least - thank you for finding and reporting this issue along with those already credited
## Additional Notes:
- Github would not allow me to add/tag the 2 CWE's this issue is applicable to (only CWE-400). The other is: CWE-730 (placed in the title)
| {'CVE-2021-39229'} | 2022-03-03T05:12:55.143444Z | 2021-09-20T20:57:02Z | HIGH | null | {'CWE-400'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-39229', 'https://github.com/caronc/apprise/pull/436', 'https://github.com/caronc/apprise/blob/0007eade20934ddef0aba38b8f1aad980cfff253/apprise/plugins/NotifyIFTTT.py#L356-L359', 'https://github.com/caronc/apprise/security/advisories/GHSA-qhmp-h54x-38qr', 'https://github.com/caronc/apprise', 'https://github.com/caronc/apprise/commit/e20fce630d55e4ca9b0a1e325a5fea6997489831', 'https://github.com/caronc/apprise/releases/tag/v0.9.5.1'} | null | {'https://github.com/caronc/apprise/commit/e20fce630d55e4ca9b0a1e325a5fea6997489831'} | {'https://github.com/caronc/apprise/commit/e20fce630d55e4ca9b0a1e325a5fea6997489831'} |
PyPI | PYSEC-2021-719 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `BatchToSpaceNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-cfx7-2xpc-8w4h', 'CVE-2021-29593'} | 2021-12-09T06:35:30.927051Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfx7-2xpc-8w4h', 'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} | null | {'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} | {'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} |
PyPI | GHSA-7ghq-fvr3-pj2x | Incomplete validation in `MaxPoolGrad` | ### Impact
An attacker can trigger a denial of service via a segmentation fault in `tf.raw_ops.MaxPoolGrad` caused by missing validation:
```python
import tensorflow as tf
tf.raw_ops.MaxPoolGrad(
orig_input = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
orig_output = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
grad = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
ksize = [1, 16, 16, 1],
strides = [1, 16, 18, 1],
padding = "EXPLICIT",
explicit_paddings = [0, 0, 14, 3, 15, 5, 0, 0])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the `orig_input` and `orig_output` tensors.
The fixes for [CVE-2021-29579](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-068.md) were incomplete.
### Patches
We have patched the issue in GitHub commit [136b51f10903e044308cf77117c0ed9871350475](https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security. | {'CVE-2021-37674'} | 2022-03-03T05:13:27.100453Z | 2021-08-25T14:41:33Z | MODERATE | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7ghq-fvr3-pj2x', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-068.md', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37674'} | null | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} |
PyPI | GHSA-8xjq-8fcg-g5hw | Out-of-bounds Write in Pillow | An issue was discovered in Pillow before 8.1.1. In TiffDecode.c, there is a negative-offset memcpy with an invalid size. | {'CVE-2021-25290'} | 2022-03-03T05:13:02.853238Z | 2021-03-29T16:35:36Z | HIGH | null | {'CWE-787'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25290', 'https://github.com/python-pillow/Pillow/commit/86f02f7c70862a0954bfe8133736d352db978eaa', 'https://security.gentoo.org/glsa/202107-33', 'https://lists.debian.org/debian-lts-announce/2021/07/msg00018.html'} | null | {'https://github.com/python-pillow/Pillow/commit/86f02f7c70862a0954bfe8133736d352db978eaa'} | {'https://github.com/python-pillow/Pillow/commit/86f02f7c70862a0954bfe8133736d352db978eaa'} |
PyPI | GHSA-xgc3-m89p-vr3x | Heap buffer overflow in `Conv2DBackpropFilter` | ### Impact
An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`:
```python
import tensorflow as tf
input_tensor = tf.constant([386.078431372549, 386.07843139643234],
shape=[1, 1, 1, 2], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1],
dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 49, 1],
use_cudnn_on_gpu=True,
padding='VALID',
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1]
)
```
Alternatively, passing empty tensors also results in similar behavior:
```python
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32)
filter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 49, 1],
use_cudnn_on_gpu=True,
padding='VALID',
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1]
)
```
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.
### Patches
We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).
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-2021-29540'} | 2022-03-03T05:12:50.923287Z | 2021-05-21T14:23:09Z | LOW | null | {'CWE-120', 'CWE-787'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x', 'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29540'} | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} |
PyPI | GHSA-3c45-wgjp-7v9r | A single version of twisted does not respect the trustedRoot setting | Python Twisted 14.0 trustRoot is not respected in HTTP client | {'CVE-2014-7143'} | 2021-08-19T16:07:48Z | 2019-12-17T22:52:34Z | HIGH | null | {'CWE-295'} | {'https://nvd.nist.gov/vuln/detail/CVE-2014-7143', 'https://github.com/twisted/twisted/commit/3b5942252f5f3e45862a0e12b266ab29e243cc33', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/96135', 'https://security-tracker.debian.org/tracker/CVE-2014-7143', 'http://www.openwall.com/lists/oss-security/2014/09/22/2', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2014-7143'} | null | {'https://github.com/twisted/twisted/commit/3b5942252f5f3e45862a0e12b266ab29e243cc33'} | {'https://github.com/twisted/twisted/commit/3b5942252f5f3e45862a0e12b266ab29e243cc33'} |
PyPI | PYSEC-2021-133 | null | Synapse is a Matrix reference homeserver written in python (pypi package matrix-synapse). Matrix is an ecosystem for open federated Instant Messaging and VoIP. In Synapse before version 1.27.0, the password reset endpoint served via Synapse was vulnerable to cross-site scripting (XSS) attacks. The impact depends on the configuration of the domain that Synapse is deployed on, but may allow access to cookies and other browser data, CSRF vulnerabilities, and access to other resources served on the same domain or parent domains. This is fixed in version 1.27.0. | {'GHSA-246w-56m2-5899', 'CVE-2021-21332'} | 2021-08-27T03:22:06.660066Z | 2021-03-26T20:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df', 'https://github.com/matrix-org/synapse/releases/tag/v1.27.0', 'https://github.com/matrix-org/synapse/pull/9200', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-246w-56m2-5899'} | null | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} |
