TensorFlow is an open source platform for machine learning. In affected versions if `tf.image.resize` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow. The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Anymail django-anymail version version 0.2 through 1.3 contains a CWE-532, CWE-209 vulnerability in WEBHOOK_AUTHORIZATION setting value that can result in An attacker with access to error logs could fabricate email tracking events. This attack appear to be exploitable via If you have exposed your Django error reports, an attacker could discover your ANYMAIL_WEBHOOK setting and use this to post fabricated or malicious Anymail tracking/inbound events to your app. This vulnerability appears to have been fixed in v1.4.

TensorFlow is an end-to-end open source platform for machine learning. TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the `While` implementation(https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the `Eval` function for the other and this quickly exhaust all stack space. 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. 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.

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.

TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.

The Security Team noticed that the termination condition of the for loop in the readExternal method is a controllable variable, which, if tampered with, may lead to CPU exhaustion. As a fix, we added an upper bound and termination condition in the read and write logic. We classify it as a "low-priority but useful improvement". SystemDS is a distributed system and needs to serialize/deserialize data but in many code paths (e.g., on Spark broadcast/shuffle or writing to sequence files) the byte stream is anyway protected by additional CRC fingerprints. In this particular case though, the number of decoders is upper-bounded by twice the number of columns, which means an attacker would need to modify two entries in the byte stream in a consistent manner. By adding these checks robustness was strictly improved with almost zero overhead. These code changes are available in versions higher than 2.2.1.

A flaw was found in the Ansible Engine 2.9.18, where sensitive info is not masked by default and is not protected by the no_log feature when using the sub-option feature of the basic.py module. This flaw allows an attacker to obtain sensitive information. The highest threat from this vulnerability is to confidentiality.

lib/ansible/playbook/__init__.py in Ansible 1.2.x before 1.2.3, when playbook does not run due to an error, allows local users to overwrite arbitrary files via a symlink attack on a retry file with a predictable name in /var/tmp/ansible/.

TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is 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.

Zope before 2.2.1 does not properly restrict access to the getRoles method, which allows users who can edit DTML to add or modify roles by modifying the roles list that is included in a request.

The "create an instance" API in OpenStack Compute (Nova) Folsom, Grizzly, and Havana does not properly enforce the os-flavor-access:is_public property, which allows remote authenticated users to boot arbitrary flavors by guessing the flavor id.  NOTE: this issue is due to an incomplete fix for CVE-2013-2256.

aaugustin websockets version 4 contains a CWE-409: Improper Handling of Highly Compressed Data (Data Amplification) vulnerability in Servers and clients, unless configured with compression=None that can result in Denial of Service by memory exhaustion. This attack appear to be exploitable via Sending a specially crafted frame on an established connection. This vulnerability appears to have been fixed in 5.

** DISPUTED ** core.py in Mitogen before 0.2.8 has a typo that drops the unidirectional-routing protection mechanism in the case of a child that is initiated by another child. The Ansible extension is unaffected. NOTE: the vendor disputes this issue because it is exploitable only in conjunction with hypothetical other factors, i.e., an affected use case within a library caller, and a bug in the message receiver policy code that led to reliance on this extra protection mechanism.

Incorrect Access Control in Lin-CMS-Flask v0.1.1 allows remote attackers to obtain sensitive information and/or gain privileges due to the application not invalidating a user's authentication token upon logout, which allows for replaying packets.

typed_ast 1.3.0 and 1.3.1 has a handle_keywordonly_args 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.)

### Impact

This advisory addresses several LOW severity issues with streaming signed messages and restricting processing of certain types of invalid messages. 

This ESDK supports a streaming mode where callers may stream the plaintext of signed messages before the ECDSA signature is validated. In addition to these signatures, the ESDK uses AES-GCM encryption and all plaintext is verified before being released to a caller. There is no impact on the integrity of the ciphertext or decrypted plaintext, however some callers may rely on the the ECDSA signature for non-repudiation. Without validating the ECDSA signature, an actor with trusted KMS permissions to decrypt a message may also be able to encrypt messages. This update introduces a new API for callers who wish to stream only unsigned messages. 

