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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | GHSA-gf2j-f278-xh4v | Division by zero in TFLite | ### Impact
An attacker can craft a TFLite model that would trigger a division by zero in [`BiasAndClamp` implementation](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/internal/common.h#L75):
```cc
inline void BiasAndClamp(float clamp_min, float clamp_max, int bias_size,
const float* bias_data, int array_size,
float* array_data) {
// ...
TFLITE_DCHECK_EQ((array_size % bias_size), 0);
// ...
}
```
There is no check that the `bias_size` is non zero.
### Patches
We have patched the issue in GitHub commit [8c6f391a2282684a25cbfec7687bd5d35261a209](https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209).
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-2022-23557'} | 2022-03-03T05:13:58.557534Z | 2022-02-09T23:47:57Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/internal/common.h#L75', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23557', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v'} | null | {'https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209'} | {'https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209'} |
PyPI | GHSA-m4fw-77v7-924m | Moderate severity vulnerability that affects qutebrowser | qutebrowser version introduced in v0.11.0 (1179ee7a937fb31414d77d9970bac21095358449) contains a Cross Site Scripting (XSS) vulnerability in history command, qute://history page that can result in Via injected JavaScript code, a website can steal the user's browsing history. This attack appear to be exploitable via the victim must open a page with a specially crafted <title> attribute, and then open the qute://history site via the :history command. This vulnerability appears to have been fixed in fixed in v1.3.3 (4c9360237f186681b1e3f2a0f30c45161cf405c7, to be released today) and v1.4.0 (5a7869f2feaa346853d2a85413d6527c87ef0d9f, released later this week). | {'CVE-2018-1000559'} | 2022-03-03T05:14:14.678547Z | 2018-09-13T15:47:57Z | MODERATE | null | {'CWE-79'} | {'https://github.com/advisories/GHSA-m4fw-77v7-924m', 'https://github.com/qutebrowser/qutebrowser/commit/5a7869f2feaa346853d2a85413d6527c87ef0d9f', 'https://github.com/qutebrowser/qutebrowser/commit/4c9360237f186681b1e3f2a0f30c45161cf405c7', 'https://github.com/qutebrowser/qutebrowser', 'https://nvd.nist.gov/vuln/detail/CVE-2018-1000559', 'https://github.com/qutebrowser/qutebrowser/issues/4011'} | null | {'https://github.com/qutebrowser/qutebrowser/commit/4c9360237f186681b1e3f2a0f30c45161cf405c7', 'https://github.com/qutebrowser/qutebrowser/commit/5a7869f2feaa346853d2a85413d6527c87ef0d9f'} | {'https://github.com/qutebrowser/qutebrowser/commit/5a7869f2feaa346853d2a85413d6527c87ef0d9f', 'https://github.com/qutebrowser/qutebrowser/commit/4c9360237f186681b1e3f2a0f30c45161cf405c7'} |
PyPI | PYSEC-2021-625 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41216', 'GHSA-3ff2-r28g-w7h9'} | 2021-12-09T06:35:09.827396Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9'} | null | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} |
PyPI | PYSEC-2020-275 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-63xm-rx5p-xvqr', 'CVE-2020-15195'} | 2021-12-09T06:34:41.380854Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} |
PyPI | PYSEC-2020-330 | null | In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | {'GHSA-rrfp-j2mp-hq9c', 'CVE-2020-15265'} | 2021-12-09T06:35:15.737663Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/42105', 'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c'} | null | {'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808'} | {'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808'} |
PyPI | PYSEC-2021-760 | null | TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37649', 'GHSA-6gv8-p3vj-pxvr'} | 2021-12-09T06:35:36.563048Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gv8-p3vj-pxvr'} | null | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} |
PyPI | PYSEC-2021-449 | null | TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. | {'CVE-2021-29521', 'GHSA-hr84-fqvp-48mm'} | 2021-12-09T06:34:46.609278Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm'} | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} |
PyPI | PYSEC-2022-155 | null | Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`. This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23591', 'GHSA-247x-2f9f-5wp7'} | 2022-03-09T00:18:29.944139Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7'} | null | {'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c'} | {'https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c'} |
PyPI | GHSA-m648-33qf-v3gp | CHECK-fail in LSTM with zero-length input in TensorFlow | ### Impact
Running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a `CHECK` failure when using the CUDA backend.
This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.
### Patches
We have patched the issue in GitHub commit [14755416e364f17fb1870882fa778c7fec7f16e3](https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2020-26270'} | 2022-03-03T05:12:46.001558Z | 2020-12-10T19:07:31Z | LOW | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m648-33qf-v3gp', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26270', 'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} | null | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} |
PyPI | GHSA-gh6x-4whr-2qv4 | Null pointer dereference and heap OOB read in operations restoring tensors | ### Impact
When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer:
```python
import tensorflow as tf
tf.raw_ops.Restore(
file_pattern=['/tmp'],
tensor_name=[],
default_value=21,
dt=tf.int,
preferred_shard=1)
```
The same undefined behavior can be triggered by `tf.raw_ops.RestoreSlice`:
```python
import tensorflow as tf
tf.raw_ops.RestoreSlice(
file_pattern=['/tmp'],
tensor_name=[],
shape_and_slice='2',
dt=inp.array([tf.int]),
preferred_shard=1)
```
Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration:
```python
import tensorflow as tf
tf.raw_ops.Restore(
file_pattern=['/tmp'],
tensor_name=['x'],
default_value=21,
dt=tf.int,
preferred_shard=42)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values.
If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read.
### Patches
We have patched the issue in GitHub commit [9e82dce6e6bd1f36a57e08fa85af213e2b2f2622](https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37639'} | 2022-04-26T18:17:07.858464Z | 2021-08-25T14:44:05Z | HIGH | null | {'CWE-476', 'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37639', '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-2022-95 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23586', 'GHSA-43jf-985q-588j'} | 2022-03-09T00:17:35.674710Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/function.cc', 'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43jf-985q-588j'} | null | {'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645', 'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2'} | {'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645', 'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2'} |
PyPI | GHSA-4vf2-4xcg-65cx | Division by 0 in `Conv2D` | ### Impact
An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`:
```python
import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller:
```cc
const int64 patch_depth = filter.dim_size(2);
if (in_depth % patch_depth != 0) { ... }
```
### Patches
We have patched the issue in GitHub commit [b12aa1d44352de21d1a6faaf04172d8c2508b42b](https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29526'} | 2022-03-03T05:14:16.972688Z | 2021-05-21T14:21:55Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29526', 'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vf2-4xcg-65cx'} | null | {'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b'} | {'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b'} |
PyPI | PYSEC-2021-633 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-rg3m-hqc5-344v', 'CVE-2021-41224'} | 2021-12-09T06:35:10.967537Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v', 'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | null | {'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | {'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} |
PyPI | PYSEC-2021-263 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37641', 'GHSA-9c8h-vvrj-w2p8'} | 2021-08-27T03:22:43.190554Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8', 'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} | null | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} |
PyPI | PYSEC-2021-799 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-vcjj-9vg7-vf68', 'CVE-2021-37688'} | 2021-12-09T06:35:40.029733Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vcjj-9vg7-vf68', 'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} | null | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} |
PyPI | PYSEC-2021-776 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37665', 'GHSA-v82p-hv3v-p6qp'} | 2021-12-09T06:35:37.987590Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9'} | null | {'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9'} | {'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9', 'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69'} |
PyPI | PYSEC-2020-326 | null | In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code. | {'GHSA-cvpc-8phh-8f45', 'CVE-2020-15211'} | 2021-12-09T06:35:15.416974Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859'} | {'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9'} |
PyPI | PYSEC-2022-143 | null | Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `SafeToRemoveIdentity` would trigger `CHECK` failures. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-5f2r-qp73-37mr', 'CVE-2022-23579'} | 2022-03-09T00:18:28.283580Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/dependency_optimizer.cc#L59-L98', 'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662'} | null | {'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662'} | {'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662'} |
PyPI | GHSA-gcv9-6737-pjqw | SSRF vulnerability in jupyter-server-proxy | ### Impact