PyPI | GHSA-77gc-v2xv-rvvh | Out-of-bounds Read in Pillow | An issue was discovered in Pillow before 8.2.0. There is an out-of-bounds read in J2kDecode, in j2ku_graya_la. | {'CVE-2021-25287'} | 2022-03-07T20:48:08.139905Z | 2021-06-08T18:49:02Z | CRITICAL | null | {'CWE-125'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-25287-cve-2021-25288-fix-oob-read-in-jpeg2kdecode', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25287', 'https://github.com/python-pillow/Pillow/pull/5377/commits/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87', 'https://github.com/python-pillow/Pillow/pull/5377#issuecomment-833821470', 'https://security.gentoo.org/glsa/202107-33', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/', 'https://github.com/python-pillow/Pillow/commit/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87', 'https://github.com/python-pillow/Pillow/pull/5377/commits/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87'} | {'https://github.com/python-pillow/Pillow/commit/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87', 'https://github.com/python-pillow/Pillow/pull/5377/commits/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87'} |
PyPI | PYSEC-2021-563 | 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'} | 2021-12-09T06:35:03.262683Z | 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-2021-830 | 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'} | 2021-12-09T06:35:44.623762Z | 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 | PYSEC-2020-133 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15210', 'GHSA-x9j7-x98r-r4w2'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453'} | {'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453'} |
PyPI | PYSEC-2018-41 | null | Ansible before versions 2.3.1.0 and 2.4.0.0 fails to properly mark lookup-plugin results as unsafe. If an attacker could control the results of lookup() calls, they could inject Unicode strings to be parsed by the jinja2 templating system, resulting in code execution. By default, the jinja2 templating language is now marked as 'unsafe' and is not evaluated. | {'GHSA-w578-j992-554x', 'CVE-2017-7481'} | 2021-07-02T02:41:33.849138Z | 2018-07-19T13:29:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2017:1599', 'https://github.com/advisories/GHSA-w578-j992-554x', 'https://access.redhat.com/errata/RHSA-2017:1244', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2017-7481', 'https://access.redhat.com/errata/RHSA-2017:1499', 'http://www.securityfocus.com/bid/98492', 'https://access.redhat.com/errata/RHSA-2017:1334', 'https://access.redhat.com/errata/RHSA-2017:2524', 'https://usn.ubuntu.com/4072-1/', 'https://github.com/ansible/ansible/commit/ed56f51f185a1ffd7ea57130d260098686fcc7c2', 'https://lists.debian.org/debian-lts-announce/2021/01/msg00023.html', 'https://access.redhat.com/errata/RHSA-2017:1476'} | null | {'https://github.com/ansible/ansible/commit/ed56f51f185a1ffd7ea57130d260098686fcc7c2'} | {'https://github.com/ansible/ansible/commit/ed56f51f185a1ffd7ea57130d260098686fcc7c2'} |
PyPI | GHSA-f248-v4qh-x2r6 | Improper Certificate Validation in blackduck | Synopsys hub-rest-api-python (aka blackduck on PyPI) version 0.0.25 - 0.0.52 does not validate SSL certificates in certain cases. | {'CVE-2020-27589'} | 2022-03-03T05:13:47.871644Z | 2021-04-20T16:29:41Z | HIGH | null | {'CWE-295'} | {'https://github.com/blackducksoftware/hub-rest-api-python/pull/113/commits/273b27d0de1004389dd8cf43c40b1197c787e7cd', 'https://pypi.org/project/blackduck/', 'https://community.synopsys.com/s/question/0D52H00005JCZAXSA5/announcement-black-duck-defect-identified', 'https://github.com/blackducksoftware/hub-rest-api-python', 'https://www.optiv.com/explore-optiv-insights/source-zero/certificate-validation-disabled-black-duck-api-wrapper', 'https://nvd.nist.gov/vuln/detail/CVE-2020-27589'} | null | {'https://github.com/blackducksoftware/hub-rest-api-python/pull/113/commits/273b27d0de1004389dd8cf43c40b1197c787e7cd'} | {'https://github.com/blackducksoftware/hub-rest-api-python/pull/113/commits/273b27d0de1004389dd8cf43c40b1197c787e7cd'} |
PyPI | PYSEC-2017-72 | null | sosreport 3.2 uses weak permissions for generated sosreport archives, which allows local users with access to /var/tmp/ to obtain sensitive information by reading the contents of the archive. | {'CVE-2015-3171'} | 2021-07-25T23:34:55.539428Z | 2017-07-25T18:29:00Z | null | null | null | {'https://github.com/sosreport/sos/commit/d7759d3ddae5fe99a340c88a1d370d65cfa73fd6', 'https://bugzilla.redhat.com/show_bug.cgi?id=1218658'} | null | {'https://github.com/sosreport/sos/commit/d7759d3ddae5fe99a340c88a1d370d65cfa73fd6'} | {'https://github.com/sosreport/sos/commit/d7759d3ddae5fe99a340c88a1d370d65cfa73fd6'} |
PyPI | PYSEC-2021-471 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. 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-fphq-gw9m-ghrv', 'CVE-2021-29543'} | 2021-12-09T06:34:50.046503Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv', 'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} |
PyPI | GHSA-85rr-4rh9-hhwh | Memory leak in Nanopb | ### Impact
Decoding specifically formed message can leak memory if dynamic allocation is enabled and an oneof field contains a static submessage that contains a dynamic field, and the message being decoded contains the submessage multiple times. This is rare in normal messages, but it is a concern when untrusted data is parsed.
### Patches
Preliminary patch is [available on git](https://github.com/nanopb/nanopb/commit/edf6dcbffee4d614ac0c2c1b258ab95185bdb6e9) and problem will be patched in versions 0.3.9.7 and 0.4.4 once testing has been completed.
### Workarounds
Following workarounds are available:
* Set the option `no_unions` for the oneof field. This will generate fields as separate instead of C union, and avoids triggering the problematic code.
* Set the type of the submessage field inside oneof to `FT_POINTER`. This way the whole submessage will be dynamically allocated and the problematic code is not executed.
* Use an arena allocator for nanopb, to make sure all memory can be released afterwards.
### References
Bug report: https://github.com/nanopb/nanopb/issues/615
### For more information
If you have any questions or comments about this advisory, comment on the bug report linked above. | {'CVE-2020-26243'} | 2022-03-03T05:14:19.479674Z | 2020-11-25T16:53:27Z | MODERATE | null | {'CWE-20', 'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-26243', 'https://github.com/nanopb/nanopb/commit/4fe23595732b6f1254cfc11a9b8d6da900b55b0c', 'https://github.com/nanopb/nanopb/security/advisories/GHSA-85rr-4rh9-hhwh', 'https://github.com/nanopb/nanopb/issues/615', 'https://github.com/nanopb/nanopb/blob/2b48a361786dfb1f63d229840217a93aae064667/CHANGELOG.txt'} | null | {'https://github.com/nanopb/nanopb/commit/4fe23595732b6f1254cfc11a9b8d6da900b55b0c'} | {'https://github.com/nanopb/nanopb/commit/4fe23595732b6f1254cfc11a9b8d6da900b55b0c'} |
PyPI | GHSA-384w-5v3f-q499 | Base class whitelist configuration ignored in OAuthenticator | ### Impact
__What goes wrong?__
The deprecated (in jupyterhub 1.2) configuration `Authenticator.whitelist`, which should be transparently mapped to `Authenticator.allowed_users` with a warning, is instead ignored by OAuthenticator classes, resulting in the same behavior as if this configuration has not been set. If this is the only mechanism of authorization restriction (i.e. no group or team restrictions in configuration) then all authenticated users will be allowed. Provider-based restrictions, including deprecated values such as `GitHubOAuthenticator.org_whitelist` are **not** affected.
