VAITP

urllib3 is an HTTP client library for Python. From 2.6.0 to before 2.7.0, urllib3 could decompress the whole response instead of the requested portion (1) during the second HTTPResponse.read(amt=N) call when the response was decompressed using the official Brotli library or (2) when HTTPResponse.drain_conn() was called after the response had been read and decompressed partially (compression algorithm did not matter here). These issues could cause urllib3 to fully decode a small amount of highly compressed data in a single operation. This could result in excessive resource consumption (high CPU usage and massive memory allocation for the decompressed data) on the client side. This vulnerability is fixed in 2.7.0.

urllib3 is an HTTP client library for Python. From 1.23 to before 2.7.0, cross-origin redirects followed from the low-level API via ProxyManager.connection_from_url().urlopen(..., assert_same_host=False) still forward these sensitive headers. This vulnerability is fixed in 2.7.0.

Heym before 0.0.21 contains a sandbox escape vulnerability in the custom Python tool executor that allows authenticated workflow authors to bypass sandbox restrictions by using object-graph introspection primitives. Attackers can use Python introspection techniques to recover the unrestricted __import__ function, import blocked modules such as os and subprocess, and access inherited backend environment variables containing database credentials and encryption keys to execute arbitrary host commands as the backend service user.

Mako is a template library written in Python. Prior to 1.3.12, on Windows, a URI using backslash traversal (e.g. \..\..\ secret.txt) bypasses the directory traversal check in Template.__init__ and the posixpath-based normalization in TemplateLookup.get_template(), allowing reads of files outside the configured template directory. This vulnerability is fixed in 1.3.12.

Lemur manages TLS certificate creation. Prior to 1.9.0, Lemur's LDAP authentication module (lemur/auth/ldap.py) constructs LDAP search filters using unsanitized user input via Python string interpolation. An authenticated LDAP user can inject LDAP filter metacharacters through the username field to manipulate group membership queries and escalate their privileges to administrator. This vulnerability is fixed in 1.9.0.

wger is a free, open-source workout and fitness manager. Prior to 2.6, the reset_user_password and gym_permissions_user_edit views in wger perform a gym-scope authorization check using Python object comparison (!=) that evaluates None != None as False, silently bypassing the guard when both the attacker and victim have no gym assignment (gym=None). A user with gym.manage_gym permission and gym=None can reset the password of any other gym=None user; the new plaintext password is returned verbatim in the HTML response body, enabling one-shot full account takeover. The victim's original password is invalidated, locking them out permanently. This vulnerability is fixed in 2.6.

The mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization (CWE-502) when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.from_pretrained() method uses torch.load() to load the pytorch_model.bin weight file without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by publishing a malicious model repository on HuggingFace Hub. When a victim loads a model from this repository, arbitrary code is executed on the victim's system in the context of the mamba process.

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted PyTorch model file, leading to arbitrary code execution on the system hosting the Ludwig model server.

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) through its predict() method. When a user provides a dataset file path to the predict() method, the framework automatically determines the file format. If the file is a pickle (.pkl) file, it is loaded using pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction.

The llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec() function without any sanitization, sandboxing, or security restrictions. An attacker can exploit this by crafting a malicious llm command with arbitrary Python code in the --functions argument and using social engineering to trick a victim into running it. This leads to arbitrary code execution on the victim's system, potentially granting the attacker full control.