ecosystem
stringclasses 11
values | vuln_id
stringlengths 10
19
| summary
stringlengths 4
220
⌀ | details
stringlengths 34
13.5k
| aliases
stringlengths 17
87
⌀ | modified_date
stringdate 2019-03-26 14:13:00
2022-05-10 08:46:52
| published_date
stringdate 2012-06-17 03:41:00
2022-05-10 08:46:50
| severity
stringclasses 5
values | score
float64 0
10
⌀ | cwe_id
stringclasses 581
values | refs
stringlengths 82
11.6k
| introduced
stringclasses 843
values | code_refs
stringlengths 46
940
| commits
stringlengths 46
940
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2020-205 | null | Multiple argument injection vulnerabilities in Ansible before 1.6.7 allow remote attackers to execute arbitrary code by leveraging access to an Ansible managed host and providing a crafted fact, as demonstrated by a fact with (1) a trailing " src=" clause, (2) a trailing " temp=" clause, or (3) a trailing " validate=" clause accompanied by a shell command. | {'CVE-2014-4967'} | 2021-07-02T02:41:33.376176Z | 2020-02-18T15:15:00Z | null | null | null | {'https://github.com/ansible/ansible/commit/62a1295a3e08cb6c3e9f1b2a1e6e5dcaeab32527', 'http://www.ocert.org/advisories/ocert-2014-004.html'} | null | {'https://github.com/ansible/ansible/commit/62a1295a3e08cb6c3e9f1b2a1e6e5dcaeab32527'} | {'https://github.com/ansible/ansible/commit/62a1295a3e08cb6c3e9f1b2a1e6e5dcaeab32527'} |
PyPI | GHSA-4fg4-p75j-w5xj | Heap out of bounds in `QuantizedBatchNormWithGlobalNormalization` | ### Impact
An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:
```python
import tensorflow as tf
t = tf.constant([1], shape=[1, 1, 1, 1], dtype=tf.quint8)
t_min = tf.constant([], shape=[0], dtype=tf.float32)
t_max = tf.constant([], shape=[0], dtype=tf.float32)
m = tf.constant([1], shape=[1], dtype=tf.quint8)
m_min = tf.constant([], shape=[0], dtype=tf.float32)
m_max = tf.constant([], shape=[0], dtype=tf.float32)
v = tf.constant([1], shape=[1], dtype=tf.quint8)
v_min = tf.constant([], shape=[0], dtype=tf.float32)
v_max = tf.constant([], shape=[0], dtype=tf.float32)
beta = tf.constant([1], shape=[1], dtype=tf.quint8)
beta_min = tf.constant([], shape=[0], dtype=tf.float32)
beta_max = tf.constant([], shape=[0], dtype=tf.float32)
gamma = tf.constant([1], shape=[1], dtype=tf.quint8)
gamma_min = tf.constant([], shape=[0], dtype=tf.float32)
gamma_max = tf.constant([], shape=[0], dtype=tf.float32)
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,
v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,
beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,
gamma_max=gamma_max, out_type=tf.qint32,
variance_epsilon=0.1, scale_after_normalization=True)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty:
```cc
const float input_min = context->input(1).flat<float>()(0);
const float input_max = context->input(2).flat<float>()(0);
...
const float mean_min = context->input(4).flat<float>()(0);
const float mean_max = context->input(5).flat<float>()(0);
...
const float var_min = context->input(7).flat<float>()(0);
const float var_max = context->input(8).flat<float>()(0);
...
const float beta_min = context->input(10).flat<float>()(0);
const float beta_max = context->input(11).flat<float>()(0);
...
const float gamma_min = context->input(13).flat<float>()(0);
const float gamma_max = context->input(14).flat<float>()(0);
```
If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds.
### Patches
We have patched the issue in GitHub commit [d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b](https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29547'} | 2022-03-03T05:14:19.780476Z | 2021-05-21T14:23:31Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29547', 'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} |
PyPI | GHSA-j86v-p27c-73fm | Unitialized access in `EinsumHelper::ParseEquation` | ### Impact
During execution, [`EinsumHelper::ParseEquation()`](https://github.com/tensorflow/tensorflow/blob/e0b6e58c328059829c3eb968136f17aa72b6c876/tensorflow/core/kernels/linalg/einsum_op_impl.h#L126-L181) is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output.
However, the code only changes these flags to `true` and never assigns `false`.
```cc
for (int i = 0; i < num_inputs; ++i) {
input_label_counts->at(i).resize(num_labels);
for (const int label : input_labels->at(i)) {
if (label != kEllipsisLabel)
input_label_counts->at(i)[label] += 1;
else
input_has_ellipsis->at(i) = true;
}
}
output_label_counts->resize(num_labels);
for (const int label : *output_labels) {
if (label != kEllipsisLabel)
output_label_counts->at(label) += 1;
else
*output_has_ellipsis = true;
}
```
This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags.
### Patches
We have patched the issue in GitHub commit [f09caa532b6e1ac8d2aa61b7832c78c5b79300c6](https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-41201'} | 2022-03-03T05:12:12.386786Z | 2021-11-10T19:17:43Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41201', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm'} | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} |
PyPI | GHSA-cfpj-3q4c-jhvr | Division by zero in TFLite | ### Impact
The implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226):
```cc
const int batch_size = input_size / filter->dims->data[1];
```
An attacker can craft a model such that `filter->dims->data[1]` is 0.
### Patches
We have patched the issue in GitHub commit [718721986aa137691ee23f03638867151f74935f](https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. Concurrently, it has also been reported by Yakun Zhang of Baidu Security. | {'CVE-2021-37680'} | 2021-08-24T16:24:24Z | 2021-08-25T14:40:38Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfpj-3q4c-jhvr', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37680', 'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | null | {'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | {'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} |
PyPI | PYSEC-2022-125 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions. 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-9c78-vcq7-7vxq', 'CVE-2022-23561'} | 2022-03-09T00:18:25.786755Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq'} | null | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6'} | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6'} |
PyPI | PYSEC-2021-593 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. 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-2021-37680', 'GHSA-cfpj-3q4c-jhvr'} | 2021-12-09T06:35:05.816233Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfpj-3q4c-jhvr', 'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | null | {'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | {'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} |
PyPI | PYSEC-2021-356 | null | nltk is vulnerable to Inefficient Regular Expression Complexity | {'CVE-2021-3828', 'GHSA-2ww3-fxvq-293j'} | 2021-10-01T22:29:03.465380Z | 2021-09-27T13:15:00Z | null | null | null | {'https://github.com/nltk/nltk/commit/277711ab1dec729e626b27aab6fa35ea5efbd7e6', 'https://huntr.dev/bounties/d19aed43-75bc-4a03-91a0-4d0bb516bc32', 'https://github.com/advisories/GHSA-2ww3-fxvq-293j'} | null | {'https://github.com/nltk/nltk/commit/277711ab1dec729e626b27aab6fa35ea5efbd7e6'} | {'https://github.com/nltk/nltk/commit/277711ab1dec729e626b27aab6fa35ea5efbd7e6'} |
PyPI | PYSEC-2021-706 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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-2021-29580', 'GHSA-x8h6-xgqx-jqgp'} | 2021-12-09T06:35:28.532911Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp'} | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} |
PyPI | PYSEC-2020-213 | null | Tornado before 3.2.2 sends arbitrary responses that contain a fixed CSRF token and may be sent with HTTP compression, which makes it easier for remote attackers to conduct a BREACH attack and determine this token via a series of crafted requests. | {'CVE-2014-9720'} | 2021-07-05T00:01:27.333585Z | 2020-01-24T18:15:00Z | null | null | null | {'http://openwall.com/lists/oss-security/2015/05/19/4', 'http://www.tornadoweb.org/en/stable/releases/v3.2.2.html', 'https://github.com/tornadoweb/tornado/commit/1c36307463b1e8affae100bf9386948e6c1b2308', 'https://bugzilla.redhat.com/show_bug.cgi?id=1222816', 'https://bugzilla.novell.com/show_bug.cgi?id=930362'} | null | {'https://github.com/tornadoweb/tornado/commit/1c36307463b1e8affae100bf9386948e6c1b2308'} | {'https://github.com/tornadoweb/tornado/commit/1c36307463b1e8affae100bf9386948e6c1b2308'} |
PyPI | PYSEC-2017-94 | null | Heap-based buffer overflow in the ALGnew function in block_templace.c in Python Cryptography Toolkit (aka pycrypto) allows remote attackers to execute arbitrary code as demonstrated by a crafted iv parameter to cryptmsg.py. | {'GHSA-cq27-v7xp-c356', 'CVE-2013-7459'} | 2021-08-27T03:22:16.665546Z | 2017-02-15T15:59:00Z | null | null | null | {'https://github.com/dlitz/pycrypto/commit/8dbe0dc3eea5c689d4f76b37b93fe216cf1f00d4', 'https://security.gentoo.org/glsa/201702-14', 'https://pony7.fr/ctf:public:32c3:cryptmsg', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/RJ37R2YLX56YZABFNAOWV4VTHTGYREAE/', 'https://github.com/advisories/GHSA-cq27-v7xp-c356', 'https://bugzilla.redhat.com/show_bug.cgi?id=1409754', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/C6BWNADPLKDBBQBUT3P75W7HAJCE7M3B/', 'https://github.com/dlitz/pycrypto/issues/176', 'http://www.securityfocus.com/bid/95122', 'http://www.openwall.com/lists/oss-security/2016/12/27/8'} | null | {'https://github.com/dlitz/pycrypto/commit/8dbe0dc3eea5c689d4f76b37b93fe216cf1f00d4'} | {'https://github.com/dlitz/pycrypto/commit/8dbe0dc3eea5c689d4f76b37b93fe216cf1f00d4'} |
PyPI | PYSEC-2021-213 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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-7cqx-92hp-x6wh', 'CVE-2021-29576'} | 2021-08-27T03:22:34.891385Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7cqx-92hp-x6wh'} | null | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} |
PyPI | GHSA-7vrm-3jc8-5wwm | Incorrect Comparison in Vyper | ### Impact
bytestrings can have dirty bytes in them, resulting in the word-for-word comparison to give incorrect results, e.g.