**What kind of vulnerability is it?** Server-Side Request Forgery ( SSRF )
**Who is impacted?** Any user deploying Jupyter Server or Notebook with jupyter-proxy-server extension enabled.
A lack of input validation allowed authenticated clients to proxy requests to other hosts, bypassing the `allowed_hosts` check. Because authentication is required, which already grants permissions to make the same requests via kernel or terminal execution, this is considered low to moderate severity.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
Upgrade to 3.2.1, or apply the patch https://github.com/jupyterhub/jupyter-server-proxy/compare/v3.2.0...v3.2.1.patch
### For more information
If you have any questions or comments about this advisory:
* Open a topic [on our forum](https://discourse.jupyter.org)
* Email the Jupyter security team at [security@ipython.org](mailto:security@ipython.org)
| {'CVE-2022-21697'} | 2022-03-03T05:12:40.317434Z | 2022-01-27T16:24:26Z | MODERATE | null | {'CWE-918'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21697', 'https://github.com/jupyterhub/jupyter-server-proxy/compare/v3.2.0...v3.2.1.patch', 'https://github.com/jupyterhub/jupyter-server-proxy/security/advisories/GHSA-gcv9-6737-pjqw', 'https://github.com/jupyterhub/jupyter-server-proxy/', 'https://github.com/jupyterhub/jupyter-server-proxy/commit/fd31930bacd12188c448c886e0783529436b99eb'} | null | {'https://github.com/jupyterhub/jupyter-server-proxy/commit/fd31930bacd12188c448c886e0783529436b99eb'} | {'https://github.com/jupyterhub/jupyter-server-proxy/commit/fd31930bacd12188c448c886e0783529436b99eb'} |
PyPI | GHSA-gh8j-2pgf-x458 | Infinite Loop in rencode | The rencode package through 1.0.6 for Python allows an infinite loop in typecode decoding (such as via ;\x2f\x7f), enabling a remote attack that consumes CPU and memory. | {'CVE-2021-40839'} | 2022-03-03T05:13:50.834851Z | 2021-09-13T20:05:51Z | HIGH | null | {'CWE-835'} | {'https://github.com/aresch/rencode', 'https://nvd.nist.gov/vuln/detail/CVE-2021-40839', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MCLETLGVM5DBX6QNHQFW6TWGO5T3DENY/', 'https://seclists.org/fulldisclosure/2021/Sep/16', 'https://github.com/aresch/rencode/pull/29', 'https://pypi.org/project/rencode/#history', 'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75', 'https://security.netapp.com/advisory/ntap-20211008-0001/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BMVQRPDVSVZNGGX57CFKCYT3DEYO4QB6/'} | null | {'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75'} | {'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75'} |
PyPI | PYSEC-2020-51 | null | In jupyterhub-kubespawner before 0.12, certain usernames will be able to craft particular server names which will grant them access to the default server of other users who have matching usernames. This has been fixed in 0.12. | {'GHSA-v7m9-9497-p9gr', 'CVE-2020-15110'} | 2020-07-22T20:28:00Z | 2020-07-17T21:15:00Z | null | null | null | {'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0', 'https://github.com/jupyterhub/kubespawner/security/advisories/GHSA-v7m9-9497-p9gr'} | null | {'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0'} | {'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0'} |
PyPI | PYSEC-2022-83 | null | Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range. | {'GHSA-77gp-3h4r-6428', 'CVE-2022-23574'} | 2022-03-09T00:17:34.161202Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L81-L102', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-77gp-3h4r-6428', 'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} | null | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} |
PyPI | GHSA-h56g-v4vp-q9q6 | Cross-site Scripting in calibreweb | calibreweb prior to version 0.6.16 contains a cross-site scripting vulnerability. | {'CVE-2022-0352'} | 2022-03-03T05:12:56.408260Z | 2022-01-29T00:00:41Z | MODERATE | null | {'CWE-79'} | {'https://huntr.dev/bounties/a577ff17-2ded-4c41-84ae-6ac02440f717', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0352', 'https://github.com/janeczku/calibre-web', 'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1'} | null | {'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1'} | {'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1'} |
PyPI | PYSEC-2021-234 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SpaceToBatchNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/412c7d9bb8f8a762c5b266c9e73bfa165f29aac8/tensorflow/lite/kernels/space_to_batch_nd.cc#L82-L83). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-v52p-hfjf-wg88', 'CVE-2021-29597'} | 2021-08-27T03:22:38.644851Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/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 | PYSEC-2021-664 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-j8qc-5fqr-52fp', 'CVE-2021-29538'} | 2021-12-09T06:35:21.353144Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp', 'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} |
PyPI | PYSEC-2020-234 | null | Jupyter Server before version 1.0.6 has an Open redirect vulnerability. A maliciously crafted link to a jupyter server could redirect the browser to a different website. All jupyter servers are technically affected, however, these maliciously crafted links can only be reasonably made for known jupyter server hosts. A link to your jupyter server may appear safe, but ultimately redirect to a spoofed server on the public internet. | {'GHSA-grfj-wjv9-4f9v', 'CVE-2020-26232'} | 2021-08-27T03:22:05.136094Z | 2020-11-24T21:15:00Z | null | null | null | {'https://github.com/jupyter-server/jupyter_server/blob/master/CHANGELOG.md#106---2020-11-18', 'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157', 'https://github.com/jupyter/jupyter_server/security/advisories/GHSA-grfj-wjv9-4f9v'} | null | {'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157'} | {'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157'} |
PyPI | GHSA-jc87-6vpp-7ff3 | Heap buffer overflow in Tensorflow | ### Impact
The `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L193-L195
Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers.
### Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4) | {'CVE-2020-15198'} | 2021-08-26T15:11:45Z | 2020-09-25T18:28:22Z | MODERATE | null | {'CWE-119', 'CWE-122'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15198', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-721 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29595', 'GHSA-vf94-36g5-69v8'} | 2021-12-09T06:35:31.250576Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vf94-36g5-69v8', 'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} | null | {'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} | {'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} |
PyPI | GHSA-cq76-mxrc-vchh | Crash in `tf.math.segment_*` operations | ### Impact
The implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large.
```python
import tensorflow as tf
tf.math.segment_max(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_min(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_mean(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_sum(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
tf.math.segment_prod(data=np.ones((1,10,1)), segment_ids=[1676240524292489355])
```
This is similar to [CVE-2021-29584](https://github.com/tensorflow/tensorflow/blob/3a74f0307236fe206b046689c4d76f57c9b74eee/tensorflow/security/advisory/tfsa-2021-071.md) (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the [implementation](https://github.com/tensorflow/tensorflow/blob/dae66e518c88de9c11718cd0f8f40a0b666a90a0/tensorflow/core/kernels/segment_reduction_ops_impl.h) (both on CPU and GPU) computes the output shape using [`AddDim`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/framework/tensor_shape.cc#L395-L408). However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use [`AddDimWithStatus`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/framework/tensor_shape.cc#L410-L440).
### Patches
We have patched the issue in GitHub commit [e9c81c1e1a9cd8dd31f4e83676cab61b60658429](https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429) (merging [#51733](https://github.com/tensorflow/tensorflow/pull/51733)).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46888). | {'CVE-2021-41195'} | 2022-03-03T05:13:05.028321Z | 2021-11-10T19:36:50Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/issues/46888', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh', 'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41195'} | null | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} |
PyPI | PYSEC-2021-408 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41216', 'GHSA-3ff2-r28g-w7h9'} | 2021-11-13T06:52:44.644675Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9'} | null | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} |
PyPI | PYSEC-2022-114 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalMaxPool` can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-21735', 'GHSA-87v6-crgm-2gfj'} | 2022-03-09T00:18:24.359111Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192'} | null | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb'} | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb'} |
PyPI | GHSA-w4xf-2pqw-5mq7 | Reference binding to nullptr in `RaggedTensorToVariant` | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToVariant`:
```python
import tensorflow as tf
tf.raw_ops.RaggedTensorToVariant(
rt_nested_splits=[],
rt_dense_values=[1,2,3],
batched_input=True)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty.
### Patches
We have patched the issue in GitHub commit [be7a4de6adfbd303ce08be4332554dff70362612](https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37666'} | 2022-03-03T05:13:39.107184Z | 2021-08-25T14:42:13Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37666', 'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w4xf-2pqw-5mq7'} | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} |
PyPI | GHSA-f8m6-h2c7-8h9x | Inefficient Regular Expression Complexity in nltk (word_tokenize, sent_tokenize) | ### Impact
The vulnerability is present in [`PunktSentenceTokenizer`](https://www.nltk.org/api/nltk.tokenize.punkt.html#nltk.tokenize.punkt.PunktSentenceTokenizer), [`sent_tokenize`](https://www.nltk.org/api/nltk.tokenize.html#nltk.tokenize.sent_tokenize) and [`word_tokenize`](https://www.nltk.org/api/nltk.tokenize.html#nltk.tokenize.word_tokenize). Any users of this class, or these two functions, are vulnerable to a Regular Expression Denial of Service (ReDoS) attack.
In short, a specifically crafted long input to any of these vulnerable functions will cause them to take a significant amount of execution time. The effect of this vulnerability is noticeable with the following example:
```python
from nltk.tokenize import word_tokenize
n = 8
for length in [10**i for i in range(2, n)]:
# Prepare a malicious input
text = "a" * length
start_t = time.time()
# Call `word_tokenize` and naively measure the execution time
word_tokenize(text)
print(f"A length of {length:<{n}} takes {time.time() - start_t:.4f}s")
```
Which gave the following output during testing:
```python
A length of 100 takes 0.0060s
A length of 1000 takes 0.0060s
A length of 10000 takes 0.6320s
A length of 100000 takes 56.3322s
...
```
I canceled the execution of the program after running it for several hours.
If your program relies on any of the vulnerable functions for tokenizing unpredictable user input, then we would strongly recommend upgrading to a version of NLTK without the vulnerability, or applying the workaround described below.
