AI & LLM Vulnerabilities

Gradio (a framework for building web interfaces for machine learning models) before version 4.20 has a vulnerability on Windows where credentials can be unintentionally revealed. The issue stems from improper encoding or escaping of output (meaning the software doesn't properly clean or protect sensitive information before displaying it).

A vulnerability in sagemaker-python-sdk (a library for machine learning on Amazon SageMaker) allows OS command injection (running unauthorized system commands) if unsafe input is passed to the capture_dependencies function's requirements_path parameter, potentially letting attackers execute code remotely or disrupt service. The vulnerability affects versions before 2.214.3.

A vulnerability in the sagemaker-python-sdk library (used for machine learning on Amazon SageMaker) allows unsafe deserialization, where the NumpyDeserializer module can execute malicious code if it processes untrusted pickled data (serialized Python objects stored in a binary format). An attacker could exploit this to run arbitrary commands on a system or crash it.

PyTorch versions before v2.2.0 contain a use-after-free vulnerability (a memory bug where code tries to access data that has already been freed) in the mobile interpreter component. This vulnerability was identified in the torch/csrc/jit/mobile/interpreter.cpp file.

LangChain's LocalFileStore feature has a path traversal vulnerability (a security flaw where attackers can access files outside the intended directory by using special path sequences like '../'). An attacker can exploit this to read or write any files on the system, potentially stealing data or executing malicious code. The problem stems from the mset and mget methods not properly filtering user input before handling file paths.

CVE-2024-1594 is a path traversal vulnerability (a flaw that lets attackers access files outside their permitted directory) in MLflow's experiment creation feature. Attackers can exploit this by inserting a fragment component (#) into the artifact_location parameter to read arbitrary files on the server.

MLflow, a machine learning platform, has a path traversal vulnerability (a security flaw where attackers can access files outside intended directories) caused by improper handling of URL parameters. Attackers can use the semicolon (;) character to hide malicious path sequences in URLs, potentially gaining unauthorized access to sensitive files or compromising the server.

A path traversal vulnerability (a security flaw where attackers use special characters like ../ to access files outside their intended directory) exists in MLflow's artifact deletion feature. Attackers can delete arbitrary files on a server by exploiting an extra decoding step that fails to properly validate user input, and this vulnerability affects versions up to 2.9.2.

CVE-2024-1558 is a path traversal vulnerability (a security flaw where an attacker uses special characters like "../" to access files outside their intended directory) in MLflow's model version creation function. An attacker can craft a malicious `source` parameter that bypasses the validation check, allowing them to read any file on the server when fetching model artifacts.