```vyper
b1: Bytes[32] = b"abcdef"
b1 = slice(b1, 0, 1)
b2: Bytes[32] = b"abcdef"
t: bool = b1 == b2 # incorrectly evaluates to True
```
even without dirty nonzero bytes, because there is no comparison of the length, two bytestrings can compare to equal if one ends with `"\x00"`.
```vyper
b1: Bytes[32] = b"abc\0"
b2: Bytes[32] = b"abc"
t: bool = b1 == b2 # incorrectly evaluates to True
```
### Patches
fixed in https://github.com/vyperlang/vyper/commit/2c73f8352635c0a433423a5b94740de1a118e508 | null | 2022-04-06T17:50:40.651520Z | 2022-04-04T21:40:45Z | HIGH | null | {'CWE-697'} | {'https://github.com/vyperlang/vyper/commit/2c73f8352635c0a433423a5b94740de1a118e508', 'https://github.com/vyperlang/vyper', 'https://github.com/vyperlang/vyper/security/advisories/GHSA-7vrm-3jc8-5wwm'} | null | {'https://github.com/vyperlang/vyper/commit/2c73f8352635c0a433423a5b94740de1a118e508'} | {'https://github.com/vyperlang/vyper/commit/2c73f8352635c0a433423a5b94740de1a118e508'} |
PyPI | GHSA-j8qh-3xrq-c825 | Division by zero in TFLite's implementation of `OneHot` | ### Impact
The implementation of the `OneHot` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72):
```cc
int prefix_dim_size = 1;
for (int i = 0; i < op_context.axis; ++i) {
prefix_dim_size *= op_context.indices->dims->data[i];
}
const int suffix_dim_size = NumElements(op_context.indices) / prefix_dim_size;
```
An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 0.
### Patches
We have patched the issue in GitHub commit [3ebedd7e345453d68e279cfc3e4072648e5e12e5](https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5).
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-2021-29600'} | 2022-03-03T05:13:17.583435Z | 2021-05-21T14:28:04Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qh-3xrq-c825', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29600'} | null | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5'} | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5'} |
PyPI | PYSEC-2021-643 | null | TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. 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-772p-x54p-hjrv', 'CVE-2021-29517'} | 2021-12-09T06:35:17.852782Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv', 'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f'} | null | {'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f'} | {'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f'} |
PyPI | PYSEC-2021-839 | null | Aim is an open-source, self-hosted machine learning experiment tracking tool. Versions of Aim prior to 3.1.0 are vulnerable to a path traversal attack. By manipulating variables that reference files with “dot-dot-slash (../)� sequences and its variations or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system including application source code or configuration and critical system files. The vulnerability issue is resolved in Aim v3.1.0. | {'GHSA-8phj-f9w2-cjcc', 'CVE-2021-43775'} | 2021-12-13T06:35:02.857370Z | 2021-11-23T21:15:00Z | null | null | null | {'https://github.com/aimhubio/aim/issues/999', 'https://github.com/aimhubio/aim/blob/0b99c6ca08e0ba7e7011453a2f68033e9b1d1bce/aim/web/api/views.py#L9-L16', 'https://github.com/aimhubio/aim/pull/1003/commits/f01266a1a479ef11d7d6c539e7dd89e9d5639738', 'https://github.com/aimhubio/aim/pull/1003', 'https://github.com/aimhubio/aim/security/advisories/GHSA-8phj-f9w2-cjcc'} | null | {'https://github.com/aimhubio/aim/pull/1003/commits/f01266a1a479ef11d7d6c539e7dd89e9d5639738'} | {'https://github.com/aimhubio/aim/pull/1003/commits/f01266a1a479ef11d7d6c539e7dd89e9d5639738'} |
PyPI | PYSEC-2021-585 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. 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-5hj3-vjjf-f5m7', 'CVE-2021-37672'} | 2021-12-09T06:35:05.144273Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7', 'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} |
PyPI | GHSA-4xfp-4pfp-89wg | Reference binding to nullptr in `RaggedTensorToSparse` | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`:
```python
import tensorflow as tf
tf.raw_ops.RaggedTensorToSparse(
rt_nested_splits=[[0, 38, 0]],
rt_dense_values=[])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order.
### Patches
We have patched the issue in GitHub commit [1071f554dbd09f7e101324d366eec5f4fe5a3ece](https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece).
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-2021-37656'} | 2022-03-03T05:12:55.872845Z | 2021-08-25T14:42:55Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37656'} | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} |
PyPI | PYSEC-2020-301 | null | In affected versions of TensorFlow 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. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26270', 'GHSA-m648-33qf-v3gp'} | 2021-12-09T06:34:44.825248Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m648-33qf-v3gp', 'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} | null | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} |
PyPI | PYSEC-2015-11 | null | The get_format function in utils/formats.py in Django before 1.7.x before 1.7.11, 1.8.x before 1.8.7, and 1.9.x before 1.9rc2 might allow remote attackers to obtain sensitive application secrets via a settings key in place of a date/time format setting, as demonstrated by SECRET_KEY. | {'CVE-2015-8213'} | 2021-09-01T08:35:41.190803Z | 2015-12-07T20:59:00Z | null | null | null | {'http://rhn.redhat.com/errata/RHSA-2016-0129.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-December/173375.html', 'http://rhn.redhat.com/errata/RHSA-2016-0158.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-December/174770.html', 'https://www.djangoproject.com/weblog/2015/nov/24/security-releases-issued/', 'http://rhn.redhat.com/errata/RHSA-2016-0157.html', 'http://rhn.redhat.com/errata/RHSA-2016-0156.html', 'http://lists.opensuse.org/opensuse-updates/2015-12/msg00017.html', 'http://www.ubuntu.com/usn/USN-2816-1', 'http://lists.opensuse.org/opensuse-updates/2015-12/msg00014.html', 'https://github.com/django/django/commit/316bc3fc9437c5960c24baceb93c73f1939711e4', 'http://www.securityfocus.com/bid/77750', 'http://www.debian.org/security/2015/dsa-3404', 'http://www.securitytracker.com/id/1034237'} | null | {'https://github.com/django/django/commit/316bc3fc9437c5960c24baceb93c73f1939711e4'} | {'https://github.com/django/django/commit/316bc3fc9437c5960c24baceb93c73f1939711e4'} |
PyPI | PYSEC-2021-751 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version. | {'GHSA-95xm-g58g-3p88', 'CVE-2021-37640'} | 2021-12-09T06:35:35.756075Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-95xm-g58g-3p88', 'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} | null | {'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} | {'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} |
PyPI | PYSEC-2021-301 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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-2021-37679', 'GHSA-g8wg-cjwc-xhhp'} | 2021-08-27T03:22:46.691143Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp', 'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | null | {'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | {'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} |
PyPI | PYSEC-2021-614 | null | TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. 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-786j-5qwq-r36x', 'CVE-2021-41204'} | 2021-12-09T06:35:08.218423Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x', 'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} | null | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} |
PyPI | PYSEC-2021-244 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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-gv26-jpj9-c8gq', 'CVE-2021-29607'} | 2021-08-27T03:22:40.417025Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gq', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a'} | null | {'https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2'} | {'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a'} |
PyPI | GHSA-qcx9-j53g-ccgf | Remote Code Execution via unsafe classes in otherwise permitted modules | ### Impact
The module `AccessControl` defines security policies for Python code used in restricted code within Zope applications. Restricted code is any code that resides in Zope's object database, such as the contents of `Script (Python)` objects.
The policies defined in `AccessControl` severely restrict access to Python modules and only exempt a few that are deemed safe, such as Python's `string` module. However, full access to the `string` module also allows access to the class `Formatter`, which can be overridden and extended within `Script (Python)` in a way that provides access to other unsafe Python libraries. Those unsafe Python libraries can be used for remote code execution.
By default, you need to have the admin-level Zope "Manager" role to add or edit `Script (Python)` objects through the web. Only sites that allow untrusted users to add/edit these scripts through the web - which would be a very unusual configuration to begin with - are at risk.
### Patches
The problem has been fixed in AccessControl 4.3 and 5.2.
Only AccessControl versions 4 and 5 are vulnerable, and only on Python 3, not Python 2.7.