### Patches
The problem has been patched in NLTK 3.6.6. After the fix, running the above program gives the following result:
```python
A length of 100 takes 0.0070s
A length of 1000 takes 0.0010s
A length of 10000 takes 0.0060s
A length of 100000 takes 0.0400s
A length of 1000000 takes 0.3520s
A length of 10000000 takes 3.4641s
```
This output shows a linear relationship in execution time versus input length, which is desirable for regular expressions.
We recommend updating to NLTK 3.6.6+ if possible.
### Workarounds
The execution time of the vulnerable functions is exponential to the length of a malicious input. With other words, the execution time can be bounded by limiting the maximum length of an input to any of the vulnerable functions. Our recommendation is to implement such a limit.
### References
* The issue showcasing the vulnerability: https://github.com/nltk/nltk/issues/2866
* The pull request containing considerably more information on the vulnerability, and the fix: https://github.com/nltk/nltk/pull/2869
* The commit containing the fix: 1405aad979c6b8080dbbc8e0858f89b2e3690341
* Information on CWE-1333: Inefficient Regular Expression Complexity: https://cwe.mitre.org/data/definitions/1333.html
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [github.com/nltk/nltk](https://github.com/nltk/nltk)
* Email us at [nltk.team@gmail.com](mailto:nltk.team@gmail.com)
| {'CVE-2021-43854'} | 2022-03-03T05:13:02.317038Z | 2022-01-06T17:38:45Z | HIGH | null | {'CWE-400'} | {'https://github.com/nltk/nltk/commit/1405aad979c6b8080dbbc8e0858f89b2e3690341', 'https://github.com/nltk/nltk/issues/2866', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43854', 'https://github.com/nltk/nltk', 'https://github.com/nltk/nltk/pull/2869', 'https://github.com/nltk/nltk/security/advisories/GHSA-f8m6-h2c7-8h9x'} | null | {'https://github.com/nltk/nltk/commit/1405aad979c6b8080dbbc8e0858f89b2e3690341'} | {'https://github.com/nltk/nltk/commit/1405aad979c6b8080dbbc8e0858f89b2e3690341'} |
PyPI | GHSA-jf7h-7m85-w2v2 | Integer overflow in TFLite memory allocation | ### Impact
The TFLite code for allocating `TFLiteIntArray`s is [vulnerable to an integer overflow issue](https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27):
```cc
int TfLiteIntArrayGetSizeInBytes(int size) {
static TfLiteIntArray dummy;
return sizeof(dummy) + sizeof(dummy.data[0]) * size;
}
```
An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in [invalid value being given to `malloc`](https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52):
```cc
TfLiteIntArray* TfLiteIntArrayCreate(int size) {
TfLiteIntArray* ret = (TfLiteIntArray*)malloc(TfLiteIntArrayGetSizeInBytes(size));
ret->size = size;
return ret;
}
```
In this case, `ret->size` would dereference an invalid pointer.
### Patches
We have patched the issue in GitHub commit [7c8cc4ec69cd348e44ad6a2699057ca88faad3e5](https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29605'} | 2022-03-03T05:13:02.259909Z | 2021-05-21T14:28:22Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jf7h-7m85-w2v2', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29605'} | null | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} |
PyPI | PYSEC-2019-209 | null | In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0. | {'CVE-2019-16778', 'GHSA-844w-j86r-4x2j'} | 2021-08-27T03:22:22.453759Z | 2019-12-16T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/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-9gwq-6cwj-47h3 | Integer overflow in TFLite array creation | ### Impact
An attacker can craft a TFLite model that would cause an integer overflow [in `TfLiteIntArrayCreate`](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60):
```cc
TfLiteIntArray* TfLiteIntArrayCreate(int size) {
int alloc_size = TfLiteIntArrayGetSizeInBytes(size);
// ...
TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size);
// ...
}
```
The [`TfLiteIntArrayGetSizeInBytes`](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33) returns an `int` instead of a `size_t`:
```cc
int TfLiteIntArrayGetSizeInBytes(int size) {
static TfLiteIntArray dummy;
int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;
#if defined(_MSC_VER)
// Context for why this is needed is in http://b/189926408#comment21
computed_size -= sizeof(dummy.data[0]);
#endif
return computed_size;
}
```
An attacker can control model inputs such that `computed_size` overflows the size of `int` datatype.
### Patches
We have patched the issue in GitHub commit [a1e1511dde36b3f8aa27a6ec630838e7ea40e091](https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team. | {'CVE-2022-23558'} | 2022-03-03T05:12:41.971477Z | 2022-02-09T23:52:24Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23558', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33'} | null | {'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091'} | {'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091'} |
PyPI | GHSA-pq7m-3gw7-gq5x | Execution with Unnecessary Privileges in ipython | We’d like to disclose an arbitrary code execution vulnerability in IPython that stems from IPython executing untrusted files in CWD. This vulnerability allows one user to run code as another.
Proof of concept
User1:
```
mkdir -m 777 /tmp/profile_default
mkdir -m 777 /tmp/profile_default/startup
echo 'print("stealing your private secrets")' > /tmp/profile_default/startup/foo.py
```
User2:
```
cd /tmp
ipython
```
User2 will see:
```
Python 3.9.7 (default, Oct 25 2021, 01:04:21)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.29.0 -- An enhanced Interactive Python. Type '?' for help.
stealing your private secrets
```
## Patched release and documentation
See https://ipython.readthedocs.io/en/stable/whatsnew/version8.html#ipython-8-0-1-cve-2022-21699,
Version 8.0.1, 7.31.1 for current Python version are recommended.
Version 7.16.3 has also been published for Python 3.6 users,
Version 5.11 (source only, 5.x branch on github) for older Python versions. | {'CVE-2022-21699'} | 2022-03-29T22:16:59.073233Z | 2022-01-21T18:55:30Z | HIGH | null | {'CWE-279', 'CWE-250', 'CWE-269'} | {'https://github.com/ipython/ipython', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21699', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/CRQRTWHYXMLDJ572VGVUZMUPEOTPM3KB/', 'https://lists.debian.org/debian-lts-announce/2022/01/msg00021.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DZ7LVZBB4D7KVSFNEQUBEHFO3JW6D2ZK/', 'https://github.com/ipython/ipython/security/advisories/GHSA-pq7m-3gw7-gq5x', 'https://ipython.readthedocs.io/en/stable/whatsnew/version8.html#ipython-8-0-1-cve-2022-21699', 'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668'} | null | {'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668'} | {'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668'} |
PyPI | GHSA-v3f7-j968-4h5f | Division by zero in Tensorflow | ### Impact
The [estimator for the cost of some convolution operations](https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198) can be made to execute a division by 0:
```python
import tensorflow as tf
@tf.function
def test():
y=tf.raw_ops.AvgPoolGrad(
orig_input_shape=[1,1,1,1],
grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
ksize=[1,1,1,1],
strides=[1,1,1,0],
padding='VALID',
data_format='NCHW')
return y
test()
```
The function fails to check that the stride argument is stricly positive:
```cc
int64_t GetOutputSize(const int64_t input, const int64_t filter,
const int64_t stride, const Padding& padding) {
// Logic for calculating output shape is from GetWindowedOutputSizeVerbose()
// function in third_party/tensorflow/core/framework/common_shape_fns.cc.
if (padding == Padding::VALID) {
return (input - filter + stride) / stride;
} else { // SAME.
return (input + stride - 1) / stride;
}
}
```
Hence, the fix is to add a check for the stride argument to ensure it is valid.
