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Overview

AI models, once trained, must be saved to a file for storage and deployment. Many popular machine learning frameworks use serialization formats that are inherently insecure. Insecure Deserialization is a vulnerability where an application deserializes (reads and reconstructs) data from an untrusted source without proper validation, potentially leading to Remote Code Execution (RCE). The most common vector for this in the Python ecosystem is the Pickle format.
  • Why it Matters: An attacker who can control a model file being loaded by an application can potentially achieve full control over the host system. This is a critical AI supply chain vulnerability.

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Technical Mechanics & Foundations

The Pickle format is not a simple data format; it is a stack-based instruction language. When a pickle file is loaded (pickle.load()), the Python interpreter executes a series of “opcodes” that reconstruct the original Python object. Some of these opcodes are dangerous because they can import arbitrary modules and call their functions. The most notorious of these is REDUCE.
  • The REDUCE Opcode: This opcode instructs the pickle interpreter to find a callable (like a function) and apply it to arguments. An attacker can craft a pickle file that uses REDUCE to execute arbitrary system commands.
  • Detection & Evasion: Simple static scanners often look for dangerous opcodes like REDUCE or GLOBAL. The challenge for an attacker is to create a payload that achieves code execution while evading these simple signature-based checks.

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Challenge Arena

  • pickle: Create a malicious pickle file that bypasses static analysis checks to achieve code execution upon being loaded.