### Workarounds
A site administrator can restrict adding/editing `Script (Python)` objects through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing these scripts through the web should be restricted to trusted users only. This is the default configuration in Zope.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [AccessControl issue tracker](https://github.com/zopefoundation/AccessControl/issues)
* Email us at [security@plone.org](mailto:security@plone.org)
| {'CVE-2021-32807'} | 2022-03-03T05:13:17.404739Z | 2021-08-05T17:01:30Z | MODERATE | null | {'CWE-915'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-32807', 'https://github.com/zopefoundation/AccessControl/blob/master/CHANGES.rst#51-2021-07-30', 'https://github.com/zopefoundation/AccessControl', 'https://github.com/zopefoundation/AccessControl/commit/b42dd4badf803bb9fb71ac34cd9cb0c249262f2c', 'https://github.com/zopefoundation/AccessControl/security/advisories/GHSA-qcx9-j53g-ccgf'} | null | {'https://github.com/zopefoundation/AccessControl/commit/b42dd4badf803bb9fb71ac34cd9cb0c249262f2c'} | {'https://github.com/zopefoundation/AccessControl/commit/b42dd4badf803bb9fb71ac34cd9cb0c249262f2c'} |
PyPI | PYSEC-2021-497 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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-2021-29569', 'GHSA-3h8m-483j-7xxm'} | 2021-12-09T06:34:54.064557Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm', 'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} | null | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} |
PyPI | GHSA-fpjm-rp2g-3r4c | Django Rest Framework jwt allows obtaining new token from notionally invalidated token | An issue was discovered in drf-jwt 1.15.x before 1.15.1. It allows attackers with access to a notionally invalidated token to obtain a new, working token via the refresh endpoint, because the blacklist protection mechanism is incompatible with the token-refresh feature. NOTE: drf-jwt is a fork of jpadilla/django-rest-framework-jwt, which is unmaintained. | {'CVE-2020-10594'} | 2022-03-21T22:31:58.234107Z | 2020-06-05T16:09:34Z | CRITICAL | null | {'CWE-287'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-10594', 'https://github.com/Styria-Digital/django-rest-framework-jwt/commit/868b5c22ddad59772b447080183e7c7101bb18e0', 'https://github.com/Styria-Digital/django-rest-framework-jwt/issues/36', 'https://pypi.org/project/drf-jwt/1.15.1/#history', 'https://github.com/jpadilla/django-rest-framework-jwt/issues/484', 'https://github.com/Styria-Digital/django-rest-framework-jwt'} | null | {'https://github.com/Styria-Digital/django-rest-framework-jwt/commit/868b5c22ddad59772b447080183e7c7101bb18e0'} | {'https://github.com/Styria-Digital/django-rest-framework-jwt/commit/868b5c22ddad59772b447080183e7c7101bb18e0'} |
PyPI | PYSEC-2020-76 | null | Pillow before 7.1.0 has multiple out-of-bounds reads in libImaging/FliDecode.c. | {'GHSA-cqhg-xjhh-p8hf', 'CVE-2020-10177'} | 2020-08-08T20:15:00Z | 2020-06-25T19:15:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging', 'https://lists.debian.org/debian-lts-announce/2020/08/msg00012.html', 'https://github.com/python-pillow/Pillow/pull/4503', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://usn.ubuntu.com/4430-2/', 'https://github.com/advisories/GHSA-cqhg-xjhh-p8hf', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://usn.ubuntu.com/4430-1/', 'https://github.com/python-pillow/Pillow/pull/4538'} | null | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging'} | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging'} |
PyPI | PYSEC-2021-478 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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-f78g-q7r4-9wcv', 'CVE-2021-29550'} | 2021-12-09T06:34:51.099370Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv'} | null | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96'} | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96'} |
PyPI | PYSEC-2021-182 | 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'} | 2021-08-27T03:22:29.446413Z | 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 | GHSA-q263-fvxm-m5mw | Heap out of bounds access in MakeEdge in TensorFlow | ### Impact
Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The [`MakeEdge` function](https://github.com/tensorflow/tensorflow/blob/3616708cb866365301d8e67b43b32b46d94b08a0/tensorflow/core/common_runtime/graph_constructor.cc#L1426-L1438) creates an edge between one output tensor of the `src` node (given by `output_index`) and the input slot of the `dst` node (given by `input_index`). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding `DataType` values and comparing these for equality:
```cc
DataType src_out = src->output_type(output_index);
DataType dst_in = dst->input_type(input_index);
//...
```
However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.
In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.
### Patches
We have patched the issue in GitHub commit [0cc38aaa4064fd9e79101994ce9872c6d91f816b](https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b) 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-2020-26271'} | 2022-03-03T05:14:08.830315Z | 2020-12-10T19:07:34Z | LOW | null | {'CWE-125', 'CWE-908'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw', 'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26271'} | null | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} |
PyPI | PYSEC-2021-395 | null | TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows. 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-2021-41202', 'GHSA-xrqm-fpgr-6hhx'} | 2021-11-13T06:52:42.645758Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46889', 'https://github.com/tensorflow/tensorflow/issues/46912', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} | null | {'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899'} | {'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} |
PyPI | GHSA-48mj-p7x2-5jfm | Basic auth bypass in esphome | ### Impact
Anyone with web_server enabled and HTTP basic auth configured on 2021.9.1 or older
`web_server` allows OTA update without checking user defined basic auth username & password
### Patches
Patch released in 2021.9.2
### Workarounds
Disable/remove `web_server`
| {'CVE-2021-41104'} | 2022-03-03T05:13:27.379334Z | 2021-09-29T17:09:14Z | HIGH | null | {'CWE-306'} | {'https://github.com/esphome/esphome/pull/2409/commits/207cde1667d8c799a197b78ca8a5a14de8d5ca1e', 'https://github.com/esphome/esphome/commit/be965a60eba6bb769e2a5afdbc8eed132f077a59', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41104', 'https://github.com/esphome/esphome/releases/tag/2021.9.2', 'https://github.com/esphome/esphome/security/advisories/GHSA-48mj-p7x2-5jfm', 'https://github.com/esphome/esphome'} | null | {'https://github.com/esphome/esphome/commit/be965a60eba6bb769e2a5afdbc8eed132f077a59', 'https://github.com/esphome/esphome/pull/2409/commits/207cde1667d8c799a197b78ca8a5a14de8d5ca1e'} | {'https://github.com/esphome/esphome/commit/be965a60eba6bb769e2a5afdbc8eed132f077a59', 'https://github.com/esphome/esphome/pull/2409/commits/207cde1667d8c799a197b78ca8a5a14de8d5ca1e'} |
PyPI | PYSEC-2021-680 | 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.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected. | {'GHSA-qg48-85hg-mqc5', 'CVE-2021-29554'} | 2021-12-09T06:35:24.121504Z | 2021-05-14T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5', 'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} | null | {'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} | {'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} |
PyPI | GHSA-rrfp-j2mp-hq9c | Segfault in `tf.quantization.quantize_and_dequantize` | ### Impact
An attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`:
```python
tf.quantization.quantize_and_dequantize(
input=[2.5, 2.5], input_min=[0,0], input_max=[1,1], axis=10)
```
This results in accessing [a dimension outside the rank of the input tensor](https://github.com/tensorflow/tensorflow/blob/0225022b725993bfc19b87a02a2faaad9a53bc17/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74) in the C++ kernel implementation:
```
const int depth = (axis_ == -1) ? 1 : input.dim_size(axis_);
```
However, [`dim_size` only does a `DCHECK`](https://github.com/tensorflow/tensorflow/blob/0225022b725993bfc19b87a02a2faaad9a53bc17/tensorflow/core/framework/tensor_shape.cc#L292-L307) to validate the argument and then uses it to access the corresponding element of an array:
```
int64 TensorShapeBase<Shape>::dim_size(int d) const {
DCHECK_GE(d, 0);
DCHECK_LT(d, dims());
DoStuffWith(dims_[d]);
}
```
Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array.
### Patches
We have patched the issue in eccb7ec454e6617738554a255d77f08e60ee0808 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
### 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 in #42105 | {'CVE-2020-15265'} | 2022-03-03T05:13:37.229593Z | 2020-11-13T17:13:04Z | LOW | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15265', 'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/issues/42105', '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 | GHSA-c545-c4f9-rf6v | Heap OOB in TFLite | ### Impact
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:
```cc
if (axis < 0) {
axis = input_dims.size + 1 + axis;
}
TF_LITE_ENSURE(context, axis <= input_dims.size);
TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_dims.size + 1);
for (int i = 0; i < output_dims->size; ++i) {
if (i < axis) {
output_dims->data[i] = input_dims.data[i];
} else if (i == axis) {
output_dims->data[i] = 1;
} else {
output_dims->data[i] = input_dims.data[i - 1];
}
}
```
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`).
### Patches
We have patched the issue in GitHub commit [d94ffe08a65400f898241c0374e9edc6fa8ed257](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37685'} | 2021-08-24T17:15:34Z | 2021-08-25T14:40:09Z | MODERATE | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c545-c4f9-rf6v', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37685'} | null | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} |
PyPI | PYSEC-2021-403 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected. | {'CVE-2021-41211', 'GHSA-cvgx-3v3q-m36c'} | 2021-11-13T06:52:43.843277Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c'} | null | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244'} | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244'} |
PyPI | GHSA-9c84-4hx6-xmm4 | Integer overflow in TFLite concatentation | ### Impact
The TFLite implementation of concatenation is [vulnerable to an integer overflow issue](https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76):
```cc
for (int d = 0; d < t0->dims->size; ++d) {
if (d == axis) {
sum_axis += t->dims->data[axis];
} else {
TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims->data[d]);
}
}
```
An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format.
### Patches
We have patched the issue in GitHub commit [4253f96a58486ffe84b61c0415bb234a4632ee73](https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73).