### Patches
We have patched the issue in GitHub commit [3218043d6d3a019756607643cf65574fbfef5d7a](https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21725'} | 2022-03-03T05:12:59.852211Z | 2022-02-10T00:15:07Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21725', 'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} | null | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} |
PyPI | GHSA-wgmx-52ph-qqcw | High severity vulnerability that affects qutebrowser | qutebrowser before version 1.4.1 is vulnerable to a cross-site request forgery flaw that allows websites to access 'qute://*' URLs. A malicious website could exploit this to load a 'qute://settings/set' URL, which then sets 'editor.command' to a bash script, resulting in arbitrary code execution. | {'CVE-2018-10895'} | 2022-03-03T05:14:17.497611Z | 2018-10-10T16:05:23Z | HIGH | null | {'CWE-352'} | {'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660', 'https://nvd.nist.gov/vuln/detail/CVE-2018-10895', 'https://github.com/advisories/GHSA-wgmx-52ph-qqcw', 'https://github.com/qutebrowser/qutebrowser'} | null | {'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660'} | {'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660'} |
PyPI | GHSA-87cj-px37-rc3x | OS Command Injection in bikeshed | This affects the package bikeshed before 3.0.0. This can occur when an untrusted source file containing Inline Tag Command metadata is processed. When an arbitrary OS command is executed, the command output would be included in the HTML output. | {'CVE-2021-23422'} | 2022-03-03T05:13:48.495615Z | 2021-08-30T16:25:35Z | HIGH | null | {'CWE-78'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-23422', 'https://github.com/tabatkins/bikeshed', 'https://snyk.io/vuln/SNYK-PYTHON-BIKESHED-1537646', 'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | null | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} |
PyPI | PYSEC-2021-536 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-rgvq-pcvf-hx75', 'CVE-2021-29608'} | 2021-12-09T06:35:00.179664Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rgvq-pcvf-hx75', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} | null | {'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} | {'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} |
PyPI | GHSA-f54p-f6jp-4rhr | Heap OOB in `FusedBatchNorm` kernels | ### Impact
The [implementation](https://github.com/tensorflow/tensorflow/blob/e71b86d47f8bc1816bf54d7bddc4170e47670b97/tensorflow/core/kernels/fused_batch_norm_op.cc#L1292) of `FusedBatchNorm` kernels is vulnerable to a heap OOB:
```python
import tensorflow as tf
tf.raw_ops.FusedBatchNormGrad(
y_backprop=tf.constant([i for i in range(9)],shape=(1,1,3,3),dtype=tf.float32)
x=tf.constant([i for i in range(2)],shape=(1,1,1,2),dtype=tf.float32)
scale=[1,1],
reserve_space_1=[1,1],
reserve_space_2=[1,1,1],
epsilon=1.0,
data_format='NCHW',
is_training=True)
```
### Patches
We have patched the issue in GitHub commit [aab9998916c2ffbd8f0592059fad352622f89cda](https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-41223'} | 2022-03-03T05:13:06.432363Z | 2021-11-10T18:46:52Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41223', 'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr'} | null | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda'} | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda'} |
PyPI | GHSA-23hm-7w47-xw72 | Out of bounds read in Tensorflow | ### Impact
The [implementation of `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153) does not fully validate the value of `axis` and can result in heap OOB accesses:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.Dequantize(
input=tf.constant([1,1],dtype=tf.qint32),
min_range=[1.0],
max_range=[10.0],
mode='MIN_COMBINED',
narrow_range=False,
axis=2**31-1,
dtype=tf.bfloat16)
return y
test()
```
The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor:
```cc
if (axis_ > -1) {
num_slices = input.dim_size(axis_);
}
// ...
int64_t pre_dim = 1, post_dim = 1;
for (int i = 0; i < axis_; ++i) {
pre_dim *= float_output.dim_size(i);
}
for (int i = axis_ + 1; i < float_output.dims(); ++i) {
post_dim *= float_output.dim_size(i);
}
```
### Patches
We have patched the issue in GitHub commit [23968a8bf65b009120c43b5ebcceaf52dbc9e943](https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21726'} | 2022-03-03T05:13:03.797193Z | 2022-02-09T18:28:54Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21726', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72', 'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} | null | {'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} | {'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} |
PyPI | PYSEC-2021-166 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-jfp7-4j67-8r3q', 'CVE-2021-29529'} | 2021-08-27T03:22:26.519373Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q'} | null | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7'} | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7'} |
PyPI | PYSEC-2020-92 | null | A denial of service via regular expression in the py.path.svnwc component of py (aka python-py) through 1.9.0 could be used by attackers to cause a compute-time denial of service attack by supplying malicious input to the blame functionality. | {'CVE-2020-29651', 'GHSA-hj5v-574p-mj7c'} | 2021-01-05T03:15:00Z | 2020-12-09T07:15:00Z | null | null | null | {'https://github.com/pytest-dev/py/issues/256', 'https://github.com/pytest-dev/py/pull/257', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/AYWNYEV3FGDHPIHX4DDUDMFZ6NLCQRC4/', 'https://github.com/advisories/GHSA-hj5v-574p-mj7c', 'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/CHDTINIBJZ67T3W74QTBIY5LPKAXEOGR/'} | null | {'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144'} | {'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144'} |
PyPI | PYSEC-2021-865 | null | In Mozilla Bleach before 3.3.0, a mutation XSS affects users calling bleach.clean with math or svg; p or br; and style, title, noscript, script, textarea, noframes, iframe, or xmp tags with strip_comments=False. | {'GHSA-vv2x-vrpj-qqpq', 'CVE-2021-23980'} | 2022-01-05T02:16:13.001009Z | 2021-02-02T17:58:00Z | null | null | null | {'https://github.com/mozilla/bleach/commit/79b7a3c5e56a09d1d323a5006afa59b56162eb13', 'https://advisory.checkmarx.net/advisory/CX-2021-4303', 'ttps://bugzilla.mozilla.org/show_bug.cgi?id=1689399', 'https://github.com/mozilla/bleach/security/advisories/GHSA-vv2x-vrpj-qqpq'} | null | {'https://github.com/mozilla/bleach/commit/79b7a3c5e56a09d1d323a5006afa59b56162eb13'} | {'https://github.com/mozilla/bleach/commit/79b7a3c5e56a09d1d323a5006afa59b56162eb13'} |
PyPI | GHSA-q85f-69q7-55h2 | Uninitialized variable access in Tensorflow | ### Impact
The [implementation of `AssignOp`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143) can result in copying unitialized data to a new tensor. This later results in undefined behavior.
The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized.
### Patches
We have patched the issue in GitHub commit [ef1d027be116f25e25bb94a60da491c2cf55bd0b](https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
| {'CVE-2022-23573'} | 2022-03-03T05:12:43.058761Z | 2022-02-09T23:26:50Z | HIGH | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143', 'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23573'} | null | {'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} | {'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} |
PyPI | PYSEC-2021-473 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29545', 'GHSA-hmg3-c7xj-6qwm'} | 2021-12-09T06:34:50.345149Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm', 'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13'} | null | {'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13'} | {'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13'} |
PyPI | PYSEC-2021-189 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29552', 'GHSA-jhq9-wm9m-cf89'} | 2021-08-27T03:22:30.663551Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89'} | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} |
PyPI | GHSA-3q6g-vf58-7m4g | Regular Expression Denial of Service in flask-restx | Flask RESTX contains a regular expression that is vulnerable to [ReDoS](https://owasp.org/www-community/attacks/Regular_expression_Denial_of_Service_-_ReDoS) (Regular Expression Denial of Service) in `email_regex`.
| {'CVE-2021-32838'} | 2022-03-03T05:13:37.106645Z | 2021-09-08T15:41:15Z | HIGH | null | {'CWE-400'} | {'https://github.com/advisories/GHSA-3q6g-vf58-7m4g', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32838', 'https://github.com/python-restx/flask-restx', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5UCTFVDU3677B5OBGK4EF5NMUPJLL6SQ/', 'https://github.com/python-restx/flask-restx/commit/bab31e085f355dd73858fd3715f7ed71849656da', 'https://github.com/python-restx/flask-restx/security/advisories/GHSA-3q6g-vf58-7m4g', 'https://github.com/python-restx/flask-restx/blob/fd99fe11a88531f5f3441a278f7020589f9d2cc0/flask_restx/inputs.py#L51', 'https://pypi.org/project/flask-restx/', 'https://github.com/python-restx/flask-restx/issues/372', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QUD6SWZLX52AAZUHDETJ2CDMQGEPGFL3/'} | null | {'https://github.com/python-restx/flask-restx/commit/bab31e085f355dd73858fd3715f7ed71849656da'} | {'https://github.com/python-restx/flask-restx/commit/bab31e085f355dd73858fd3715f7ed71849656da'} |
PyPI | PYSEC-2017-70 | null | salt before 2015.5.5 leaks git usernames and passwords to the log. | {'CVE-2015-6918'} | 2021-07-25T23:34:53.773176Z | 2017-10-10T16:29:00Z | null | null | null | {'https://github.com/saltstack/salt/commit/28aa9b105804ff433d8f663b2f9b804f2b75495a', 'https://bugzilla.redhat.com/show_bug.cgi?id=1257154'} | null | {'https://github.com/saltstack/salt/commit/28aa9b105804ff433d8f663b2f9b804f2b75495a'} | {'https://github.com/saltstack/salt/commit/28aa9b105804ff433d8f663b2f9b804f2b75495a'} |
PyPI | GHSA-8c89-2vwr-chcq | Heap buffer overflow in `QuantizedResizeBilinear` | ### Impact
An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization:
```python
import tensorflow as tf
images = tf.constant([], shape=[0], dtype=tf.qint32)
size = tf.constant([], shape=[0], dtype=tf.int32)
min = tf.constant([], dtype=tf.float32)
max = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=False, half_pixel_centers=False)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:
```cc
const float in_min = context->input(2).flat<float>()(0);
const float in_max = context->input(3).flat<float>()(0);
```
However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow.