__Who is impacted?__
All users of OAuthenticator 0.12.0 and 0.12.1 with JupyterHub 1.2 (JupyterHub Helm chart 0.10.0-0.10.5) who use the `admin.whitelist.users` configuration in the jupyterhub helm chart or the `c.Authenticator.whitelist` configuration directly. Users of other deprecated configuration, e.g. `c.GitHubOAuthenticator.team_whitelist` are **not** affected.
If you see a log line like this and expect a specific list of allowed usernames:
```
[I 2020-11-27 16:51:54.528 JupyterHub app:1717] Not using allowed_users. Any authenticated user will be allowed.
```
you are likely affected.
### Patches
- Replacing deprecated `c.Authenticator.whitelist = ...` with `c.Authenticator.allowed_users = ...` avoids the issue.
- Update oauthenticator to 0.12.2
- Update jupyterhub helm chart to 0.10.6
If any users have been authorized during this time who should not have been, they must be deleted via the API or admin interface, [per the documentation](https://jupyterhub.readthedocs.io/en/1.2.2/getting-started/authenticators-users-basics.html#add-or-remove-users-from-the-hub).
### Workarounds
Replacing `c.Authenticator.whitelist = ...` with `c.Authenticator.allowed_users = ...` avoids the issue.
In the jupyterhub helm chart prior to 0.10.6, this can be done via `hub.extraConfig`:
```yaml
auth:
allowedUsers:
- user1
- user2
hub:
extraConfig:
allowedUsers: |
# set new field not exposed in helm chart < 0.10.6
set_config_if_not_none(c.Authenticator, "allowed_users", "auth.allowedUsers")
```
### For more information
If you have any questions or comments about this advisory:
* Open a thread [on the Jupyter forum](http://discourse.jupyter.org)
* Email us at [security@ipython.org](mailto:security@ipython.org) | {'CVE-2020-26250'} | 2022-03-03T05:12:02.627667Z | 2020-12-01T20:25:00Z | HIGH | null | {'CWE-863'} | {'https://jupyterhub.readthedocs.io/en/1.2.2/getting-started/authenticators-users-basics.html#add-or-remove-users-from-the-hub', 'https://github.com/jupyterhub/oauthenticator/security/advisories/GHSA-384w-5v3f-q499', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26250', 'https://github.com/jupyterhub/oauthenticator/blob/master/docs/source/changelog.md#0122---2020-11-30', 'https://github.com/jupyterhub/oauthenticator/commit/a4aac191c16cf6281f3d346615aefa75702b02d7'} | null | {'https://github.com/jupyterhub/oauthenticator/commit/a4aac191c16cf6281f3d346615aefa75702b02d7'} | {'https://github.com/jupyterhub/oauthenticator/commit/a4aac191c16cf6281f3d346615aefa75702b02d7'} |
PyPI | GHSA-gcvh-66ff-4mwm | `CHECK`-failures in Tensorflow | ### Impact
The [implementation of `MapStage`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/map_stage_op.cc#L519-L550) is vulnerable a `CHECK`-fail if the key tensor is not a scalar:
```python
import tensorflow as tf
import numpy as np
tf.raw_ops.MapStage(
key = tf.constant(value=[4], shape= (1,2), dtype=tf.int64),
indices = np.array([[6]]),
values = np.array([-60]),
dtypes = [tf.int64], capacity=0, memory_limit=0,
container='', shared_name='', name=None
)
```
### Patches
We have patched the issue in GitHub commit [f57315566d7094f322b784947093406c2aea0d7d](https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d).
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 Faysal Hossain Shezan from University of Virginia.
~ | {'CVE-2022-21734'} | 2022-03-03T05:13:56.683466Z | 2022-02-10T00:21:12Z | MODERATE | null | {'CWE-617', 'CWE-843'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21734', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/map_stage_op.cc#L519-L550', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} | null | {'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} | {'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} |
PyPI | PYSEC-2021-867 | null | Gerapy is a distributed crawler management framework. Gerapy prior to version 0.9.8 is vulnerable to remote code execution, and this issue is patched in version 0.9.8. | {'CVE-2021-43857', 'GHSA-9w7f-m4j4-j3xw'} | 2022-01-07T19:22:06.271375Z | 2021-12-27T19:15:00Z | null | null | null | {'https://github.com/Gerapy/Gerapy/commit/49bcb19be5e0320e7e1535f34fe00f16a3cf3b28', 'http://packetstormsecurity.com/files/165459/Gerapy-0.9.7-Remote-Code-Execution.html', 'https://github.com/Gerapy/Gerapy/issues/219', 'https://github.com/Gerapy/Gerapy/security/advisories/GHSA-9w7f-m4j4-j3xw'} | null | {'https://github.com/Gerapy/Gerapy/commit/49bcb19be5e0320e7e1535f34fe00f16a3cf3b28'} | {'https://github.com/Gerapy/Gerapy/commit/49bcb19be5e0320e7e1535f34fe00f16a3cf3b28'} |
PyPI | PYSEC-2021-534 | null | TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. 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-2021-29606', 'GHSA-h4pc-gx2w-f2xv'} | 2021-12-09T06:34:59.860176Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h4pc-gx2w-f2xv', 'https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412'} | null | {'https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412'} | {'https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412'} |
PyPI | GHSA-8h2j-cgx8-6xv7 | Cross-Site Request Forgery (CSRF) in FastAPI | ### Impact
FastAPI versions lower than `0.65.2` that used cookies for authentication in path operations that received JSON payloads sent by browsers were vulnerable to a Cross-Site Request Forgery (CSRF) attack.
In versions lower than `0.65.2`, FastAPI would try to read the request payload as JSON even if the `content-type` header sent was not set to `application/json` or a compatible JSON media type (e.g. `application/geo+json`).
So, a request with a content type of `text/plain` containing JSON data would be accepted and the JSON data would be extracted.
But requests with content type `text/plain` are exempt from [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) preflights, for being considered [Simple requests](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS#simple_requests). So, the browser would execute them right away including cookies, and the text content could be a JSON string that would be parsed and accepted by the FastAPI application.
### Patches
This is fixed in FastAPI `0.65.2`.
The request data is now parsed as JSON only if the `content-type` header is `application/json` or another JSON compatible media type like `application/geo+json`.
### Workarounds
It's best to upgrade to the latest FastAPI.