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-2021-29601'} | 2022-03-03T05:13:56.316972Z | 2021-05-21T14:28:08Z | MODERATE | null | {'CWE-190'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29601', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c84-4hx6-xmm4', 'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73'} | null | {'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73'} | {'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73'} |
PyPI | PYSEC-2021-116 | null | 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', 'SNYK-PYTHON-BIKESHED-1537646', 'GHSA-87cj-px37-rc3x'} | 2021-08-16T10:33:00.121184Z | 2021-08-16T08:15:00Z | null | null | null | {'https://snyk.io/vuln/SNYK-PYTHON-BIKESHED-1537646', 'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd', 'https://github.com/advisories/GHSA-87cj-px37-rc3x'} | null | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} |
PyPI | PYSEC-2021-546 | null | TensorFlow is an end-to-end open source platform for machine learning. Passing a complex argument to `tf.transpose` at the same time as passing `conjugate=True` argument results in a crash. 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-xqfj-cr6q-pc8w', 'CVE-2021-29618'} | 2021-12-09T06:35:01.733982Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/issues/46973', 'https://github.com/tensorflow/tensorflow/commit/1dc6a7ce6e0b3e27a7ae650bfc05b195ca793f88', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xqfj-cr6q-pc8w', 'https://github.com/tensorflow/issues/42105'} | null | {'https://github.com/tensorflow/tensorflow/commit/1dc6a7ce6e0b3e27a7ae650bfc05b195ca793f88'} | {'https://github.com/tensorflow/tensorflow/commit/1dc6a7ce6e0b3e27a7ae650bfc05b195ca793f88'} |
PyPI | PYSEC-2020-116 | null | In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15193', 'GHSA-rjjg-hgv6-h69v'} | 2021-09-01T08:19:32.562362Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | PYSEC-2021-815 | null | TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. 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-57wx-m983-2f88', 'CVE-2021-41208'} | 2021-12-09T06:35:42.346240Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88', 'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} | null | {'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} | {'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} |
PyPI | GHSA-fcf9-3qw3-gxmj | High severity vulnerability that affects cryptography | A flaw was found in python-cryptography versions between >=1.9.0 and <2.3. The finalize_with_tag API did not enforce a minimum tag length. If a user did not validate the input length prior to passing it to finalize_with_tag an attacker could craft an invalid payload with a shortened tag (e.g. 1 byte) such that they would have a 1 in 256 chance of passing the MAC check. GCM tag forgeries can cause key leakage. | {'CVE-2018-10903'} | 2022-03-07T20:47:10.911751Z | 2018-07-31T18:28:09Z | HIGH | null | {'CWE-20'} | {'https://github.com/pyca/cryptography/pull/4342/commits/688e0f673bfbf43fa898994326c6877f00ab19ef', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2018-10903', 'https://nvd.nist.gov/vuln/detail/CVE-2018-10903', 'https://github.com/advisories/GHSA-fcf9-3qw3-gxmj'} | null | {'https://github.com/pyca/cryptography/pull/4342/commits/688e0f673bfbf43fa898994326c6877f00ab19ef'} | {'https://github.com/pyca/cryptography/pull/4342/commits/688e0f673bfbf43fa898994326c6877f00ab19ef'} |
PyPI | GHSA-mhhc-q96p-mfm9 | Infinite loop in TFLite | ### Impact
The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122):
```cc
for (int i = 0; i < effective_dims;) {
if ((1 << i) & op_context->params->ellipsis_mask) {
// ...
int ellipsis_end_idx =
std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,
effective_dims);
// ...
for (; i < ellipsis_end_idx; ++i) {
// ...
}
continue;
}
// ...
++i;
}
```
An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop.
### Patches
We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695).
The fix will be included in TensorFlow 2.6.0. This is the only affected version.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37686'} | 2022-03-03T05:13:58.855686Z | 2021-08-25T14:39:58Z | MODERATE | null | {'CWE-835'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mhhc-q96p-mfm9', 'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.4', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37686', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.5.1', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.4.3', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.6.0'} | null | {'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} | {'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} |
PyPI | PYSEC-2021-268 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37646', 'GHSA-h6jh-7gv5-28vg'} | 2021-08-27T03:22:43.623027Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6jh-7gv5-28vg'} | null | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} |
PyPI | PYSEC-2021-792 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF in TFLite is [vulnerable to a null pointer error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The [`GetVariableInput` function](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but `GetTensorData` assumes that the argument is always a valid tensor. Furthermore, because `GetVariableInput` calls [`GetMutableInput`](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return `nullptr`, the `tensor->is_variable` expression can also trigger a null pointer exception. We have patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. 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-2021-37681', 'GHSA-7xwj-5r4v-429p'} | 2021-12-09T06:35:39.432731Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7xwj-5r4v-429p'} | null | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76'} | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76'} |
PyPI | PYSEC-2021-638 | null | TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected. | {'GHSA-4278-2v5v-65r4', 'CVE-2021-29512'} | 2021-12-09T06:35:17.036607Z | 2021-05-14T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | GHSA-4f7p-27jc-3c36 | HTTP Request Smuggling in waitress | ### Impact
When using Waitress behind a proxy that does not properly validate the incoming HTTP request matches the RFC7230 standard, Waitress and the frontend proxy may disagree on where one request starts and where it ends.
This would allow requests to be smuggled via the front-end proxy to waitress and later behavior.
There are two classes of vulnerability that may lead to request smuggling that are addressed by this advisory:
- The use of Python's `int()` to parse strings into integers, leading to `+10` to be parsed as `10`, or `0x01` to be parsed as `1`, where as the standard specifies that the string should contain only digits or hex digits.
- Waitress does not support chunk extensions, however it was discarding them without validating that they did not contain illegal characters
### Patches
This has been fixed in Waitress 2.1.1
### Workarounds
When deploying a proxy in front of waitress, turning on any and all functionality to make sure that the request matches the RFC7230 standard. Certain proxy servers may not have this functionality though and users are encouraged to upgrade to the latest version of waitress instead.
### References
- https://portswigger.net/research/http-desync-attacks-request-smuggling-reborn
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the Github issue tracker](https://github.com/Pylons/waitress/issues) (if not security related/sensitive)
* Email us at [pylons-project-security@googlegroups.com](mailto:pylons-project-security@googlegroups.com) (If security related or sensitive)
| {'CVE-2022-24761'} | 2022-03-18T19:18:30.184688Z | 2022-03-18T19:00:59Z | HIGH | null | {'CWE-444'} | {'https://github.com/Pylons/waitress', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24761', 'https://github.com/Pylons/waitress/commit/9e0b8c801e4d505c2ffc91b891af4ba48af715e0', 'https://github.com/Pylons/waitress/security/advisories/GHSA-4f7p-27jc-3c36', 'https://github.com/Pylons/waitress/releases/tag/v2.1.1'} | null | {'https://github.com/Pylons/waitress/commit/9e0b8c801e4d505c2ffc91b891af4ba48af715e0'} | {'https://github.com/Pylons/waitress/commit/9e0b8c801e4d505c2ffc91b891af4ba48af715e0'} |
PyPI | PYSEC-2021-287 | 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'} | 2021-08-27T03:22:45.390087Z | 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-2020-287 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15207', 'GHSA-q4qf-3fc6-8x34'} | 2021-12-09T06:34:42.836592Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/2d88f470dea2671b430884260f3626b1fe99830a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34', '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/2d88f470dea2671b430884260f3626b1fe99830a'} | {'https://github.com/tensorflow/tensorflow/commit/2d88f470dea2671b430884260f3626b1fe99830a'} |
PyPI | PYSEC-2021-454 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. 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. 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-4vf2-4xcg-65cx', 'CVE-2021-29526'} | 2021-12-09T06:34:47.426864Z | 2021-05-14T20:15:00Z | null | null | null | {'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-2022-148 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a use after free behavior when decoding PNG images. After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state. 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-24x4-6qmh-88qg', 'CVE-2022-23584'} | 2022-03-09T00:18:28.987872Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-24x4-6qmh-88qg', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L339-L346', 'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b'} | null | {'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b'} | {'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b'} |
PyPI | PYSEC-2022-67 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in `TfLiteIntArrayCreate`. The `TfLiteIntArrayGetSizeInBytes` returns an `int` instead of a `size_t. An attacker can control model inputs such that `computed_size` overflows the size of `int` datatype. 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-2022-23558', 'GHSA-9gwq-6cwj-47h3'} | 2022-03-09T00:17:32.167293Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3', 'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33'} | null | {'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091'} | {'https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091'} |