### Patches
We have patched the issue in GitHub commit [f6c40f0c6cbf00d46c7717a26419f2062f2f8694](https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29537'} | 2022-03-03T05:13:57.936315Z | 2021-05-21T14:22:35Z | LOW | null | {'CWE-787', 'CWE-131'} | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29537'} | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} |
PyPI | GHSA-4pwq-fj89-6rjc | Cross-site Scripting in Apache Airflow | In Apache Airflow < 1.10.12, the "origin" parameter passed to some of the endpoints like '/trigger' was vulnerable to XSS exploit. | {'CVE-2020-13944'} | 2022-03-03T05:13:09.465862Z | 2021-06-18T18:29:54Z | MODERATE | null | {'CWE-79'} | {'https://lists.apache.org/thread.html/r97e1b60ca508a86be58c43f405c0c8ff00ba467ba0bee68704ae7e3e%40%3Cdev.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cannounce.apache.org%3E', 'https://nvd.nist.gov/vuln/detail/CVE-2020-13944', 'http://www.openwall.com/lists/oss-security/2021/05/01/2', 'https://lists.apache.org/thread.html/ra8ce70088ba291f358e077cafdb14d174b7a1ce9a9d86d1b332d6367@%3Cusers.airflow.apache.org%3E', 'http://www.openwall.com/lists/oss-security/2020/12/11/2', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cdev.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/rc005f4de9d9b0ba943ceb8ff5a21a5c6ff8a9df52632476698d99432@%3Cannounce.apache.org%3E', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cusers.airflow.apache.org%3E', 'https://github.com/apache/airflow/commit/5c2bb7b0b0e717b11f093910b443243330ad93ca', 'https://lists.apache.org/thread.html/r4656959c8ed06c1f6202d89aa4e67b35ad7bdba5a666caff3fea888e@%3Cusers.airflow.apache.org%3E'} | null | {'https://github.com/apache/airflow/commit/5c2bb7b0b0e717b11f093910b443243330ad93ca'} | {'https://github.com/apache/airflow/commit/5c2bb7b0b0e717b11f093910b443243330ad93ca'} |
PyPI | GHSA-f8h4-7rgh-q2gm | Segfault and heap buffer overflow in `{Experimental,}DatasetToTFRecord` | ### Impact
The implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault:
```python
import tensorflow as tf
dataset = tf.data.Dataset.range(3)
dataset = tf.data.experimental.to_variant(dataset)
tf.raw_ops.ExperimentalDatasetToTFRecord(
input_dataset=dataset,
filename='/tmp/output',
compression_type='')
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types.
### Patches
We have patched the issue in GitHub commit [e0b6e58c328059829c3eb968136f17aa72b6c876](https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37650'} | 2022-03-03T05:13:34.854257Z | 2021-08-25T14:43:24Z | HIGH | null | {'CWE-120', 'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37650', '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-2021-648 | null | TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29522', 'GHSA-c968-pq7h-7fxv'} | 2021-12-09T06:35:18.591146Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv'} | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa'} | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa'} |
PyPI | PYSEC-2021-218 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults. The implementation(https://github.com/tensorflow/tensorflow/blob/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7/tensorflow/core/kernels/ctc_decoder_ops.cc#L68-L79) fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29581', 'GHSA-vq2r-5xvm-3hc3'} | 2021-08-27T03:22:35.737731Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq2r-5xvm-3hc3', 'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} |
PyPI | PYSEC-2021-832 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41225', 'GHSA-7r94-xv9v-63jw'} | 2021-12-09T06:35:44.943479Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw'} | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3'} | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3'} |
PyPI | PYSEC-2020-131 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-mxjj-953w-2c2v', 'CVE-2020-15208'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', '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 | PYSEC-2021-131 | 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 1.25.0, requests to user provided domains were not restricted to external IP addresses when calculating the key validity for third-party invite events and sending push notifications. This could cause Synapse to make requests to internal infrastructure. The type of request was not controlled by the user, although limited modification of request bodies was possible. For the most thorough protection server administrators should remove the deprecated `federation_ip_range_blacklist` from their settings after upgrading to Synapse v1.25.0 which will result in Synapse using the improved default IP address restrictions. See the new `ip_range_blacklist` and `ip_range_whitelist` settings if more specific control is necessary. | {'GHSA-v936-j8gp-9q3p', 'CVE-2021-21273'} | 2021-08-27T03:22:06.569635Z | 2021-02-26T18:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/releases/tag/v1.25.0', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-v936-j8gp-9q3p', 'https://github.com/matrix-org/synapse/commit/30fba6210834a4ecd91badf0c8f3eb278b72e746', 'https://github.com/matrix-org/synapse/pull/8821'} | null | {'https://github.com/matrix-org/synapse/commit/30fba6210834a4ecd91badf0c8f3eb278b72e746'} | {'https://github.com/matrix-org/synapse/commit/30fba6210834a4ecd91badf0c8f3eb278b72e746'} |
PyPI | PYSEC-2021-561 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37648', 'GHSA-wp77-4gmm-7cq8'} | 2021-12-09T06:35:03.096515Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp77-4gmm-7cq8', 'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | null | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} |
PyPI | PYSEC-2022-138 | null | Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range. | {'GHSA-77gp-3h4r-6428', 'CVE-2022-23574'} | 2022-03-09T00:18:27.547711Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L81-L102', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-77gp-3h4r-6428', 'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} | null | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} |
PyPI | PYSEC-2021-424 | null | Matrix is an ecosystem for open federated Instant Messaging and Voice over IP. In versions 1.41.0 and prior, unauthorised users can access the name, avatar, topic and number of members of a room if they know the ID of the room. This vulnerability is limited to homeservers where the vulnerable homeserver is in the room and untrusted users are permitted to create groups (communities). By default, only homeserver administrators can create groups. However, homeserver administrators can already access this information in the database or using the admin API. As a result, only homeservers where the configuration setting `enable_group_creation` has been set to `true` are impacted. Server administrators should upgrade to 1.41.1 or higher to patch the vulnerability. There are two potential workarounds. Server administrators can set `enable_group_creation` to `false` in their homeserver configuration (this is the default value) to prevent creation of groups by non-administrators. Administrators that are using a reverse proxy could, with partial loss of group functionality, block the endpoints `/_matrix/client/r0/groups/{group_id}/rooms` and `/_matrix/client/unstable/groups/{group_id}/rooms`. | {'CVE-2021-39163', 'GHSA-jj53-8fmw-f2w2'} | 2021-11-16T03:58:44.500451Z | 2021-08-31T16:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/PXT7ID7DNBRN2TVTETU3SYQHJKEG6PXN/', 'https://github.com/matrix-org/synapse/releases/tag/v1.41.1', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-jj53-8fmw-f2w2', 'https://github.com/matrix-org/synapse/commit/cb35df940a', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2VHDEPCZ22GJFMZCWA2XZAGPOEV72POF/'} | null | {'https://github.com/matrix-org/synapse/commit/cb35df940a'} | {'https://github.com/matrix-org/synapse/commit/cb35df940a'} |
PyPI | GHSA-5f2r-qp73-37mr | `CHECK`-failures during Grappler's `SafeToRemoveIdentity` in Tensorflow | ### Impact
The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that [`SafeToRemoveIdentity`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/dependency_optimizer.cc#L59-L98) would trigger `CHECK` failures.
### Patches
We have patched the issue in GitHub commit [92dba16749fae36c246bec3f9ba474d9ddeb7662](https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662).
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-2022-23579'} | 2022-03-03T05:13:50.267333Z | 2022-02-10T00:33:29Z | MODERATE | null | {'CWE-617'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23579', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/dependency_optimizer.cc#L59-L98', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5f2r-qp73-37mr'} | null | {'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662'} | {'https://github.com/tensorflow/tensorflow/commit/92dba16749fae36c246bec3f9ba474d9ddeb7662'} |
PyPI | GHSA-mvg9-xffr-p774 | Out of bounds read in Pillow | An issue was discovered in Pillow before 8.1.1. In TiffDecode.c, there is an out-of-bounds read in TiffreadRGBATile via invalid tile boundaries. | {'CVE-2021-25291'} | 2022-03-03T05:14:07.810296Z | 2021-03-29T16:35:57Z | HIGH | null | {'CWE-125'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25291', 'https://github.com/python-pillow/Pillow/commit/cbdce6c5d054fccaf4af34b47f212355c64ace7a', 'https://security.gentoo.org/glsa/202107-33', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/cbdce6c5d054fccaf4af34b47f212355c64ace7a'} | {'https://github.com/python-pillow/Pillow/commit/cbdce6c5d054fccaf4af34b47f212355c64ace7a'} |
PyPI | GHSA-5gqf-456p-4836 | Reference binding to nullptr in `SdcaOptimizer` | ### Impact
The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer:
```python
import tensorflow as tf
sparse_example_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_indices = [tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_feature_values = []
dense_features = []
dense_weights = []
example_weights = tf.constant((0.0), dtype=tf.float32)
example_labels = tf.constant((0.0), dtype=tf.float32)
sparse_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)]
sparse_weights = [tf.constant((0.0), dtype=tf.float32), tf.constant((0.0), dtype=tf.float32)]
example_state_data = tf.constant([0.0, 0.0, 0.0, 0.0], shape=[1, 4], dtype=tf.float32)
tf.raw_ops.SdcaOptimizer(
sparse_example_indices=sparse_example_indices,
sparse_feature_indices=sparse_feature_indices,
sparse_feature_values=sparse_feature_values, dense_features=dense_features,
example_weights=example_weights, example_labels=example_labels,
sparse_indices=sparse_indices, sparse_weights=sparse_weights,
dense_weights=dense_weights, example_state_data=example_state_data,
loss_type="logistic_loss", l1=0.0, l2=0.0, num_loss_partitions=1,
num_inner_iterations=1, adaptative=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all [constraints expected by the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer).
