AI Sec Watch

CVE-2023-5675 is a security flaw in Quarkus (a Java framework for building applications) where authorization checks are bypassed for REST API endpoints whose methods are defined in abstract classes or modified by extensions using annotation processors, if certain security settings are enabled. This means unauthorized users could potentially access protected API endpoints that should require authentication or specific permissions.

PyTorch versions before 2.2.0 contain an out-of-bounds read vulnerability (a bug where code tries to read data from memory outside its allowed range) in the flatbuffer_loader component, which is used for loading machine learning models on mobile devices. This vulnerability could potentially allow attackers to read sensitive information from memory or cause the program to crash.

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.

PyTorch versions before v2.2.0 contain a heap buffer overflow vulnerability (a type of memory safety bug where a program writes data beyond allocated memory limits) in its runtime component that allows attackers to crash the software through specially crafted input. This is a Denial of Service attack, meaning the goal is to make the software unusable rather than steal data.

CVE-2024-3660 is a code injection vulnerability (a flaw that lets attackers insert and run harmful code) in TensorFlow's Keras framework (a machine learning library) affecting versions before 2.13. Attackers can exploit this to execute arbitrary code (run commands they choose) with the same permissions as the application using a vulnerable model.

MLflow (a machine learning platform) has a vulnerability where its URI parsing function incorrectly classifies certain file paths as non-local, allowing attackers to read sensitive files they shouldn't access. By crafting malicious model versions with specially crafted parameters, attackers can bypass security checks and read arbitrary files from the system.

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.

BentoML (a framework for building AI applications) contains an insecure deserialization vulnerability that lets attackers run arbitrary commands on servers by sending specially crafted requests. When the framework deserializes (converts stored data back into usable objects) a malicious object, it automatically executes hidden OS commands, giving attackers control of the server.

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.