PyPI | PYSEC-2022-88 | 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'} | 2022-03-09T00:17:34.776924Z | 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 | PYSEC-2018-33 | null | __init__.py in f2py in NumPy before 1.8.1 allows local users to write to arbitrary files via a symlink attack on a temporary file. | {'CVE-2014-1858'} | 2021-06-29T22:52:17.794688Z | 2018-01-08T19:29:00Z | null | null | null | {'https://github.com/numpy/numpy/blob/maintenance/1.8.x/doc/release/1.8.1-notes.rst', 'http://www.openwall.com/lists/oss-security/2014/02/08/3', 'http://lists.fedoraproject.org/pipermail/package-announce/2014-February/128358.html', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=737778', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/91318', 'http://lists.fedoraproject.org/pipermail/package-announce/2014-February/128781.html', 'https://github.com/numpy/numpy/commit/0bb46c1448b0d3f5453d5182a17ea7ac5854ee15', 'https://bugzilla.redhat.com/show_bug.cgi?id=1062009', 'https://github.com/numpy/numpy/pull/4262', 'http://www.securityfocus.com/bid/65441'} | null | {'https://github.com/numpy/numpy/commit/0bb46c1448b0d3f5453d5182a17ea7ac5854ee15'} | {'https://github.com/numpy/numpy/commit/0bb46c1448b0d3f5453d5182a17ea7ac5854ee15'} |
PyPI | PYSEC-2021-842 | null | TensorFlow is an open source platform for machine learning. In affected versions 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. This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using `AddDim`. 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`. 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-cq76-mxrc-vchh', 'CVE-2021-41195'} | 2021-12-13T06:21:24.676324Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46888', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | null | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} |
PyPI | PYSEC-2020-141 | null | In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched. | {'GHSA-9jjw-hf72-3mxw', 'CVE-2020-26269'} | 2020-12-14T17:42:00Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c'} | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c'} |
PyPI | GHSA-2ghc-6v89-pw9j | Prototype Pollution in body-parser-xml | body-parser-xml is vulnerable to Improperly Controlled Modification of Object Prototype Attributes ('Prototype Pollution') | {'CVE-2021-3666'} | 2021-09-14T18:39:31Z | 2021-09-14T20:25:35Z | HIGH | null | {'CWE-1321', 'CWE-915'} | {'https://github.com/fiznool/body-parser-xml', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3666', 'https://huntr.dev/bounties/1-other-fiznool/body-parser-xml', 'https://github.com/fiznool/body-parser-xml/commit/d46ca622560f7c9a033cd9321c61e92558150d63'} | null | {'https://github.com/fiznool/body-parser-xml/commit/d46ca622560f7c9a033cd9321c61e92558150d63'} | {'https://github.com/fiznool/body-parser-xml/commit/d46ca622560f7c9a033cd9321c61e92558150d63'} |
PyPI | PYSEC-2021-511 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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-9xh4-23q4-v6wr', 'CVE-2021-29583'} | 2021-12-09T06:34:56.228145Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr', 'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} | null | {'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} | {'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} |
PyPI | PYSEC-2014-10 | null | PIL/IcnsImagePlugin.py in Python Imaging Library (PIL) and Pillow before 2.3.2 and 2.5.x before 2.5.2 allows remote attackers to cause a denial of service via a crafted block size. | {'CVE-2014-3589'} | 2021-07-05T00:01:23.718339Z | 2014-08-25T14:55:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commit/205e056f8f9b06ed7b925cf8aa0874bc4aaf8a7d', 'https://pypi.python.org/pypi/Pillow/2.5.2', 'https://pypi.python.org/pypi/Pillow/2.3.2', 'http://lists.opensuse.org/opensuse-updates/2015-04/msg00056.html', 'http://secunia.com/advisories/59825', 'http://www.debian.org/security/2014/dsa-3009'} | null | {'https://github.com/python-pillow/Pillow/commit/205e056f8f9b06ed7b925cf8aa0874bc4aaf8a7d'} | {'https://github.com/python-pillow/Pillow/commit/205e056f8f9b06ed7b925cf8aa0874bc4aaf8a7d'} |
PyPI | PYSEC-2021-141 | null | In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service. | {'GHSA-pq64-v7f5-gqh8', 'CVE-2021-27291'} | 2021-08-27T03:22:17.331175Z | 2021-03-17T13:15:00Z | null | null | null | {'https://github.com/pygments/pygments/commit/2e7e8c4a7b318f4032493773732754e418279a14', 'https://www.debian.org/security/2021/dsa-4889', 'https://lists.debian.org/debian-lts-announce/2021/05/msg00003.html', 'https://github.com/advisories/GHSA-pq64-v7f5-gqh8', 'https://www.debian.org/security/2021/dsa-4878', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GSJRFHALQ7E3UV4FFMFU2YQ6LUDHAI55/', 'https://lists.debian.org/debian-lts-announce/2021/03/msg00024.html', 'https://gist.github.com/b-c-ds/b1a2cc0c68a35c57188575eb496de5ce', 'https://lists.debian.org/debian-lts-announce/2021/05/msg00006.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WSLD67LFGXOX2K5YNESSWAS4AGZIJTUQ/'} | null | {'https://github.com/pygments/pygments/commit/2e7e8c4a7b318f4032493773732754e418279a14'} | {'https://github.com/pygments/pygments/commit/2e7e8c4a7b318f4032493773732754e418279a14'} |
PyPI | GHSA-qfc5-mcwq-26q8 | Double Free in psutil | psutil (aka python-psutil) through 5.6.5 can have a double free. This occurs because of refcount mishandling within a while or for loop that converts system data into a Python object. | {'CVE-2019-18874'} | 2022-03-03T05:13:57.772669Z | 2020-03-12T17:02:50Z | HIGH | null | {'CWE-415'} | {'https://github.com/giampaolo/psutil/pull/1616', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/OLETTJYZL2SMBUI4Q2NGBMGPDPP54SRG/', 'https://lists.debian.org/debian-lts-announce/2019/11/msg00018.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2P7QI7MOTZTFXQYU23CP3RAWXCERMOAS/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-18874', 'https://usn.ubuntu.com/4204-1/', 'https://github.com/giampaolo/psutil/commit/7d512c8e4442a896d56505be3e78f1156f443465', 'https://github.com/giampaolo/psutil/blob/master/HISTORY.rst#566'} | null | {'https://github.com/giampaolo/psutil/commit/7d512c8e4442a896d56505be3e78f1156f443465'} | {'https://github.com/giampaolo/psutil/commit/7d512c8e4442a896d56505be3e78f1156f443465'} |
PyPI | PYSEC-2021-61 | null | clickhouse-driver before 0.1.5 allows a malicious clickhouse server to trigger a crash or execute arbitrary code (on a database client) via a crafted server response, due to a buffer overflow. | {'GHSA-vgv5-cxvh-vfxh', 'CVE-2020-26759'} | 2021-01-08T21:19:00Z | 2021-01-06T13:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-vgv5-cxvh-vfxh', 'https://github.com/mymarilyn/clickhouse-driver/commit/3e990547e064b8fca916b23a0f7d6fe8c63c7f6b', 'https://github.com/mymarilyn/clickhouse-driver/commit/d708ed548e1d6f254ba81a21de8ba543a53b5598'} | null | {'https://github.com/mymarilyn/clickhouse-driver/commit/3e990547e064b8fca916b23a0f7d6fe8c63c7f6b', 'https://github.com/mymarilyn/clickhouse-driver/commit/d708ed548e1d6f254ba81a21de8ba543a53b5598'} | {'https://github.com/mymarilyn/clickhouse-driver/commit/d708ed548e1d6f254ba81a21de8ba543a53b5598', 'https://github.com/mymarilyn/clickhouse-driver/commit/3e990547e064b8fca916b23a0f7d6fe8c63c7f6b'} |
PyPI | PYSEC-2021-784 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. 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-2021-37673', 'GHSA-278g-rq84-9hmg'} | 2021-12-09T06:35:38.706561Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg'} | null | {'https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d'} | {'https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d'} |
PyPI | GHSA-hggm-jpg3-v476 | RSA decryption vulnerable to Bleichenbacher timing vulnerability | ### Impact
RSA decryption was vulnerable to Bleichenbacher timing vulnerabilities, which would impact people using RSA decryption in online scenarios.
### Patches
This is fixed in cryptography 3.2. https://github.com/pyca/cryptography/commit/58494b41d6ecb0f56b7c5f05d5f5e3ca0320d494 is the resolving commit. | {'CVE-2020-25659'} | 2022-04-25T21:46:57.889394Z | 2020-10-27T20:33:13Z | MODERATE | null | {'CWE-385'} | {'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://github.com/pyca/cryptography/commit/58494b41d6ecb0f56b7c5f05d5f5e3ca0320d494', 'https://github.com/pyca/cryptography', 'https://github.com/pyca/cryptography/pull/5507/commits/ce1bef6f1ee06ac497ca0c837fbd1c7ef6c2472b', 'https://nvd.nist.gov/vuln/detail/CVE-2020-25659', 'https://github.com/pyca/cryptography/security/advisories/GHSA-hggm-jpg3-v476', 'https://pypi.org/project/cryptography/'} | null | {'https://github.com/pyca/cryptography/pull/5507/commits/ce1bef6f1ee06ac497ca0c837fbd1c7ef6c2472b', 'https://github.com/pyca/cryptography/commit/58494b41d6ecb0f56b7c5f05d5f5e3ca0320d494'} | {'https://github.com/pyca/cryptography/commit/58494b41d6ecb0f56b7c5f05d5f5e3ca0320d494', 'https://github.com/pyca/cryptography/pull/5507/commits/ce1bef6f1ee06ac497ca0c837fbd1c7ef6c2472b'} |
PyPI | PYSEC-2020-291 | 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'} | 2021-12-09T06:34:43.650264Z | 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 | GHSA-jwf9-w5xm-f437 | Heap OOB in TFLite's `Gather*` implementations | ### Impact
TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation.
Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`.
Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc).
```python
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_v2_behavior()
params = tf.compat.v1.placeholder(name="params", dtype=tf.int64, shape=(1,))
indices = tf.compat.v1.placeholder(name="indices", dtype=tf.int64, shape=())
out = tf.gather(params, indices, name='out')
with tf.compat.v1.Session() as sess:
converter = tf.compat.v1.lite.TFLiteConverter.from_session(sess, [params, indices], [out])
tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
params_data = np.reshape(np.array([1], dtype=np.int64), newshape=(1,))
indices_data = np.reshape(np.array(-10, dtype=np.int64), newshape=())
interpreter.set_tensor(input_details[0]['index'], params_data)
interpreter.set_tensor(input_details[1]['index'], indices_data)
interpreter.invoke()
```
### Patches
We have patched the issue in GitHub commits [bb6a0383ed553c286f87ca88c207f6774d5c4a8f](https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f) and [eb921122119a6b6e470ee98b89e65d721663179d](https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37687'} | 2021-08-24T17:49:13Z | 2021-08-25T14:40:02Z | MODERATE | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37687', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jwf9-w5xm-f437', 'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d', 'https://github.com/tensorflow/tensorflow'} | null | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} |
PyPI | GHSA-9rpc-5v9q-5r7f | Incomplete validation in `SparseReshape` | ### Impact
Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure.