### Patches
We have patched the issue in GitHub commit [f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb](https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb).
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-2021-29572'} | 2022-03-03T05:13:10.211775Z | 2021-05-21T14:25:31Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5gqf-456p-4836', 'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29572'} | null | {'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} | {'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} |
PyPI | GHSA-vmjw-c2vp-p33c | Crash in NMS ops caused by integer conversion to unsigned | ### Impact
An attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0:
```python
import tensorflow as tf
tf.raw_ops.NonMaxSuppressionV5(
boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],
scores=[1.0,2.0,3.0],
max_output_size=-1,
iou_threshold=0.5,
score_threshold=0.5,
soft_nms_sigma=1.0,
pad_to_max_output_size=True)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`:
```cc
const int output_size = max_output_size.scalar<int>()();
// ...
std::vector<int> selected;
// ...
if (pad_to_max_output_size) {
selected.resize(output_size, 0);
// ...
}
```
However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.
A similar issue occurs in `CombinedNonMaxSuppression`:
```python
import tensorflow as tf
tf.raw_ops.NonMaxSuppressionV5(
boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]],
scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]],
max_output_size_per_class=-1,
max_total_size=10,
iou_threshold=score_threshold=0.5,
pad_per_class=True,
clip_boxes=True)
```
### Patches
We have patched the issue in GitHub commit [3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d](https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d) and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58](https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58).
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-2021-37669'} | 2022-03-03T05:14:08.140626Z | 2021-08-25T14:42:03Z | MODERATE | null | {'CWE-681'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37669', 'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58'} | null | {'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d', 'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58'} | {'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58', 'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d'} |
PyPI | PYSEC-2021-577 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37664', 'GHSA-r4c4-5fpq-56wg'} | 2021-12-09T06:35:04.439609Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4c4-5fpq-56wg', 'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | null | {'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | {'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} |
PyPI | PYSEC-2020-127 | null | In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-q8gv-q7wr-9jf8', 'CVE-2020-15204'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1'} | {'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1'} |
PyPI | GHSA-ch4f-829c-v5pw | Division by 0 in `ResourceScatterDiv` | ### Impact
The implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error:
```python
import tensorflow as tf
v= tf.Variable([1,2,3])
tf.raw_ops.ResourceScatterDiv(
resource=v.handle,
indices=[1],
updates=[0])
```
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.
### Patches
We have patched the issue in GitHub commit [4aacb30888638da75023e6601149415b39763d76](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37642'} | 2022-03-03T05:13:41.042235Z | 2021-08-25T14:43:56Z | MODERATE | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37642', '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-2021-824 | null | TensorFlow is an open source platform for machine learning. In affected versions the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an `Enter` node) always exists when encountering the second node (e.g., an `Exit` node). When this is not the case, `parent` is `nullptr` so dereferencing it causes a crash. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41217', 'GHSA-5crj-c72x-m7gq'} | 2021-12-09T06:35:43.751303Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq'} | null | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff'} | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff'} |
PyPI | PYSEC-2021-432 | null | Nanopb is a small code-size Protocol Buffers implementation in ansi C. In Nanopb before versions 0.3.9.8 and 0.4.5, decoding a specifically formed message can cause invalid `free()` or `realloc()` calls if the message type contains an `oneof` field, and the `oneof` directly contains both a pointer field and a non-pointer field. If the message data first contains the non-pointer field and then the pointer field, the data of the non-pointer field is incorrectly treated as if it was a pointer value. Such message data rarely occurs in normal messages, but it is a concern when untrusted data is parsed. This has been fixed in versions 0.3.9.8 and 0.4.5. See referenced GitHub Security Advisory for more information including workarounds. | {'CVE-2021-21401', 'GHSA-7mv5-5mxh-qg88'} | 2021-11-24T22:47:12.152718Z | 2021-03-23T18:15:00Z | null | null | null | {'https://github.com/nanopb/nanopb/security/advisories/GHSA-7mv5-5mxh-qg88', 'https://github.com/nanopb/nanopb/blob/c9124132a604047d0ef97a09c0e99cd9bed2c818/CHANGELOG.txt#L1', 'https://github.com/nanopb/nanopb/issues/647', 'https://github.com/nanopb/nanopb/commit/e2f0ccf939d9f82931d085acb6df8e9a182a4261'} | null | {'https://github.com/nanopb/nanopb/commit/e2f0ccf939d9f82931d085acb6df8e9a182a4261'} | {'https://github.com/nanopb/nanopb/commit/e2f0ccf939d9f82931d085acb6df8e9a182a4261'} |
PyPI | PYSEC-2014-99 | null | Multiple cross-site scripting (XSS) vulnerabilities in the respond_error function in routing.py in Eugene Pankov Ajenti before 1.2.21.7 allow remote attackers to inject arbitrary web script or HTML via the PATH_INFO to (1) resources.js or (2) resources.css in ajenti:static/, related to the traceback page. | {'CVE-2014-4301'} | 2021-12-13T06:35:03.086455Z | 2014-06-18T14:55:00Z | null | null | null | {'http://www.securityfocus.com/bid/68047', 'https://www.netsparker.com/critical-xss-vulnerabilities-in-ajenti', 'http://secunia.com/advisories/59177', 'https://github.com/Eugeny/ajenti/commit/d3fc5eb142ff16d55d158afb050af18d5ff09120'} | null | {'https://github.com/Eugeny/ajenti/commit/d3fc5eb142ff16d55d158afb050af18d5ff09120'} | {'https://github.com/Eugeny/ajenti/commit/d3fc5eb142ff16d55d158afb050af18d5ff09120'} |
PyPI | PYSEC-2021-598 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37685', 'GHSA-c545-c4f9-rf6v'} | 2021-12-09T06:35:06.268797Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c545-c4f9-rf6v', 'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | null | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} |
PyPI | PYSEC-2021-609 | null | TensorFlow is an open source platform for machine learning. In affected versions if `tf.image.resize` 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. | {'CVE-2021-41199', 'GHSA-5hx2-qx8j-qjqm'} | 2021-12-09T06:35:07.452136Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm', 'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837', 'https://github.com/tensorflow/tensorflow/issues/46914'} | null | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837'} | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837'} |
PyPI | PYSEC-2021-46 | null | before_upstream_connection in AuthPlugin in http/proxy/auth.py in proxy.py before 2.3.1 accepts incorrect Proxy-Authorization header data because of a boolean confusion (and versus or). | {'CVE-2021-3116'} | 2021-01-14T15:09:00Z | 2021-01-11T05:15:00Z | null | null | null | {'https://cardaci.xyz/advisories/2021/01/10/proxy.py-2.3.0-broken-basic-authentication/', 'https://github.com/abhinavsingh/proxy.py/pull/482/commits/9b00093288237f5073c403f2c4f62acfdfa8ed46', 'https://pypi.org/project/proxy.py/2.3.1/#history'} | null | {'https://github.com/abhinavsingh/proxy.py/pull/482/commits/9b00093288237f5073c403f2c4f62acfdfa8ed46'} | {'https://github.com/abhinavsingh/proxy.py/pull/482/commits/9b00093288237f5073c403f2c4f62acfdfa8ed46'} |
PyPI | PYSEC-2021-259 | null | TensorFlow is an end-to-end open source platform for machine learning. It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. We have patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37637', 'GHSA-c9qf-r67m-p7cg'} | 2021-08-27T03:22:42.844418Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9qf-r67m-p7cg'} | null | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5'} | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5'} |
PyPI | PYSEC-2020-84 | null | libImaging/FliDecode.c in Pillow before 6.2.2 has an FLI buffer overflow. | {'CVE-2020-5313', 'GHSA-hj69-c76v-86wr'} | 2020-02-18T16:15:00Z | 2020-01-03T01:15:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commit/a09acd0decd8a87ccce939d5ff65dab59e7d365b', 'https://github.com/advisories/GHSA-hj69-c76v-86wr', 'https://www.debian.org/security/2020/dsa-4631', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2MMU3WT2X64GS5WHDPKKC2WZA7UIIQ3A/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3DUMIBUYGJRAVJCTFUWBRLVQKOUTVX5P/', 'https://usn.ubuntu.com/4272-1/', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.2.html'} | null | {'https://github.com/python-pillow/Pillow/commit/a09acd0decd8a87ccce939d5ff65dab59e7d365b'} | {'https://github.com/python-pillow/Pillow/commit/a09acd0decd8a87ccce939d5ff65dab59e7d365b'} |
PyPI | PYSEC-2021-170 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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-393f-2jr3-cp69', 'CVE-2021-29533'} | 2021-08-27T03:22:27.240459Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69', 'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} |
PyPI | PYSEC-2021-520 | null | TensorFlow is an end-to-end open source platform for machine learning. The fix for CVE-2020-15209(https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability(https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape. 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-2021-29592', 'GHSA-jjr8-m8g8-p6wv'} | 2021-12-09T06:34:57.625576Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jjr8-m8g8-p6wv'} | null | {'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16'} | {'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16'} |
PyPI | PYSEC-2021-465 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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-8c89-2vwr-chcq', 'CVE-2021-29537'} | 2021-12-09T06:34:49.104886Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq'} | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} |