But still, it would be possible to add a middleware or a dependency that checks the `content-type` header and aborts the request if it is not `application/json` or another JSON compatible content type.
### References
* [CORS on Mozilla web docs](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS)
* [This answer on StackExchange](https://security.stackexchange.com/questions/157528/ways-to-bypass-browsers-cors-policy/157531#157531)
* [OWASP CSRF](https://owasp.org/www-community/attacks/csrf)
* Fixed in PR [#2118](https://github.com/tiangolo/fastapi/pull/2118)
### For more information
If you have any questions or comments, write to [security@tiangolo.com](mailto:security@tiangolo.com) | {'CVE-2021-32677'} | 2022-03-03T05:13:41.740751Z | 2021-06-10T15:43:54Z | HIGH | null | {'CWE-352'} | {'https://github.com/tiangolo/fastapi/commit/fa7e3c996edf2d5482fff8f9d890ac2390dede4d', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MATAWX25TYKNEKLDMKWNLYDB34UWTROA/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32677', 'https://github.com/tiangolo/fastapi/security/advisories/GHSA-8h2j-cgx8-6xv7'} | null | {'https://github.com/tiangolo/fastapi/commit/fa7e3c996edf2d5482fff8f9d890ac2390dede4d'} | {'https://github.com/tiangolo/fastapi/commit/fa7e3c996edf2d5482fff8f9d890ac2390dede4d'} |
PyPI | PYSEC-2021-164 | 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'} | 2021-08-27T03:22:26.181060Z | 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-2021-308 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version. | {'CVE-2021-37686', 'GHSA-mhhc-q96p-mfm9'} | 2021-08-27T03:22:47.333103Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mhhc-q96p-mfm9', 'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} | null | {'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} | {'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} |
PyPI | GHSA-57h3-9rgr-c24m | Out of bounds write in Pillow | An issue was discovered in Pillow before 8.1.1. TiffDecode has a heap-based buffer overflow when decoding crafted YCbCr files because of certain interpretation conflicts with LibTIFF in RGBA mode. NOTE: this issue exists because of an incomplete fix for CVE-2020-35654. | {'CVE-2021-25289'} | 2021-12-02T17:48:12Z | 2021-03-29T16:35:16Z | HIGH | null | {'CWE-787'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25289', 'https://security.gentoo.org/glsa/202107-33', 'https://github.com/python-pillow/Pillow/', 'https://github.com/python-pillow/Pillow/commit/3fee28eb9479bf7d59e0fa08068f9cc4a6e2f04c'} | null | {'https://github.com/python-pillow/Pillow/commit/3fee28eb9479bf7d59e0fa08068f9cc4a6e2f04c'} | {'https://github.com/python-pillow/Pillow/commit/3fee28eb9479bf7d59e0fa08068f9cc4a6e2f04c'} |
PyPI | PYSEC-2021-758 | null | TensorFlow is an end-to-end open source platform for machine learning. When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not. If `indices` is empty, then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector). We have patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-c5x2-p679-95wc', 'CVE-2021-37647'} | 2021-12-09T06:35:36.390179Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c5x2-p679-95wc', 'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} | null | {'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} | {'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} |
PyPI | PYSEC-2017-25 | null | XML External Entity (XXE) vulnerability in PySAML2 4.4.0 and earlier allows remote attackers to read arbitrary files via a crafted SAML XML request or response. | {'GHSA-c2vx-49jm-h3f6', 'CVE-2016-10149'} | 2021-07-05T00:01:25.139700Z | 2017-03-24T14:59:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2017:0937', 'https://github.com/rohe/pysaml2/pull/379', 'https://github.com/advisories/GHSA-c2vx-49jm-h3f6', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=850716', 'https://access.redhat.com/errata/RHSA-2017:0936', 'http://www.openwall.com/lists/oss-security/2017/01/19/5', 'http://www.securityfocus.com/bid/97692', 'https://github.com/rohe/pysaml2/issues/366', 'https://github.com/rohe/pysaml2/commit/6e09a25d9b4b7aa7a506853210a9a14100b8bc9b', 'https://access.redhat.com/errata/RHSA-2017:0938', 'http://www.debian.org/security/2017/dsa-3759'} | null | {'https://github.com/rohe/pysaml2/commit/6e09a25d9b4b7aa7a506853210a9a14100b8bc9b'} | {'https://github.com/rohe/pysaml2/commit/6e09a25d9b4b7aa7a506853210a9a14100b8bc9b'} |
PyPI | PYSEC-2020-308 | null | In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15193', 'GHSA-rjjg-hgv6-h69v'} | 2021-12-09T06:35:12.446415Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v', '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 | GHSA-fpcp-9h7m-ffpx | Null pointer dereference in TensorFlow | ### Impact
When [building an XLA compilation cache](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/jit/xla_platform_info.cc#L43-L104), if default settings are used, TensorFlow triggers a null pointer dereference:
```cc
string allowed_gpus =
flr->config_proto()->gpu_options().visible_device_list();
```
In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`.
### Patches
We have patched the issue in GitHub commit [e21af685e1828f7ca65038307df5cc06de4479e8](https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8).
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-2022-23595'} | 2022-03-03T05:13:33.436421Z | 2022-02-09T23:33:17Z | MODERATE | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx', 'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23595', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/jit/xla_platform_info.cc#L43-L104'} | null | {'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} | {'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} |
PyPI | PYSEC-2022-116 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. 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-2022-21737', 'GHSA-f2vv-v9cg-qhh7'} | 2022-03-09T00:18:24.620644Z | 2022-02-03T14:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7', 'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} | null | {'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} | {'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} |
PyPI | PYSEC-2021-373 | null | Cobbler before 3.3.0 allows log poisoning, and resultant Remote Code Execution, via an XMLRPC method that logs to the logfile for template injection. | {'CVE-2021-40323', 'GHSA-cpqf-3c3r-c9g2'} | 2021-10-19T21:47:31.690816Z | 2021-10-04T06:15:00Z | null | null | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a', 'https://github.com/advisories/GHSA-cpqf-3c3r-c9g2', 'https://github.com/cobbler/cobbler/releases/tag/v3.3.0'} | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} |
PyPI | GHSA-6qgm-fv6v-rfpv | Overflow/denial of service in `tf.raw_ops.ReverseSequence` | ### Impact
The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service.
```python
import tensorflow as tf
input = tf.zeros([1, 1, 1], dtype=tf.int32)
seq_lengths = tf.constant([0], shape=[1], dtype=tf.int32)
tf.raw_ops.ReverseSequence(
input=input, seq_lengths=seq_lengths, seq_dim=-2, batch_dim=0)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid.
Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`.