```python
import tensorflow as tf
input_indices = tf.constant(41, shape=[1, 1], dtype=tf.int64)
input_shape = tf.zeros([11], dtype=tf.int64)
new_shape = tf.zeros([1], dtype=tf.int64)
tf.raw_ops.SparseReshape(input_indices=input_indices,
input_shape=input_shape,
new_shape=new_shape)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments specify a valid sparse tensor.
### Patches
We have patched the issue in GitHub commit [1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6](https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are the only affected versions.
### 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-2021-29611'} | 2022-03-03T05:14:19.417863Z | 2021-05-21T14:28:35Z | LOW | null | {'CWE-20', 'CWE-665'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9rpc-5v9q-5r7f', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29611', 'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} | null | {'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} | {'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} |
PyPI | PYSEC-2021-291 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. 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`. 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 unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and 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. | {'GHSA-vmjw-c2vp-p33c', 'CVE-2021-37669'} | 2021-08-27T03:22:45.759545Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c', 'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58', 'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d'} | 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 | GHSA-9xh4-23q4-v6wr | Heap buffer overflow and undefined behavior in `FusedBatchNorm` | ### Impact
The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean, variance=variance,
epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
```
If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:
```python
import tensorflow as tf
import numpy as np
x = tf.zeros([10, 10, 10, 1], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean, variance=variance,
epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.
If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.
### Patches
We have patched the issue in GitHub commit [6972f9dfe325636b3db4e0bc517ee22a159365c0](https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0).
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-2021-29583'} | 2022-04-26T18:17:19.393448Z | 2021-05-21T14:26:35Z | LOW | null | {'CWE-476', 'CWE-787', 'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr', 'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29583'} | null | {'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} | {'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} |
PyPI | PYSEC-2022-71 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `Range` suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations. 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-qx3f-p745-w4hr', 'CVE-2022-23562'} | 2022-03-09T00:17:32.679005Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/52676', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr', 'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} | null | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} |
PyPI | GHSA-7fcj-pq9j-wh2r | Local Privilege Escalation in PyInstaller | ### Impact
Local Privilege Escalation in all Windows software frozen by PyInstaller in "onefile" mode.
The vulnerability is present only on Windows and in this particular case: If a **software frozen by PyInstaller in "onefile" mode** is launched by a (privileged) user who has **his/her "TempPath" resolving to a world writable directory**. This is the case e.g. if the software is launched as a service or as a scheduled task using a system account (in which case TempPath will default to C:\Windows\Temp).
In order to be exploitable the software has to be (re)started after the attacker has launched the exploit program. So for a service launched at startup, a service restart is needed (e.g. after a crash or an upgrade).
While PyInstaller itself was not vulnerable, all Windows software frozen by PyInstaller in "onefile" mode is vulnerable.
CVSSv3 score 7.0 (High)
CVSSv3 vector CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H
Affected
- all Windows software frozen by PyInstaller in "onefile" mode
No affected
- PyInstaller itself (except if frozen by PyInstaller in "onefile" mode on Windows)
- software frozen in "one*dir*" mode
- other platforms (GNU/Linux, OS X, BSD, etc.)
### Patches
The problem is patched in commits 42a67148b3bdf9211fda8499fdc5b63acdd7e6cc (fixed code) and be948cf0954707671aa499da17b10c86b6fa5e5c (recompiled bootloaders). Users should upgrade to PyInstaller version 3.6 and rebuild their software.
### Workarounds
There is no known workaround. Users using PyInstaller to freeze their Windows software using "onefile" mode should upgrade PyInstaller and rebuild their software.
### Credits
This vulnerability was discovered and reported by Farid AYOUJIL (@faridtsl), David HA, Florent LE NIGER and Yann GASCUEL (@lnv42) from Alter Solutions (@AlterSolutions) and fixed in collaboration with
Hartmut Goebel (@htgoebel, maintainer of PyInstaller).
### Funding Development
PyInstaller is in urgent need of funding to make future security fixes happen, see <https://github.com/pyinstaller/pyinstaller/issues/4404> for details. | {'CVE-2019-16784'} | 2022-03-03T05:13:02.928204Z | 2020-01-16T22:18:27Z | HIGH | null | {'CWE-250'} | {'https://github.com/pyinstaller/pyinstaller/commit/42a67148b3bdf9211fda8499fdc5b63acdd7e6cc', 'https://github.com/pyinstaller/pyinstaller/security/advisories/GHSA-7fcj-pq9j-wh2r', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16784', 'https://github.com/pyinstaller/pyinstaller/commit/be948cf0954707671aa499da17b10c86b6fa5e5c'} | null | {'https://github.com/pyinstaller/pyinstaller/commit/42a67148b3bdf9211fda8499fdc5b63acdd7e6cc', 'https://github.com/pyinstaller/pyinstaller/commit/be948cf0954707671aa499da17b10c86b6fa5e5c'} | {'https://github.com/pyinstaller/pyinstaller/commit/be948cf0954707671aa499da17b10c86b6fa5e5c', 'https://github.com/pyinstaller/pyinstaller/commit/42a67148b3bdf9211fda8499fdc5b63acdd7e6cc'} |
PyPI | PYSEC-2021-442 | null | TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected. | {'CVE-2021-29514', 'GHSA-8h46-5m9h-7553'} | 2021-12-09T06:34:45.524725Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8h46-5m9h-7553'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | PYSEC-2021-157 | null | TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29520', 'GHSA-wcv5-qrj6-9pfm'} | 2021-08-27T03:22:24.934633Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-qrj6-9pfm', 'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | null | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} |
PyPI | PYSEC-2021-507 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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-79fv-9865-4qcv', 'CVE-2021-29579'} | 2021-12-09T06:34:55.622531Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79fv-9865-4qcv', 'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} | null | {'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} | {'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} |
PyPI | PYSEC-2020-229 | null | django-nopassword before 5.0.0 stores cleartext secrets in the database. | {'GHSA-37cf-r3w2-gjfw', 'CVE-2019-10682'} | 2021-08-27T03:21:57.541967Z | 2020-03-18T15:15:00Z | null | null | null | {'https://github.com/relekang/django-nopassword/commit/d8b4615f5fbfe3997d96cf4cb3e342406396193c', 'https://github.com/relekang/django-nopassword/blob/8e8cfc765ee00adfed120c2c79bf71ef856e9022/nopassword/models.py#L14', 'https://github.com/advisories/GHSA-37cf-r3w2-gjfw', 'https://github.com/relekang/django-nopassword/compare/v4.0.1...v5.0.0'} | null | {'https://github.com/relekang/django-nopassword/commit/d8b4615f5fbfe3997d96cf4cb3e342406396193c'} | {'https://github.com/relekang/django-nopassword/commit/d8b4615f5fbfe3997d96cf4cb3e342406396193c'} |
PyPI | PYSEC-2021-229 | 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'} | 2021-08-27T03:22:37.768858Z | 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-2021-679 | 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'} | 2021-12-09T06:35:23.959666Z | 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-2021-696 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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-2021-29570', 'GHSA-545v-42p7-98fq'} | 2021-12-09T06:35:26.840571Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-545v-42p7-98fq', 'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | null | {'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | {'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} |
PyPI | GHSA-mw6v-crh8-8533 | Integer Overflow or Wraparound in Google TensorFlow | Google TensorFlow 1.7.x and earlier is affected by a Buffer Overflow vulnerability. The type of exploitation is context-dependent. | {'CVE-2018-7575'} | 2022-03-23T22:30:08.476235Z | 2019-04-30T15:36:41Z | CRITICAL | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/d107fee1e4a9a4462f01564798d345802acc2aef', 'https://nvd.nist.gov/vuln/detail/CVE-2018-7575', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-004.md'} | null | {'https://github.com/tensorflow/tensorflow/commit/d107fee1e4a9a4462f01564798d345802acc2aef'} | {'https://github.com/tensorflow/tensorflow/commit/d107fee1e4a9a4462f01564798d345802acc2aef'} |
PyPI | GHSA-g4gq-j4p2-j8fr | Remote Code Execution via Script (Python) objects under Python 3 | ### Impact
Background: The optional add-on package `Products.PythonScripts` adds `Script (Python)` to the list of content items a user can add to the Zope object database. Inside these scripts users can write Python code that is executed when rendered through the web. The code environment in these script objects is limited, it relies on the `RestrictedPython` package to provide a "safe" subset of Python instructions as well as the `AccessControl` package that defines security policies for execution in the context of a Zope application.
Recently the `AccessControl` package was updated to fix a remote code execution security issue. A link to the security advisory is provided in the References section below. The bug tightens the `AccessControl` security policies for Zope by blocking access to unsafe classes inside the Python `string` module.