PyPI | PYSEC-2022-56 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. 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-c582-c96p-r5cq', 'CVE-2022-21732'} | 2022-03-09T00:17:30.817713Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/experimental/threadpool_dataset_op.cc#L79-L135'} | null | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e'} | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e'} |
PyPI | PYSEC-2019-136 | null | Waitress through version 1.3.1 implemented a "MAY" part of the RFC7230 which states: "Although the line terminator for the start-line and header fields is the sequence CRLF, a recipient MAY recognize a single LF as a line terminator and ignore any preceding CR." Unfortunately if a front-end server does not parse header fields with an LF the same way as it does those with a CRLF it can lead to the front-end and the back-end server parsing the same HTTP message in two different ways. This can lead to a potential for HTTP request smuggling/splitting whereby Waitress may see two requests while the front-end server only sees a single HTTP message. This issue is fixed in Waitress 1.4.0. | {'GHSA-pg36-wpm5-g57p', 'CVE-2019-16785'} | 2020-02-25T17:15:00Z | 2019-12-20T23:15:00Z | null | null | null | {'https://github.com/Pylons/waitress/security/advisories/GHSA-pg36-wpm5-g57p', 'https://access.redhat.com/errata/RHSA-2020:0720', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GVDHR2DNKCNQ7YQXISJ45NT4IQDX3LJ7/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LYEOTGWJZVKPRXX2HBNVIYWCX73QYPM5/', 'https://docs.pylonsproject.org/projects/waitress/en/latest/#security-fixes', 'https://github.com/Pylons/waitress/commit/8eba394ad75deaf9e5cd15b78a3d16b12e6b0eba'} | null | {'https://github.com/Pylons/waitress/commit/8eba394ad75deaf9e5cd15b78a3d16b12e6b0eba'} | {'https://github.com/Pylons/waitress/commit/8eba394ad75deaf9e5cd15b78a3d16b12e6b0eba'} |
PyPI | PYSEC-2021-317 | null | The package pillow from 0 and before 8.3.2 are vulnerable to Regular Expression Denial of Service (ReDoS) via the getrgb function. | {'CVE-2021-23437', 'GHSA-98vv-pw6r-q6q4', 'SNYK-PYTHON-PILLOW-1319443'} | 2021-09-03T18:35:52.828411Z | 2021-09-03T16:15:00Z | null | null | null | {'https://snyk.io/vuln/SNYK-PYTHON-PILLOW-1319443', 'https://pillow.readthedocs.io/en/stable/releasenotes/8.3.2.html', 'https://github.com/advisories/GHSA-98vv-pw6r-q6q4', 'https://github.com/python-pillow/Pillow/commit/9e08eb8f78fdfd2f476e1b20b7cf38683754866b'} | null | {'https://github.com/python-pillow/Pillow/commit/9e08eb8f78fdfd2f476e1b20b7cf38683754866b'} | {'https://github.com/python-pillow/Pillow/commit/9e08eb8f78fdfd2f476e1b20b7cf38683754866b'} |
PyPI | GHSA-jff3-mwp3-f8cw | Exposure of Sensitive Information to an Unauthorized Actor in Products.GenericSetup | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Information disclosure vulnerability - anonymous visitors may view log and snapshot files generated by the Generic Setup Tool.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
The problem has been fixed in version 2.1.1. Depending on how you have installed Products.GenericSetup, you should change the buildout version pin to 2.1.1 and re-run the buildout, or if you used pip simply do pip install `"Products.GenericSetup>=2.1.1"`
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
Visit the ZMI Security tab at `portal_setup/manage_access` and click on the link _Access contents information_. On the next page, uncheck the box _Also use roles acquired from folders containing this objects_ at the bottom and check the boxes for _Manager_ and _Owner_. Then click on _Save Changes_. Return to the ZMI Security tab at `portal_setup/manage_access` and scroll down to the link _View_. Click on _View_, uncheck the box _Also use roles acquired from folders containing this objects_ at the bottom and check the boxes for _Manager_ and _Owner_. Then click on _Save Changes_.
### References
_Are there any links users can visit to find out more?_
- [GHSA-jff3-mwp3-f8cw](https://github.com/zopefoundation/Products.GenericSetup/security/advisories/GHSA-jff3-mwp3-f8cw)
- [Products.GenericSetup on PyPI](https://pypi.org/project/Products.GenericSetup/)
- [Definition of information disclosure at MITRE](https://cwe.mitre.org/data/definitions/200.html)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Products.GenericSetup issue tracker](https://github.com/zopefoundation/Products.GenericSetup/issues)
* Email us at [security@plone.org](mailto:security@plone.org) | {'CVE-2021-21360'} | 2022-03-03T05:13:45.084029Z | 2021-03-09T00:38:31Z | LOW | null | {'CWE-200'} | {'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'https://pypi.org/project/Products.GenericSetup/', 'https://github.com/zopefoundation/Products.GenericSetup/security/advisories/GHSA-jff3-mwp3-f8cw', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21360', 'https://github.com/zopefoundation/Products.GenericSetup/commit/700319512b3615b3871a1f24e096cf66dc488c57', 'http://www.openwall.com/lists/oss-security/2021/05/21/1'} | null | {'https://github.com/zopefoundation/Products.GenericSetup/commit/700319512b3615b3871a1f24e096cf66dc488c57'} | {'https://github.com/zopefoundation/Products.GenericSetup/commit/700319512b3615b3871a1f24e096cf66dc488c57'} |
PyPI | PYSEC-2021-747 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseDenseCwiseDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) 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 d9204be9f49520cdaaeb2541d1dc5187b23f31d9. 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-2021-37636', 'GHSA-hp4c-x6r7-6555'} | 2021-12-09T06:35:35.406311Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hp4c-x6r7-6555'} | null | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9'} | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9'} |
PyPI | PYSEC-2020-317 | 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'} | 2021-12-09T06:35:13.455948Z | 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 | GHSA-c4rh-4376-gff4 | Improper certificate management in AWS IoT Device SDK v2 | The AWS IoT Device SDK v2 for Java, Python, C++ and Node.js appends a user supplied Certificate Authority (CA) to the root CAs instead of overriding it on Unix systems. TLS handshakes will thus succeed if the peer can be verified either from the user-supplied CA or the system’s default trust-store. Attackers with access to a host’s trust stores or are able to compromise a certificate authority already in the host's trust store (note: the attacker must also be able to spoof DNS in this case) may be able to use this issue to bypass CA pinning. An attacker could then spoof the MQTT broker, and either drop traffic and/or respond with the attacker's data, but they would not be able to forward this data on to the MQTT broker because the attacker would still need the user's private keys to authenticate against the MQTT broker. The 'aws_tls_ctx_options_override_default_trust_store_*' function within the aws-c-io submodule has been updated to override the default trust store. This corrects this issue. This issue affects: Amazon Web Services AWS IoT Device SDK v2 for Java versions prior to 1.5.0 on Linux/Unix. Amazon Web Services AWS IoT Device SDK v2 for Python versions prior to 1.6.1 on Linux/Unix. Amazon Web Services AWS IoT Device SDK v2 for C++ versions prior to 1.12.7 on Linux/Unix. Amazon Web Services AWS IoT Device SDK v2 for Node.js versions prior to 1.5.3 on Linux/Unix. Amazon Web Services AWS-C-IO 0.10.4 on Linux/Unix. | {'CVE-2021-40830'} | 2022-03-03T05:13:15.678455Z | 2021-11-24T21:12:04Z | MODERATE | null | {'CWE-295'} | {'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/0450ce68add7e3d05c6d781ecdac953c299c053a', 'https://nvd.nist.gov/vuln/detail/CVE-2021-40830', 'https://github.com/aws/aws-iot-device-sdk-java-v2/commit/67950ad2a02f2f9355c310b69dc9226b017f32f2', 'https://github.com/aws/aws-iot-device-sdk-python-v2', 'https://github.com/awslabs/aws-c-io/', 'https://github.com/aws/aws-iot-device-sdk-cpp-v2', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/53a36e3ac203291494120604d416b6de59177cac', 'https://github.com/aws/aws-iot-device-sdk-js-v2', 'https://github.com/aws/aws-iot-device-sdk-java-v2'} | null | {'https://github.com/aws/aws-iot-device-sdk-java-v2/commit/67950ad2a02f2f9355c310b69dc9226b017f32f2', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/53a36e3ac203291494120604d416b6de59177cac', 'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/0450ce68add7e3d05c6d781ecdac953c299c053a'} | {'https://github.com/aws/aws-iot-device-sdk-java-v2/commit/67950ad2a02f2f9355c310b69dc9226b017f32f2', 'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/0450ce68add7e3d05c6d781ecdac953c299c053a', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/53a36e3ac203291494120604d416b6de59177cac'} |
PyPI | GHSA-ccgm-3xw4-h5p8 | Improper Restriction of XML External Entity Reference in pikepdf | models/metadata.py in the pikepdf package 1.3.0 through 2.9.2 for Python allows XXE when parsing XMP metadata entries. | {'CVE-2021-29421'} | 2022-03-03T05:13:15.711071Z | 2021-04-20T16:30:03Z | HIGH | null | {'CWE-611'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3QFLBBYGEDNXJ7FS6PIWTVI4T4BUPGEQ/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29421', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/36P4HTLBJPO524WMQWW57N3QRF4RFSJG/', 'https://github.com/pikepdf/pikepdf/commit/3f38f73218e5e782fe411ccbb3b44a793c0b343a'} | null | {'https://github.com/pikepdf/pikepdf/commit/3f38f73218e5e782fe411ccbb3b44a793c0b343a'} | {'https://github.com/pikepdf/pikepdf/commit/3f38f73218e5e782fe411ccbb3b44a793c0b343a'} |
PyPI | PYSEC-2021-252 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `ParseAttrValue`(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/framework/attr_value_util.cc#L397-L453) can be tricked into stack overflow due to recursion by giving in a specially crafted input. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-qw5h-7f53-xrp6', 'CVE-2021-29615'} | 2021-08-27T03:22:41.882183Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qw5h-7f53-xrp6', 'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} | null | {'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} | {'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} |
PyPI | PYSEC-2021-602 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-wf5p-c75w-w3wh', 'CVE-2021-37689'} | 2021-12-09T06:35:06.599796Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wf5p-c75w-w3wh', 'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} |
PyPI | GHSA-v768-w7m9-2vmm | Reference binding to nullptr in shape inference | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.SparseFillEmptyRows`:
```python
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.SparseFillEmptyRows(
indices = tf.constant([], shape=[0, 0], dtype=tf.int64),
values = tf.constant([], shape=[0], dtype=tf.int64),
dense_shape = tf.constant([], shape=[0], dtype=tf.int64),
default_value = 0)
```
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.
