VAITP

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

The package bottle from 0 and before 0.12.19 are vulnerable to Web Cache Poisoning by using a vector called parameter cloaking. When the attacker can separate query parameters using a semicolon (;), they can cause a difference in the interpretation of the request between the proxy (running with default configuration) and the server. This can result in malicious requests being cached as completely safe ones, as the proxy would usually not see the semicolon as a separator, and therefore would not include it in a cache key of an unkeyed parameter.

An attacker may be able to execute malicious actions due to the lack of device access protections and device permissions when using the web application. This could lead to uploading python files which can be later executed.

OrbiTeam BSCW Classic before 7.4.3 allows authenticated remote code execution (RCE) during archive extraction via attacker-supplied Python code in the class attribute of a .bscw file. This is fixed in 5.0.12, 5.1.10, 5.2.4, 7.3.3, and 7.4.3.

fw_dbus.py in system-config-firewall 1.2.29 and earlier uses the pickle Python module unsafely during D-Bus communication between the GUI and the backend, which might allow local users to gain privileges via a crafted serialized object.

Multiple untrusted search path vulnerabilities in dstat before 0.7.0 allow local users to gain privileges via a Trojan horse Python module in (1) the current working directory or (2) a certain subdirectory of the current working directory.

An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). The intermediate-level directories of the filesystem cache had the system's standard umask rather than 0o077.

Microsoft Windows XP SP1 allows local users to cause a denial of service (system crash) via an empty datagram to a raw IP over IP socket (IP protocol 4), as originally demonstrated using code in Python 2.3.

TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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 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.