You are only affected if the following are true:
- You use Python 3 for your Zope deployment (Zope 4 on Python 2 is not affected)
- You run Zope 4 below version 4.6.3 or Zope 5 below version 5.3
- You have installed the optional `Products.PythonScripts` add-on package
By default, you need to have the admin-level Zope "Manager" role to add or edit Script (Python) objects through the web. Only sites that allow untrusted users to add/edit these scripts through the web - which would be a very unusual configuration to begin with - are at risk.
### Patches
The problem has been fixed in `AccessControl` versions 4.3 and 5.2. Zope releases 4.6.3 and 5.3 now require these new `AccessControl` releases.
### Workarounds
A site administrator can restrict adding/editing Script (Python) objects through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing these scripts through the web should be restricted to trusted users only. This is the default configuration in Zope.
### References
* [AccessControl security advisory GHSA-qcx9-j53g-ccgf](https://github.com/zopefoundation/AccessControl/security/advisories/GHSA-qcx9-j53g-ccgf)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Zope issue tracker](https://github.com/zopefoundation/Zope/issues)
* Email us at [security@plone.org](mailto:security@plone.org)
| {'CVE-2021-32811'} | 2022-03-03T05:12:44.272513Z | 2021-08-05T17:00:37Z | HIGH | null | {'CWE-915'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-32811', 'https://github.com/zopefoundation/Zope', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-g4gq-j4p2-j8fr', 'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988', 'https://github.com/zopefoundation/AccessControl/security/advisories/GHSA-qcx9-j53g-ccgf'} | null | {'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988'} | {'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988'} |
PyPI | GHSA-94jq-q5v2-76wj | Improper certificate management in AWS IoT Device SDK v2 | Connections initialized by the AWS IoT Device SDK v2 for Java (versions prior to 1.3.3), Python (versions prior to 1.5.18), C++ (versions prior to 1.12.7) and Node.js (versions prior to 1.5.1) did not verify server certificate hostname during TLS handshake when overriding Certificate Authorities (CA) in their trust stores on Windows. This issue has been addressed in aws-c-io submodule versions 0.9.13 onward. This issue affects: Amazon Web Services AWS IoT Device SDK v2 for Java versions prior to 1.3.3 on Microsoft Windows. Amazon Web Services AWS IoT Device SDK v2 for Python versions prior to 1.5.18 on Microsoft Windows. Amazon Web Services AWS IoT Device SDK v2 for C++ versions prior to 1.12.7 on Microsoft Windows. Amazon Web Services AWS IoT Device SDK v2 for Node.js versions prior to 1.5.3 on Microsoft Windows. | {'CVE-2021-40828'} | 2022-03-03T05:13:50.572827Z | 2021-11-24T21:02:24Z | MODERATE | null | {'CWE-295'} | {'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/fd4c0ba04b35eab9e20c635af5548fcc5a92d8be', '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', 'https://github.com/aws/aws-iot-device-sdk-java-v2', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/4be41394f1aee979e6f4b012fcb01eecabd0c08d', 'https://nvd.nist.gov/vuln/detail/CVE-2021-40828'} | null | {'https://github.com/aws/aws-iot-device-sdk-java-v2/commit/67950ad2a02f2f9355c310b69dc9226b017f32f2', 'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/fd4c0ba04b35eab9e20c635af5548fcc5a92d8be', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/4be41394f1aee979e6f4b012fcb01eecabd0c08d'} | {'https://github.com/aws/aws-iot-device-sdk-java-v2/commit/67950ad2a02f2f9355c310b69dc9226b017f32f2', 'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/fd4c0ba04b35eab9e20c635af5548fcc5a92d8be', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/4be41394f1aee979e6f4b012fcb01eecabd0c08d'} |
PyPI | PYSEC-2021-415 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41223', 'GHSA-f54p-f6jp-4rhr'} | 2021-11-13T06:52:45.621437Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f54p-f6jp-4rhr'} | null | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda'} | {'https://github.com/tensorflow/tensorflow/commit/aab9998916c2ffbd8f0592059fad352622f89cda'} |
PyPI | PYSEC-2022-109 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalAvgPoolGrad` does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-vjg4-v33c-ggc4', 'CVE-2022-21730'} | 2022-03-09T00:18:23.671699Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360', 'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} | null | {'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} | {'https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9'} |
PyPI | GHSA-j8c8-67vp-6mx7 | Arbitrary memory read in `ImmutableConst` | ### Impact
The `ImmutableConst` operation in TensorFlow can be tricked into reading arbitrary memory contents:
```python
import tensorflow as tf
with open('/tmp/test','wb') as f:
f.write(b'\xe2'*128)
data = tf.raw_ops.ImmutableConst(dtype=tf.string,shape=3,memory_region_name='/tmp/test')
print(data)
```
This is because the `tstring` TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype.
### Patches
We have patched the issue in GitHub commit [3712a2d3455e6ccb924daa5724a3652a86f6b585](https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585) and GitHub commit [1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b](https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b).
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-2021-41227'} | 2022-03-03T05:12:51.029397Z | 2021-11-10T18:34:49Z | MODERATE | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41227', 'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7', 'https://github.com/tensorflow/tensorflow'} | null | {'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b', 'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585'} | {'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b'} |
PyPI | GHSA-6gmv-pjp9-p8w8 | Out of bounds read in Tensorflow | ### Impact
The [implementation of shape inference for `ReverseSequence`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L1636-L1671) does not fully validate the value of `batch_dim` and can result in a heap OOB read:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.ReverseSequence(
input = ['aaa','bbb'],
seq_lengths = [1,1,1],
seq_dim = -10,
batch_dim = -10 )
return y
test()
```
There is a check to make sure the value of `batch_dim` does not go over the rank of the input, but there is no check for negative values:
```cc
const int32_t input_rank = c->Rank(input);
if (batch_dim >= input_rank) {
return errors::InvalidArgument(
"batch_dim must be < input rank: ", batch_dim, " vs. ", input_rank);
}
// ...
DimensionHandle batch_dim_dim = c->Dim(input, batch_dim);
```
Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then [the implementation of `Dim`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.h#L415-L428) would access elements before the start of an array:
```cc
DimensionHandle Dim(ShapeHandle s, int64_t idx) {
if (!s.Handle() || s->rank_ == kUnknownRank) {
return UnknownDim();
}
return DimKnownRank(s, idx);
}
·
static DimensionHandle DimKnownRank(ShapeHandle s, int64_t idx) {
CHECK_NE(s->rank_, kUnknownRank);
if (idx < 0) {
return s->dims_[s->dims_.size() + idx];
}
return s->dims_[idx];
}
```
### Patches
We have patched the issue in GitHub commit [37c01fb5e25c3d80213060460196406c43d31995](https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995).
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-2022-21728'} | 2022-03-03T05:14:02.194954Z | 2022-02-09T18:29:24Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L1636-L1671', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21728', 'https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.h#L415-L428', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8'} | null | {'https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995'} | {'https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995'} |
PyPI | PYSEC-2021-803 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version. | {'CVE-2021-37692', 'GHSA-cmgw-8vpc-rc59'} | 2021-12-09T06:35:40.404135Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cmgw-8vpc-rc59', 'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22', 'https://github.com/tensorflow/tensorflow/pull/50508'} | null | {'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22'} | {'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22'} |
PyPI | GHSA-7fvx-3jfc-2cpc | Heap OOB in `ResourceScatterUpdate` | ### Impact
An attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`:
```python
import tensorflow as tf
v = tf.Variable([b'vvv'])
tf.raw_ops.ResourceScatterUpdate(
resource=v.handle,
indices=[0],
updates=['1', '2', '3', '4', '5'])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship.
### Patches
We have patched the issue in GitHub commit [01cff3f986259d661103412a20745928c727326f](https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f).