### Patches
We have patched the issue in GitHub commit [ecf768cbe50cedc0a45ce1ee223146a3d3d26d23](https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23).
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-2021-29575'} | 2022-03-03T05:13:20.049464Z | 2021-05-21T14:26:13Z | LOW | null | {'CWE-120', 'CWE-119'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6qgm-fv6v-rfpv', 'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29575'} | null | {'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} | {'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} |
PyPI | PYSEC-2021-689 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-ph87-fvjr-v33w', 'CVE-2021-29563'} | 2021-12-09T06:35:25.642142Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ph87-fvjr-v33w'} | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} |
PyPI | GHSA-vqj2-4v8m-8vrq | Insecure Temporary File in mlflow | mlflow prior to 1.23.1 contains an insecure temporary file. The insecure function `tempfile.mktemp()` is deprecated and `mkstemp()` should be used instead. | {'CVE-2022-0736'} | 2022-03-07T20:47:28.413101Z | 2022-02-24T00:00:54Z | HIGH | null | {'CWE-668', 'CWE-377'} | {'https://github.com/mlflow/mlflow', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0736', 'https://huntr.dev/bounties/e5384764-c583-4dec-a1d8-4697f4e12f75', 'https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711'} | null | {'https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711'} | {'https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711'} |
PyPI | PYSEC-2021-723 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SpaceToBatchNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/412c7d9bb8f8a762c5b266c9e73bfa165f29aac8/tensorflow/lite/kernels/space_to_batch_nd.cc#L82-L83). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-v52p-hfjf-wg88', 'CVE-2021-29597'} | 2021-12-09T06:35:31.566408Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708', '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 | GHSA-232r-66cg-79px | Paramiko not properly checking authention before processing other requests | transport.py in the SSH server implementation of Paramiko before 1.17.6, 1.18.x before 1.18.5, 2.0.x before 2.0.8, 2.1.x before 2.1.5, 2.2.x before 2.2.3, 2.3.x before 2.3.2, and 2.4.x before 2.4.1 does not properly check whether authentication is completed before processing other requests, as demonstrated by channel-open. A customized SSH client can simply skip the authentication step. | {'CVE-2018-7750'} | 2022-04-26T20:45:08.475857Z | 2018-07-12T20:29:30Z | CRITICAL | null | {'CWE-287'} | {'https://access.redhat.com/errata/RHSA-2018:1274', 'http://www.securityfocus.com/bid/103713', 'https://github.com/paramiko/paramiko', 'https://lists.debian.org/debian-lts-announce/2018/10/msg00018.html', 'https://usn.ubuntu.com/3603-2/', 'https://github.com/advisories/GHSA-232r-66cg-79px', 'https://access.redhat.com/errata/RHSA-2018:1972', 'https://lists.debian.org/debian-lts-announce/2021/12/msg00025.html', 'https://access.redhat.com/errata/RHSA-2018:0591', 'https://access.redhat.com/errata/RHSA-2018:1328', 'https://access.redhat.com/errata/RHSA-2018:1124', 'https://nvd.nist.gov/vuln/detail/CVE-2018-7750', 'https://access.redhat.com/errata/RHSA-2018:1125', 'https://usn.ubuntu.com/3603-1/', 'https://github.com/paramiko/paramiko/commit/fa29bd8446c8eab237f5187d28787727b4610516', 'https://access.redhat.com/errata/RHSA-2018:0646', 'https://github.com/paramiko/paramiko/issues/1175', 'https://github.com/paramiko/paramiko/blob/master/sites/www/changelog.rst', 'https://www.exploit-db.com/exploits/45712/', 'https://access.redhat.com/errata/RHSA-2018:1213', 'https://access.redhat.com/errata/RHSA-2018:1525'} | null | {'https://github.com/paramiko/paramiko/commit/fa29bd8446c8eab237f5187d28787727b4610516'} | {'https://github.com/paramiko/paramiko/commit/fa29bd8446c8eab237f5187d28787727b4610516'} |
PyPI | GHSA-xcwj-wfcm-m23c | Invalid validation in `SparseMatrixSparseCholesky` | ### Impact
An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`:
```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([-10.0], dtype=np.float32)
dense_shape = [1, 1]
st = tf.SparseTensor(indices_array, value_array, dense_shape)
input = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
st.indices, st.values, st.dense_shape)
permutation = tf.constant([], shape=[1, 0], dtype=tf.int32)
tf.raw_ops.SparseMatrixSparseCholesky(input=input, permutation=permutation, type=tf.float32)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments:
```cc
void Compute(OpKernelContext* ctx) final {
...
const Tensor& input_permutation_indices = ctx->input(1);
...
ValidateInputs(ctx, *input_matrix, input_permutation_indices, &batch_size, &num_rows);
...
}
void ValidateInputs(OpKernelContext* ctx,
const CSRSparseMatrix& sparse_matrix,
const Tensor& permutation_indices, int* batch_size,
int64* num_rows) {
OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum<T>::value, ...)
...
}
```
Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since [`OP_REQUIRES`](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function.
```cc
#define OP_REQUIRES(CTX, EXP, STATUS) \
do { \
if (!TF_PREDICT_TRUE(EXP)) { \
CheckNotInComputeAsync((CTX), "OP_REQUIRES_ASYNC"); \
(CTX)->CtxFailure(__FILE__, __LINE__, (STATUS)); \
return; \
} \
} while (0)
```
Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`.
### Patches
We have patched the issue in GitHub commit [e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd](https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd).
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-2021-29530'} | 2022-03-03T05:14:10.768316Z | 2021-05-21T14:22:09Z | LOW | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29530', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xcwj-wfcm-m23c', 'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} | {'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} |
PyPI | PYSEC-2020-236 | null | Matrix is an ecosystem for open federated Instant Messaging and VoIP. Synapse is a reference "homeserver" implementation of Matrix. A malicious or poorly-implemented homeserver can inject malformed events into a room by specifying a different room id in the path of a `/send_join`, `/send_leave`, `/invite` or `/exchange_third_party_invite` request. This can lead to a denial of service in which future events will not be correctly sent to other servers over federation. This affects any server which accepts federation requests from untrusted servers. The Matrix Synapse reference implementation before version 1.23.1 the implementation is vulnerable to this injection attack. Issue is fixed in version 1.23.1. As a workaround homeserver administrators could limit access to the federation API to trusted servers (for example via `federation_domain_whitelist`). | {'CVE-2020-26257', 'GHSA-hxmp-pqch-c8mm'} | 2021-08-27T03:22:06.434071Z | 2020-12-09T19:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/pull/8776', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-hxmp-pqch-c8mm', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QR4MMYZKX5N5GYGH4H5LBUUC5TLAFHI7/', 'https://github.com/matrix-org/synapse/blob/develop/CHANGES.md#synapse-1231-2020-12-09', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DBTIU3ZNBFWZ56V4X7JIAD33V5H2GOMC/', 'https://github.com/matrix-org/synapse/commit/3ce2f303f15f6ac3dc352298972dc6e04d9b7a8b'} | null | {'https://github.com/matrix-org/synapse/commit/3ce2f303f15f6ac3dc352298972dc6e04d9b7a8b'} | {'https://github.com/matrix-org/synapse/commit/3ce2f303f15f6ac3dc352298972dc6e04d9b7a8b'} |
PyPI | GHSA-24x6-8c7m-hv3f | Heap OOB read in TFLite's implementation of `Minimum` or `Maximum` | ### Impact
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:
```cc
auto maxmin_func = [&](int indexes[N]) {
output_data[SubscriptToIndex(output_desc, indexes)] =
op(input1_data[SubscriptToIndex(desc1, indexes)],
input2_data[SubscriptToIndex(desc2, indexes)]);
};
```
### Patches
We have patched the issue in GitHub commit [953f28dca13c92839ba389c055587cfe6c723578](https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578).
