Code injection vulnerability in pyload/pyload < 0.5.0b3.dev31
Code Injection in GitHub repository pyload/pyload prior to 0.5.0b3.dev31.
XXE Injection in glances < 3.2.1 via Fault in XML parsing
The package glances before 3.2.1 are vulnerable to XML External Entity (XXE) Injection via the use of Fault to parse untrusted XML data, which is known to be vulnerable to XML attacks.
Python-oslo-utils exposes plaintext passwords in debug logs when they contain a double quote
A flaw was found in python-oslo-utils. Due to improper parsing, passwords with a double quote ( " ) in them cause incorrect masking in debug logs, causing any part of the password after the double quote to be plaintext.
Cisco NX-OS local root access via Python interpreter (CVEs: CSCun02887, CSCur00115, CSCur00127)
Cisco NX-OS 6.0(2) and 6.2(2) on Nexus devices has an improper OS configuration, which allows local users to obtain root access via unspecified input to the Python interpreter, aka Bug IDs CSCun02887, CSCur00115, and CSCur00127.
Cisco NX-OS 6.2(8a) allows an admin in one VDC to delete files in another VDC
The Python interpreter in Cisco NX-OS 6.2(8a) on Nexus 7000 devices allows local users to bypass intended access restrictions and delete an arbitrary VDC's files by leveraging administrative privileges in one VDC, aka Bug ID CSCur08416.
Untrusted search path in RHEV-M 3.1 allows local users to gain privileges via malicious Python modules in /tmp when adding a host
Multiple untrusted search path vulnerabilities in Red Hat Enterprise Virtualization Manager (RHEV-M) before 3.1, when adding a host, allow local users to gain privileges via a Trojan horse (1) deployUtil.py or (2) vds_bootstrap.py Python module in /tmp/.
Untrusted search path in Blender 2.46 allows local users to execute arbitrary code via a malicious Python file in the current directory
Untrusted search path vulnerability in BPY_interface in Blender 2.46 allows local users to execute arbitrary code via a Trojan horse Python file in the current working directory, related to an erroneous setting of sys.path by the PySys_SetArgv function.
Blender 0.1.9h: Remote code execution via crafted KML/KMZ files
Eval injection vulnerability in the (a) kmz_ImportWithMesh.py Script for Blender 0.1.9h, as used in (b) Blender before 2.43, allows user-assisted remote attackers to execute arbitrary Python code by importing a crafted (1) KML or (2) KMZ file.
Segfault/DoS vulnerability pre-2.9.0 TensorFlow due to unsupported quantized types in tf.compat.v1 ops
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.
Web Cache Poisoning in bottle < 0.12.19 via parameter cloaking
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.
Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.
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