For customers who process ESDK messages from untrusted sources, this update also introduces a new configuration to limit the number of Encrypted Data Keys (EDKs) that the ESDK will attempt to process per message. This configuration provides customers with a way to limit the number of AWS KMS Decrypt API calls that the ESDK will make per message. This setting will reject messages with more EDKs than the configured limit.

Finally, this update adds early rejection of invalid messages with certain invalid combinations of algorithm suite and header data.

### Patches

Fixed in versions 1.9 and 2.2. We recommend that all users upgrade to address these issues.

Customers leveraging the ESDK’s streaming features have several options to protect signature validation. One is to ensure that client code reads to the end of the stream before using released plaintext. With this release, using the new API for streaming and falling back to the non-streaming decrypt API for signed messages prevents using any plaintext from signed data before the signature is validated. See https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/about-versions.html#version2.2.x

Users processing ESDK messages from untrusted sources should use the new maximum encrypted data keys parameter. See https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/about-versions.html#version2.2.x

### Workarounds

None

### For more information

https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/concepts.html#digital-sigs

https://docs.aws.amazon.com/encryption-sdk/latest/developer-guide/about-versions.html#version2.2.x


Jupyter Notebook before 5.7.1 allows XSS via an untrusted notebook because nbconvert responses are considered to have the same origin as the notebook server. In other words, nbconvert endpoints can execute JavaScript with access to the server API. In notebook/nbconvert/handlers.py, NbconvertFileHandler and NbconvertPostHandler do not set a Content Security Policy to prevent this.

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. 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.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values.

In Django 3.2 before 3.2.25, 4.2 before 4.2.11, and 5.0 before 5.0.3, the django.utils.text.Truncator.words() method (with html=True) and the truncatewords_html template filter are subject to a potential regular expression denial-of-service attack via a crafted string. NOTE: this issue exists because of an incomplete fix for CVE-2019-14232 and CVE-2023-43665.

Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would trigger a division by zero in `BiasAndClamp` implementation. There is no check that the `bias_size` is non zero. 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.

WordOps through 3.20.0 has a wo/cli/plugins/stack_pref.py TOCTOU race condition because the conf_path os.open does not use a mode parameter during file creation.

The administration application in Django 0.91, 0.95, and 0.96 stores unauthenticated HTTP POST requests and processes them after successful authentication occurs, which allows remote attackers to conduct cross-site request forgery (CSRF) attacks and delete or modify data via unspecified requests.

NLTK Downloader before 3.4.5 is vulnerable to a directory traversal, allowing attackers to write arbitrary files via a ../ (dot dot slash) in an NLTK package (ZIP archive) that is mishandled during extraction.

Improper Handling of Length Parameter Inconsistency in GitHub repository ikus060/rdiffweb prior to 2.4.8.

### Impact

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files). This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings.

Note that Ibis itself makes **extremely limited** use of `pyarrow.parquet.read_table`:

1. `read_table` is used in tests, where the input file is entirely controlled by the Ibis developers
2. `read_table` is used in the `ibis/examples/__init__.py` as a fallback for backends that don't support reading Parquet directly. Parquet data used in `ibis.examples` are also managed by the Ibis developers. This Parquet data is generated from CSV files and SQLite databases.
3. The Pandas and Dask backends both use PyArrow to read Parquet files and are therefore affected.

Ibis **does not** make use of APIs that directly read from either Arrow IPC files or Feather files.

### Patches

Ibis imports the `pyarrow_hotfix` package wherever pyarrow is used, as of version 7.1.0.

Upgrading to Arrow 14.0.1 is also a possible solution, starting in Ibis 7.1.0.

### Workarounds

Install [`pyarrow_hotfix`](https://pypi.org/project/pyarrow-hotfix/) and run `import pyarrow_hotfix` ahead of any and all `import ibis` statements.