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-2021-29590'} | 2022-03-03T05:13:08.168895Z | 2021-05-21T14:26:53Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29590', '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 | PYSEC-2021-236 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `Split` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65). An attacker can craft a model such that `num_splits` 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-2021-29599', 'GHSA-97wf-p777-86jq'} | 2021-08-27T03:22:39.020093Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97wf-p777-86jq', 'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | null | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} |
PyPI | GHSA-ph87-fvjr-v33w | CHECK-fail in `tf.raw_ops.RFFT` | ### Impact
An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`:
```python
import tensorflow as tf
inputs = tf.constant([1], shape=[1], dtype=tf.float32)
fft_length = tf.constant([0], shape=[1], dtype=tf.int32)
tf.raw_ops.RFFT(input=inputs, fft_length=fft_length)
```
The above example causes Eigen code to operate on an empty matrix. This triggers on an assertion and causes program termination.
### Patches
We have patched the issue in GitHub commit [31bd5026304677faa8a0b77602c6154171b9aec1](https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1).
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-2021-29563'} | 2022-03-03T05:12:46.533412Z | 2021-05-21T14:25:05Z | LOW | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29563', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ph87-fvjr-v33w'} | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} |
PyPI | PYSEC-2021-666 | 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'} | 2021-12-09T06:35:21.673979Z | 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 | GHSA-h67m-xg8f-fxcf | Deadlock in mutually recursive `tf.function` objects | ### Impact
The [code behind `tf.function` API](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/python/eager/def_function.py#L542) can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive:
```python
import tensorflow as tf
@tf.function()
def fun1(num):
if num == 1:
return
print(num)
fun2(num-1)
@tf.function()
def fun2(num):
if num == 0:
return
print(num)
fun1(num-1)
fun1(9)
```
This occurs due to using a non-reentrant `Lock` Python object.
Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario.
### Patches
We have patched the issue in GitHub commit [afac8158d43691661ad083f6dd9e56f327c1dcb7](https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7).
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-2021-41213'} | 2022-03-03T05:12:53.950868Z | 2021-11-10T18:59:32Z | MODERATE | null | {'CWE-667'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41213'} | null | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} |
PyPI | PYSEC-2021-148 | null | In SiCKRAGE, versions 9.3.54.dev1 to 10.0.11.dev1 are vulnerable to Reflected Cross-Site-Scripting (XSS) due to user input not being validated properly in the `quicksearch` feature. Therefore, an attacker can steal a user's sessionID to masquerade as a victim user, to carry out any actions in the context of the user. | {'GHSA-x823-j7c4-vpc5', 'CVE-2021-25926'} | 2021-08-27T03:22:21.656706Z | 2021-04-12T14:15:00Z | null | null | null | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25926,', 'https://github.com/advisories/GHSA-x823-j7c4-vpc5'} | null | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} |
PyPI | GHSA-p4v2-r99v-wjc2 | Improper Encoding or Escaping of Output in Nicotine+ | Denial of service (DoS) vulnerability in Nicotine+ starting with version 3.0.3 and prior to version 3.2.1 allows a user with a modified Soulseek client to crash Nicotine+ by sending a file download request with a file path containing a null character. | {'CVE-2021-45848'} | 2022-03-29T15:31:46.064952Z | 2022-03-16T00:00:38Z | HIGH | null | {'CWE-116'} | {'https://github.com/nicotine-plus/nicotine-plus/issues/1777', 'https://github.com/nicotine-plus/nicotine-plus/commit/0e3e2fac27a518f0a84330f1ddf1193424522045', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HWYV53KERFH2EC4XI2IVVQFTV75E5XM6/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-45848', 'https://github.com/nicotine-plus/nicotine-plus'} | null | {'https://github.com/nicotine-plus/nicotine-plus/commit/0e3e2fac27a518f0a84330f1ddf1193424522045'} | {'https://github.com/nicotine-plus/nicotine-plus/commit/0e3e2fac27a518f0a84330f1ddf1193424522045'} |
PyPI | PYSEC-2021-518 | 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'} | 2021-12-09T06:34:57.326959Z | 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 | PYSEC-2016-24 | null | redirect() in bottle.py in bottle 0.12.10 doesn't filter a "\r\n" sequence, which leads to a CRLF attack, as demonstrated by a redirect("233\r\nSet-Cookie: name=salt") call. | {'CVE-2016-9964'} | 2021-08-27T03:21:56.402931Z | 2016-12-16T09:59:00Z | null | null | null | {'https://github.com/bottlepy/bottle/issues/913', 'http://www.debian.org/security/2016/dsa-3743', 'http://www.securityfocus.com/bid/94961', 'https://github.com/bottlepy/bottle/commit/6d7e13da0f998820800ecb3fe9ccee4189aefb54'} | null | {'https://github.com/bottlepy/bottle/commit/6d7e13da0f998820800ecb3fe9ccee4189aefb54'} | {'https://github.com/bottlepy/bottle/commit/6d7e13da0f998820800ecb3fe9ccee4189aefb54'} |
PyPI | PYSEC-2020-148 | null | urllib3 before 1.25.9 allows CRLF injection if the attacker controls the HTTP request method, as demonstrated by inserting CR and LF control characters in the first argument of putrequest(). NOTE: this is similar to CVE-2020-26116. | {'CVE-2020-26137', 'GHSA-wqvq-5m8c-6g24'} | 2020-10-14T05:15:00Z | 2020-09-30T18:15:00Z | null | null | null | {'https://bugs.python.org/issue39603', 'https://github.com/urllib3/urllib3/commit/1dd69c5c5982fae7c87a620d487c2ebf7a6b436b', 'https://github.com/urllib3/urllib3/pull/1800', 'https://github.com/advisories/GHSA-wqvq-5m8c-6g24', 'https://usn.ubuntu.com/4570-1/'} | null | {'https://github.com/urllib3/urllib3/commit/1dd69c5c5982fae7c87a620d487c2ebf7a6b436b'} | {'https://github.com/urllib3/urllib3/commit/1dd69c5c5982fae7c87a620d487c2ebf7a6b436b'} |
PyPI | PYSEC-2022-81 | null | Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the `DCHECK` function 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. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range. | {'GHSA-rww7-2gpw-fv6j', 'CVE-2022-23572'} | 2022-03-09T00:17:33.923211Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L168-L174', '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-2022-141 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. 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-8cxv-76p7-jxwr', 'CVE-2022-23577'} | 2022-03-09T00:18:27.968735Z | 2022-02-04T23:15:00Z | null | null | null | {'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-2020-324 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-qh32-6jjc-qprm', 'CVE-2020-15209'} | 2021-12-09T06:35:15.002754Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qh32-6jjc-qprm', '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/0b5662bc2be13a8c8f044d925d87fb6e56247cd8'} | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8'} |
PyPI | PYSEC-2019-227 | null | In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0. | {'CVE-2019-16778', 'GHSA-844w-j86r-4x2j'} | 2021-08-27T03:22:22.453759Z | 2019-12-16T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j'} | null | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} |
PyPI | GHSA-vwhq-49r4-gj9v | Reference binding to `nullptr` in `tf.ragged.cross` | ### Impact
The [shape inference code for `tf.ragged.cross`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/ragged_array_ops.cc#L64) has an undefined behavior due to binding a reference to `nullptr`. In the following scenario, this results in a crash:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.ragged.cross([tf.ragged.constant([['1']]),'2'])
return y
test()
```
### Patches
We have patched the issue in GitHub commit [fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8](https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8).