### Patches
We have patched the issue in GitHub commit [578e634b4f1c1c684d4b4294f9e5281b2133b3ed](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37676'} | 2022-03-03T05:14:01.276269Z | 2021-08-25T14:41:26Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v768-w7m9-2vmm', 'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37676'} | null | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} |
PyPI | GHSA-m9mq-p2f9-cfqv | Critical severity vulnerability that affects bleach | An issue was discovered in Bleach 2.1.x before 2.1.3. Attributes that have URI values weren't properly sanitized if the values contained character entities. Using character entities, it was possible to construct a URI value with a scheme that was not allowed that would slide through unsanitized. | {'CVE-2018-7753'} | 2022-03-07T20:47:08.254480Z | 2019-01-04T17:46:30Z | CRITICAL | null | {'CWE-20'} | {'https://nvd.nist.gov/vuln/detail/CVE-2018-7753', 'https://github.com/mozilla/bleach', 'https://bugs.debian.org/892252', 'https://github.com/advisories/GHSA-m9mq-p2f9-cfqv', 'https://github.com/mozilla/bleach/commit/c5df5789ec3471a31311f42c2d19fc2cf21b35ef', 'https://github.com/mozilla/bleach/releases/tag/v2.1.3'} | null | {'https://github.com/mozilla/bleach/commit/c5df5789ec3471a31311f42c2d19fc2cf21b35ef'} | {'https://github.com/mozilla/bleach/commit/c5df5789ec3471a31311f42c2d19fc2cf21b35ef'} |
PyPI | PYSEC-2021-481 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` argument. 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-2021-29553', 'GHSA-h9px-9vqg-222h'} | 2021-12-09T06:34:51.614588Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h9px-9vqg-222h', 'https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387'} | null | {'https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387'} | {'https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387'} |
PyPI | PYSEC-2021-194 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.SparseMatMul`. The division by 0 occurs deep in Eigen code because the `b` tensor is empty. 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-xw93-v57j-fcgh', 'CVE-2021-29557'} | 2021-08-27T03:22:31.559796Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xw93-v57j-fcgh'} | null | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5'} | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5'} |
PyPI | PYSEC-2021-710 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in caused by an integer overflow in constructing a new tensor shape. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-xvjm-fvxx-q3hv', 'CVE-2021-29584'} | 2021-12-09T06:35:29.199701Z | 2021-05-14T20:15:00Z | null | null | null | {'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 | GHSA-6p5r-g9mq-ggh2 | Reference binding to nullptr in `MatrixSetDiagV*` ops | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixSetDiagV*`:
```python
import tensorflow as tf
tf.raw_ops.MatrixSetDiagV3(
input=[1,2,3],
diagonal=[1,1],
k=[],
align='RIGHT_LEFT')
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong:
```cc
auto& diag_index = context->input(1);
...
lower_diag_index = diag_index.flat<int32>()(0);
```
### Patches
We have patched the issue in GitHub commit [ff8894044dfae5568ecbf2ed514c1a37dc394f1b](https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b).
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-2021-37658'} | 2022-03-03T05:14:20.827120Z | 2021-08-25T14:42:49Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37658', 'https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6p5r-g9mq-ggh2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b'} | {'https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b'} |
PyPI | PYSEC-2021-655 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-jfp7-4j67-8r3q', 'CVE-2021-29529'} | 2021-12-09T06:35:19.746209Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q'} | null | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7'} | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7'} |
PyPI | PYSEC-2021-205 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty 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. | {'GHSA-4p4p-www8-8fv9', 'CVE-2021-29568'} | 2021-08-27T03:22:33.499981Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4p4p-www8-8fv9'} | null | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8'} | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8'} |
PyPI | GHSA-c9f3-9wfr-wgh7 | Lack of validation in data format attributes in TensorFlow | ### Impact
The `tf.raw_ops.DataFormatVecPermute` API does not validate the `src_format` and `dst_format` attributes. [The code](https://github.com/tensorflow/tensorflow/blob/304b96815324e6a73d046df10df6626d63ac12ad/tensorflow/core/kernels/data_format_ops.cc) assumes that these two arguments define a permutation of `NHWC`.
However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.
```python
>>> import tensorflow as tf
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
src_format='1234', dst_format='1253')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
src_format='1234', dst_format='1223')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
src_format='1224', dst_format='1423')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
src_format='12345678', dst_format='87654321')
munmap_chunk(): invalid pointer
Aborted
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],
src_format='12345678', dst_format='87654321')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[71364624, 0],
[71365824, 0],
[ 560, 0],
[ 48, 0]], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],
src_format='12345678', dst_format='87654321')
free(): invalid next size (fast)
Aborted
```
A similar issue occurs in `tf.raw_ops.DataFormatDimMap`, for the same reasons:
```python
>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',
>>> dst_format='8765')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1954047348, 1954047348],
[1852793646, 1852793646],
[1954047348, 1954047348],
[1852793632, 1852793632]], dtype=int32)>
```
### Patches
We have patched the issue in GitHub commit [ebc70b7a592420d3d2f359e4b1694c236b82c7ae](https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-26267'} | 2022-03-03T05:13:36.799741Z | 2020-12-10T19:07:26Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26267'} | null | {'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} | {'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} |
PyPI | PYSEC-2017-82 | null | The salt-ssh minion code in SaltStack Salt 2016.11 before 2016.11.4 copied over configuration from the Salt Master without adjusting permissions, which might leak credentials to local attackers on configured minions (clients). | {'CVE-2017-8109'} | 2021-08-25T04:30:30.228761Z | 2017-04-25T17:59:00Z | null | null | null | {'https://docs.saltstack.com/en/latest/topics/releases/2016.11.4.html', 'https://github.com/saltstack/salt/pull/40609', 'http://www.securityfocus.com/bid/98095', 'https://github.com/saltstack/salt/issues/40075', 'https://github.com/saltstack/salt/pull/40609/commits/6e34c2b5e5e849302af7ccd00509929c3809c658', 'https://bugzilla.suse.com/show_bug.cgi?id=1035912'} | null | {'https://github.com/saltstack/salt/pull/40609/commits/6e34c2b5e5e849302af7ccd00509929c3809c658'} | {'https://github.com/saltstack/salt/pull/40609/commits/6e34c2b5e5e849302af7ccd00509929c3809c658'} |
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