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-2021-37655'} | 2022-03-03T05:14:03.632801Z | 2021-08-25T14:42:58Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7fvx-3jfc-2cpc', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37655'} | null | {'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f'} | {'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f'} |
PyPI | PYSEC-2021-550 | 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'} | 2021-12-09T06:35:02.145086Z | 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-2021-100 | null | FastAPI is a web framework for building APIs with Python 3.6+ based on standard Python type hints. FastAPI versions lower than 0.65.2 that used cookies for authentication in path operations that received JSON payloads sent by browsers were vulnerable to a Cross-Site Request Forgery (CSRF) attack. In versions lower than 0.65.2, FastAPI would try to read the request payload as JSON even if the content-type header sent was not set to application/json or a compatible JSON media type (e.g. application/geo+json). A request with a content type of text/plain containing JSON data would be accepted and the JSON data would be extracted. Requests with content type text/plain are exempt from CORS preflights, for being considered Simple requests. The browser will execute them right away including cookies, and the text content could be a JSON string that would be parsed and accepted by the FastAPI application. This is fixed in FastAPI 0.65.2. The request data is now parsed as JSON only if the content-type header is application/json or another JSON compatible media type like application/geo+json. It's best to upgrade to the latest FastAPI, but if updating is not possible then a middleware or a dependency that checks the content-type header and aborts the request if it is not application/json or another JSON compatible content type can act as a mitigating workaround. | {'GHSA-8h2j-cgx8-6xv7', 'CVE-2021-32677'} | 2021-06-22T04:54:55.863034Z | 2021-06-09T18:15:00Z | null | null | null | {'https://github.com/tiangolo/fastapi/commit/fa7e3c996edf2d5482fff8f9d890ac2390dede4d', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MATAWX25TYKNEKLDMKWNLYDB34UWTROA/', 'https://github.com/tiangolo/fastapi/security/advisories/GHSA-8h2j-cgx8-6xv7'} | null | {'https://github.com/tiangolo/fastapi/commit/fa7e3c996edf2d5482fff8f9d890ac2390dede4d'} | {'https://github.com/tiangolo/fastapi/commit/fa7e3c996edf2d5482fff8f9d890ac2390dede4d'} |
PyPI | GHSA-xp7p-3gx7-j6wx | calibre-web is vulnerable to Business Logic Errors | calibre-web is vulnerable to Business Logic Errors | {'CVE-2021-4171'} | 2022-03-21T19:17:04.585200Z | 2022-01-21T23:44:15Z | CRITICAL | null | null | {'https://nvd.nist.gov/vuln/detail/CVE-2021-4171', 'https://huntr.dev/bounties/1117f439-133c-4563-afb2-6cd80607bd5c', 'https://github.com/janeczku/calibre-web/commit/3e0d8763c377d2146462811e3e4ccf13f0d312ce', 'https://github.com/janeczku/calibre-web'} | null | {'https://github.com/janeczku/calibre-web/commit/3e0d8763c377d2146462811e3e4ccf13f0d312ce'} | {'https://github.com/janeczku/calibre-web/commit/3e0d8763c377d2146462811e3e4ccf13f0d312ce'} |
PyPI | PYSEC-2021-177 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-xgc3-m89p-vr3x', 'CVE-2021-29540'} | 2021-08-27T03:22:28.584780Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x', 'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} |
PyPI | PYSEC-2021-527 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `Split` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65). An attacker can craft a model such that `num_splits` would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29599', 'GHSA-97wf-p777-86jq'} | 2021-12-09T06:34:58.781489Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97wf-p777-86jq', 'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | null | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} |
PyPI | PYSEC-2020-177 | null | In SaltStack Salt through 2019.2.0, the salt-api NET API with the ssh client enabled is vulnerable to command injection. This allows an unauthenticated attacker with network access to the API endpoint to execute arbitrary code on the salt-api host. | {'CVE-2019-17361'} | 2020-08-24T17:37:00Z | 2020-01-17T02:15:00Z | null | null | null | {'https://usn.ubuntu.com/4459-1/', 'https://docs.saltstack.com/en/latest/topics/releases/2019.2.3.html#security-fix', 'https://github.com/saltstack/salt/commits/master', 'http://lists.opensuse.org/opensuse-security-announce/2020-03/msg00026.html', 'https://www.debian.org/security/2020/dsa-4676'} | null | {'https://github.com/saltstack/salt/commits/master'} | {'https://github.com/saltstack/salt/commits/master'} |
PyPI | GHSA-hmv2-79q8-fv6g | Uncontrolled Resource Consumption in urllib3 | The _encode_invalid_chars function in util/url.py in the urllib3 library 1.25.2 through 1.25.7 for Python allows a denial of service (CPU consumption) because of an inefficient algorithm. The percent_encodings array contains all matches of percent encodings. It is not deduplicated. For a URL of length N, the size of percent_encodings may be up to O(N). The next step (normalize existing percent-encoded bytes) also takes up to O(N) for each step, so the total time is O(N^2). If percent_encodings were deduplicated, the time to compute _encode_invalid_chars would be O(kN), where k is at most 484 ((10+6*2)^2). | {'CVE-2020-7212'} | 2022-03-03T05:14:01.349412Z | 2021-04-30T17:31:43Z | HIGH | null | {'CWE-400'} | {'https://github.com/urllib3/urllib3/commit/a74c9cfbaed9f811e7563cfc3dce894928e0221a', 'https://pypi.org/project/urllib3/1.25.8/', 'https://github.com/urllib3/urllib3/blob/master/CHANGES.rst', 'https://nvd.nist.gov/vuln/detail/CVE-2020-7212'} | null | {'https://github.com/urllib3/urllib3/commit/a74c9cfbaed9f811e7563cfc3dce894928e0221a'} | {'https://github.com/urllib3/urllib3/commit/a74c9cfbaed9f811e7563cfc3dce894928e0221a'} |
PyPI | PYSEC-2020-83 | null | libImaging/PcxDecode.c in Pillow before 6.2.2 has a PCX P mode buffer overflow. | {'GHSA-p49h-hjvm-jg3h', 'CVE-2020-5312'} | 2020-07-10T17:09:00Z | 2020-01-03T01:15:00Z | null | null | null | {'https://www.debian.org/security/2020/dsa-4631', 'https://access.redhat.com/errata/RHSA-2020:0578', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2MMU3WT2X64GS5WHDPKKC2WZA7UIIQ3A/', 'https://github.com/python-pillow/Pillow/commit/93b22b846e0269ee9594ff71a72bec02d2bea8fd', 'https://access.redhat.com/errata/RHSA-2020:0580', 'https://access.redhat.com/errata/RHSA-2020:0681', 'https://access.redhat.com/errata/RHSA-2020:0566', 'https://access.redhat.com/errata/RHSA-2020:0694', 'https://usn.ubuntu.com/4272-1/', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.2.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3DUMIBUYGJRAVJCTFUWBRLVQKOUTVX5P/', 'https://github.com/advisories/GHSA-p49h-hjvm-jg3h', 'https://access.redhat.com/errata/RHSA-2020:0683'} | null | {'https://github.com/python-pillow/Pillow/commit/93b22b846e0269ee9594ff71a72bec02d2bea8fd'} | {'https://github.com/python-pillow/Pillow/commit/93b22b846e0269ee9594ff71a72bec02d2bea8fd'} |
PyPI | PYSEC-2019-131 | null | typed_ast 1.3.0 and 1.3.1 has an ast_for_arguments out-of-bounds read. An attacker with the ability to cause a Python interpreter to parse Python source (but not necessarily execute it) may be able to crash the interpreter process. This could be a concern, for example, in a web-based service that parses (but does not execute) Python code. (This issue also affected certain Python 3.8.0-alpha prereleases.) | {'GHSA-7xxv-wpxj-mx5v', 'CVE-2019-19275'} | 2020-03-14T02:15:00Z | 2019-11-26T15:15:00Z | null | null | null | {'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e', 'https://bugs.python.org/issue36495', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LG5H4Q6LFVRX7SFXLBEJMNQFI4T5SCEA/', 'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce', 'https://github.com/advisories/GHSA-7xxv-wpxj-mx5v', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b'} | null | {'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce'} | {'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b', 'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce'} |
PyPI | PYSEC-2022-51 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. 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, since the code computes `axis + 1`, an attacker can trigger an integer overflow. 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-2022-21727', 'GHSA-c6fh-56w7-fvjw'} | 2022-03-09T00:17:30.181517Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw', 'https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034'} | null | {'https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b'} | {'https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b'} |
PyPI | GHSA-8gv3-57p6-g35r | Heap buffer overflow in `RaggedTensorToTensor` | ### Impact
An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`:
```python
import tensorflow as tf
shape = tf.constant([10, 10], shape=[2], dtype=tf.int64)
values = tf.constant(0, shape=[1], dtype=tf.int64)
default_value = tf.constant(0, dtype=tf.int64)
l = [849, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
row = tf.constant(l, shape=[5, 43], dtype=tf.int64)
rows = [row]
types = ['ROW_SPLITS']
tf.raw_ops.RaggedTensorToTensor(
shape=shape, values=values, default_value=default_value,
row_partition_tensors=rows, row_partition_types=types)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel:
```cc
for (INDEX_TYPE i = 0; i < row_split_size - 1; ++i) {
INDEX_TYPE row_length = row_split(i + 1) - row_split(i);
INDEX_TYPE real_length = std::min(output_size, row_length);
INDEX_TYPE parent_output_index_current = parent_output_index[i];
...
}
```
Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`.
### Patches
We have patched the issue in GitHub commit [a84358aa12f0b1518e606095ab9cfddbf597c121](https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121).
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-2021-29560'} | 2022-04-26T18:17:10.547761Z | 2021-05-21T14:24:57Z | LOW | null | {'CWE-787', 'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29560', 'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8gv3-57p6-g35r'} | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} |
PyPI | GHSA-j2x6-9323-fp7h | Integer bounds error in Vyper | ### Impact
in the following code, the return of `<iface>.returns_int128()` is not validated to fall within the bounds of `int128`. as of v0.3.0, `<iface>.returns_int128()` is validated in simple expressions, but not complex expressions.
```vyper
interface iface:
def returns_int128() -> int128: view
def returns_Bytes33() -> Bytes[33]: view
x: iface
@external
def call_out():
x: int128 = self.x.returns_int128() # affected, <0.3.0
y: uint256 = convert(self.x.returns_int128(), uint256) # affected, <0.3.2
z: Bytes[33] = concat(self.x.returns_Bytes33(), b"") # affected >= 0.3.0, <0.3.2
```
### Patches
0.3.2 (as of https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b)
### Workarounds
Break up operations involving external calls into multiple statements. For instance, instead of the example above, use
```
x: int128 = self.x.returns_int128()
y: uint256 = convert(x, uint256)
``` | {'CVE-2022-24845'} | 2022-04-22T20:30:22.083907Z | 2022-04-22T20:24:13Z | HIGH | null | {'CWE-190'} | {'https://github.com/vyperlang/vyper', 'https://github.com/vyperlang/vyper/security/advisories/GHSA-j2x6-9323-fp7h', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24845', 'https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b'} | null | {'https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b'} | {'https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b'} |
PyPI | PYSEC-2021-462 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. 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. | {'CVE-2021-29534', 'GHSA-6j9c-grc6-5m6g'} | 2021-12-09T06:34:48.648836Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6j9c-grc6-5m6g'} | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.