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-2021-41214'} | 2022-03-03T05:12:46.112671Z | 2021-11-10T18:58:16Z | HIGH | null | {'CWE-824'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-41214', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v'} | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} |
PyPI | PYSEC-2021-774 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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-g25h-jr74-qp5j', 'CVE-2021-37663'} | 2021-12-09T06:35:37.816605Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j'} | null | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708'} | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708'} |
PyPI | PYSEC-2021-91 | null | The Python "Flask-Security-Too" package is used for adding security features to your Flask application. It is an is a independently maintained version of Flask-Security based on the 3.0.0 version of Flask-Security. In Flask-Security-Too from version 3.3.0 and before version 3.4.5, the /login and /change endpoints can return the authenticated user's authentication token in response to a GET request. Since GET requests aren't protected with a CSRF token, this could lead to a malicious 3rd party site acquiring the authentication token. Version 3.4.5 and version 4.0.0 are patched. As a workaround, if you aren't using authentication tokens - you can set the SECURITY_TOKEN_MAX_AGE to "0" (seconds) which should make the token unusable. | {'CVE-2021-21241'} | 2021-06-09T05:01:03.786366Z | 2021-01-11T21:15:00Z | null | null | null | {'https://github.com/Flask-Middleware/flask-security/security/advisories/GHSA-hh7m-rx4f-4vpv', 'https://github.com/Flask-Middleware/flask-security/commit/6d50ee9169acf813257c37b75babe9c28e83542a', 'https://github.com/Flask-Middleware/flask-security/releases/tag/3.4.5', 'https://github.com/Flask-Middleware/flask-security/commit/61d313150b5f620d0b800896c4f2199005e84b1f', 'https://github.com/Flask-Middleware/flask-security/pull/422', 'https://pypi.org/project/Flask-Security-Too'} | null | {'https://github.com/Flask-Middleware/flask-security/commit/61d313150b5f620d0b800896c4f2199005e84b1f', 'https://github.com/Flask-Middleware/flask-security/commit/6d50ee9169acf813257c37b75babe9c28e83542a'} | {'https://github.com/Flask-Middleware/flask-security/commit/61d313150b5f620d0b800896c4f2199005e84b1f', 'https://github.com/Flask-Middleware/flask-security/commit/6d50ee9169acf813257c37b75babe9c28e83542a'} |
PyPI | PYSEC-2021-631 | 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'} | 2021-12-09T06:35:10.661498Z | 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-grmf-4fq6-2r79 | Improper Restriction of Operations within the Bounds of a Memory Buffer in aubio | aubio v0.4.0 to v0.4.8 has a Buffer Overflow in new_aubio_tempo. | {'CVE-2018-19800'} | 2022-03-21T19:16:59.028066Z | 2019-07-26T16:10:28Z | CRITICAL | null | {'CWE-119'} | {'https://github.com/aubio/aubio', 'https://github.com/aubio/aubio/commit/b1559f4c9ce2b304d8d27ffdc7128b6795ca82e5', 'https://github.com/aubio/aubio/blob/0.4.9/ChangeLog', 'https://nvd.nist.gov/vuln/detail/CVE-2018-19800'} | null | {'https://github.com/aubio/aubio/commit/b1559f4c9ce2b304d8d27ffdc7128b6795ca82e5'} | {'https://github.com/aubio/aubio/commit/b1559f4c9ce2b304d8d27ffdc7128b6795ca82e5'} |
PyPI | PYSEC-2021-261 | null | TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-gh6x-4whr-2qv4', 'CVE-2021-37639'} | 2021-08-27T03:22:43.020795Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh6x-4whr-2qv4', 'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} | null | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} |
PyPI | PYSEC-2022-97 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. 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-fx5c-h9f6-rv7c', 'CVE-2022-23588'} | 2022-03-09T00:17:35.928489Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1328-L1402', 'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fx5c-h9f6-rv7c', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/tensor.cc#L733-L781'} | null | {'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6'} | {'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6'} |
PyPI | PYSEC-2022-157 | null | Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version. | {'GHSA-gwcx-jrx4-92w2', 'CVE-2022-23593'} | 2022-03-09T00:18:30.081576Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gwcx-jrx4-92w2', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205', 'https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a'} | null | {'https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a'} | {'https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a'} |
PyPI | PYSEC-2021-87 | null | Cranelift is an open-source code generator maintained by Bytecode Alliance. It translates a target-independent intermediate representation into executable machine code. There is a bug in 0.73 of the Cranelift x64 backend that can create a scenario that could result in a potential sandbox escape in a Wasm program. This bug was introduced in the new backend on 2020-09-08 and first included in a release on 2020-09-30, but the new backend was not the default prior to 0.73. The recently-released version 0.73 with default settings, and prior versions with an explicit build flag to select the new backend, are vulnerable. The bug in question performs a sign-extend instead of a zero-extend on a value loaded from the stack, under a specific set of circumstances. If those circumstances occur, the bug could allow access to memory addresses upto 2GiB before the start of the Wasm program heap. If the heap bound is larger than 2GiB, then it would be possible to read memory from a computable range dependent on the size of the heaps bound. The impact of this bug is highly dependent on heap implementation, specifically: * if the heap has bounds checks, and * does not rely exclusively on guard pages, and * the heap bound is 2GiB or smaller * then this bug cannot be used to reach memory from another Wasm program heap. The impact of the vulnerability is mitigated if there is no memory mapped in the range accessible using this bug, for example, if there is a 2 GiB guard region before the Wasm program heap. The bug in question performs a sign-extend instead of a zero-extend on a value loaded from the stack, when the register allocator reloads a spilled integer value narrower than 64 bits. This interacts poorly with another optimization: the instruction selector elides a 32-to-64-bit zero-extend operator when we know that an instruction producing a 32-bit value actually zeros the upper 32 bits of its destination register. Hence, we rely on these zeroed bits, but the type of the