For example:

```python
import ibis
```

becomes

```python
import pyarrow_hotfix
import ibis
```

### References

https://www.cve.org/CVERecord?id=CVE-2023-47248
https://nvd.nist.gov/vuln/detail/CVE-2023-47248

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.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor 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.

In Pallets Jinja before 2.10.1, str.format_map allows a sandbox escape.

An issue was discovered in Guest.migrate in virt/libvirt/guest.py in OpenStack Nova before 19.3.1, 20.x before 20.3.1, and 21.0.0. By performing a soft reboot of an instance that has previously undergone live migration, a user may gain access to destination host devices that share the same paths as host devices previously referenced by the virtual machine on the source host. This can include block devices that map to different Cinder volumes at the destination than at the source. Only deployments allowing host-based connections (for instance, root and ephemeral devices) are affected.

kupu_spellcheck.py in Kupu in Plone before 4.0 allows remote attackers to cause a denial of service (ZServer thread lock) via a crafted URL.

OpenStack Cinder before 2014.1.5 (icehouse), 2014.2.x before 2014.2.4 (juno), and 2015.1.x before 2015.1.1 (kilo) allows remote authenticated users to read arbitrary files via a crafted qcow2 signature in an image to the upload-to-image command.

In Django 2.2 before 2.2.21, 3.1 before 3.1.9, and 3.2 before 3.2.1, MultiPartParser, UploadedFile, and FieldFile allowed directory traversal via uploaded files with suitably crafted file names.

CVE ID↕	Published↕	CVSS↓
PYSEC-2021-392	Nov 5, 2021	N/A
PYSEC-2018-46	Mar 13, 2018	N/A
PYSEC-2021-228	May 14, 2021	N/A
PYSEC-2021-291	Aug 12, 2021	N/A
PYSEC-2021-467	May 14, 2021	N/A
PYSEC-2022-222	Jun 27, 2022	N/A
PYSEC-2021-1	Apr 29, 2021	N/A
PYSEC-2013-2	Sep 16, 2013	N/A
PYSEC-2021-155	May 14, 2021	N/A
GHSA-9cmq-pj6p-hgwf	Apr 30, 2022	N/A
GHSA-43cm-73px-5v4m	May 17, 2022	N/A
PYSEC-2018-79	Jun 26, 2018	N/A
PYSEC-2019-104	Aug 18, 2019	N/A
PYSEC-2021-341	Aug 16, 2021	N/A
PYSEC-2019-60	Nov 26, 2019	N/A
GHSA-89v2-g37m-g3ff	Jun 1, 2021	N/A
PYSEC-2018-17	Nov 18, 2018	N/A
PYSEC-2021-204	May 14, 2021	N/A
PYSEC-2020-318	Sep 25, 2020	N/A
PYSEC-2021-857	Dec 17, 2021	N/A
PYSEC-2024-47	Mar 15, 2024	N/A
PYSEC-2022-66	Feb 4, 2022	N/A
PYSEC-2024-175	May 6, 2024	N/A
PYSEC-2008-2	Sep 4, 2008	N/A
PYSEC-2019-36	Aug 22, 2019	N/A
PYSEC-2022-292	Sep 26, 2022	N/A
GHSA-x563-6hqv-26mr	Nov 17, 2023	N/A
PYSEC-2021-484	May 14, 2021	N/A
PYSEC-2019-217	Apr 7, 2019	N/A
PYSEC-2020-243	Aug 26, 2020	N/A
PYSEC-2014-38	Sep 30, 2014	N/A
GHSA-9hcj-h2qc-689p	May 17, 2022	N/A
PYSEC-2021-7	May 5, 2021	N/A

Know every vulnerability
before it knows you.

pypi CVE Vulnerabilities — DevGuard Vulnerability Database

Know every vulnerabilitybefore it knows you.

pypi CVE Vulnerabilities — DevGuard Vulnerability Database

Know every vulnerability
before it knows you.