value is still i32, and the spill/reload reconstitutes those bits as the sign extension of the i32’s MSB. The issue would thus occur when: * An i32 value in a Wasm program is greater than or equal to 0x8000_0000; * The value is spilled and reloaded by the register allocator due to high register pressure in the program between the value’s definition and its use; * The value is produced by an instruction that we know to be “special” in that it zeroes the upper 32 bits of its destination: add, sub, mul, and, or; * The value is then zero-extended to 64 bits in the Wasm program; * The resulting 64-bit value is used. Under these circumstances there is a potential sandbox escape when the i32 value is a pointer. The usual code emitted for heap accesses zero-extends the Wasm heap address, adds it to a 64-bit heap base, and accesses the resulting address. If the zero-extend becomes a sign-extend, the program could reach backward and access memory up to 2GiB before the start of its heap. In addition to assessing the nature of the code generation bug in Cranelift, we have also determined that under specific circumstances, both Lucet and Wasmtime using this version of Cranelift may be exploitable. See referenced GitHub Advisory for more details. | {'GHSA-hpqh-2wqx-7qp5', 'CVE-2021-32629'} | 2021-06-02T03:48:07.159295Z | 2021-05-24T16:15:00Z | null | null | null | {'https://github.com/bytecodealliance/wasmtime/commit/95559c01aaa7c061088a433040f31e8291fb09d0', 'https://crates.io/crates/cranelift-codegen', 'https://www.fastly.com/security-advisories/memory-access-due-to-code-generation-flaw-in-cranelift-module', 'https://github.com/bytecodealliance/wasmtime/security/advisories/GHSA-hpqh-2wqx-7qp5'} | null | {'https://github.com/bytecodealliance/wasmtime/commit/95559c01aaa7c061088a433040f31e8291fb09d0'} | {'https://github.com/bytecodealliance/wasmtime/commit/95559c01aaa7c061088a433040f31e8291fb09d0'} |
PyPI | PYSEC-2021-762 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-hpv4-7p9c-mvfr', 'CVE-2021-37651'} | 2021-12-09T06:35:36.737111Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr'} | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} |
PyPI | GHSA-rgcm-rpq9-9cgr | Missing Authentication for Critical Function in Saleor | An issue was discovered in Mirumee Saleor 2.x before 2.9.1. Incorrect access control in the checkoutCustomerAttach mutations allows attackers to attach their checkouts to any user ID and consequently leak user data (e.g., name, address, and previous orders of any other customer). | {'CVE-2020-7964'} | 2021-07-27T15:13:34Z | 2021-07-28T17:57:09Z | MODERATE | null | {'CWE-306'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-7964', 'https://github.com/mirumee/saleor/releases/tag/2.9.1', 'https://github.com/mirumee/saleor/commit/233b8890c60fa6d90daf99e4d90fea85867732c3'} | null | {'https://github.com/mirumee/saleor/commit/233b8890c60fa6d90daf99e4d90fea85867732c3'} | {'https://github.com/mirumee/saleor/commit/233b8890c60fa6d90daf99e4d90fea85867732c3'} |
PyPI | PYSEC-2021-298 | 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.SparseFillEmptyRows`. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. We have patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed. 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-2021-37676', 'GHSA-v768-w7m9-2vmm'} | 2021-08-27T03:22:46.384345Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v768-w7m9-2vmm', 'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} | null | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} |
PyPI | GHSA-2cpx-427x-q2c6 | CHECK-fail in AddManySparseToTensorsMap | ### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.AddManySparseToTensorsMap`:
```python
import tensorflow as tf
import numpy as np
sparse_indices = tf.constant(530, shape=[1, 1], dtype=tf.int64)
sparse_values = tf.ones([1], dtype=tf.int64)
shape = tf.Variable(tf.ones([55], dtype=tf.int64))
shape[:8].assign(np.array([855, 901, 429, 892, 892, 852, 93, 96], dtype=np.int64))
tf.raw_ops.AddManySparseToTensorsMap(sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=shape)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` as dimensions for the output shape:
```cc
TensorShape tensor_input_shape(input_shape->vec<int64>());
```
The [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status.
```cc
template <class Shape>
TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {
set_tag(REP16);
set_data_type(DT_INVALID);
TF_CHECK_OK(InitDims(dim_sizes));
}
```
In our scenario, this occurs when adding a dimension from the argument results in overflow:
```cc
template <class Shape>
Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {
...
Status status = Status::OK();
for (int64 s : dim_sizes) {
status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));
if (!status.ok()) {
return status;
}
}
}
template <class Shape>
Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {
...
int64 new_num_elements;
if (kIsPartial && (num_elements() < 0 || size < 0)) {
new_num_elements = -1;
} else {
new_num_elements = MultiplyWithoutOverflow(num_elements(), size);
if (TF_PREDICT_FALSE(new_num_elements < 0)) {
return errors::Internal("Encountered overflow when multiplying ",
num_elements(), " with ", size,
", result: ", new_num_elements);
}
}
...
}
```
This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows.
### Patches
We have patched the issue in GitHub commit [69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c](https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c).
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-2021-29523'} | 2022-03-03T05:13:59.631408Z | 2021-05-21T14:21:43Z | LOW | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29523', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2cpx-427x-q2c6'} | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} |
PyPI | PYSEC-2020-332 | 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'} | 2021-12-09T06:35:15.994631Z | 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 | PYSEC-2020-298 | null | In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26267', 'GHSA-c9f3-9wfr-wgh7'} | 2021-12-09T06:34:44.408160Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7', 'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} | null | {'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} | {'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} |
PyPI | PYSEC-2020-277 | null | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15197', 'GHSA-qc53-44cj-vfvx'} | 2021-12-09T06:34:41.476873Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx', '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'} |
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