CVE-2020-22083: Jsonpickle Deserialization RCE Vulnerability

CVE-2020-22083 Overview

CVE-2020-22083 is an insecure deserialization vulnerability in jsonpickle through version 1.4.1 that allows remote code execution during deserialization of a malicious payload through the decode() function. The jsonpickle library is a Python library used to serialize complex Python objects to JSON and deserialize them back. When processing untrusted input, attackers can craft malicious payloads that execute arbitrary code upon deserialization.

It should be noted that this behavior has been argued by some to be expected and documented—pickle-based serialization is known to be capable of causing arbitrary code execution and must not be used with untrusted data. However, the critical nature of the impact when applications do process untrusted input makes this a significant security concern.

Critical Impact

Remote attackers can achieve arbitrary code execution by sending maliciously crafted JSON payloads to applications using jsonpickle's decode() function with untrusted data, potentially leading to complete system compromise.

Affected Products

• jsonpickle versions through 1.4.1
• Applications using jsonpickle to deserialize untrusted user input
• Python environments with jsonpickle dependency for JSON serialization/deserialization

Discovery Timeline

• 2020-12-17 - CVE-2020-22083 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2020-22083

Vulnerability Analysis

This vulnerability falls under CWE-502 (Deserialization of Untrusted Data). The jsonpickle library extends Python's pickle serialization capabilities to produce JSON output. Like pickle, jsonpickle can reconstruct arbitrary Python objects during deserialization, including objects with __reduce__ methods that can execute arbitrary code.

When an application uses jsonpickle.decode() to deserialize JSON data from an untrusted source, an attacker can craft a payload containing serialized Python objects that, upon reconstruction, execute malicious code. This is particularly dangerous because the JSON format appears benign compared to binary pickle data, potentially leading developers to incorrectly assume jsonpickle is safe for processing untrusted input.

The attack is network-accessible, requires no authentication or user interaction, and can result in complete compromise of confidentiality, integrity, and availability on the target system.

Root Cause

The root cause is the inherent design of pickle-based serialization in Python, which jsonpickle leverages. Python's pickle module is designed to serialize and deserialize arbitrary Python objects, including code execution capabilities through special methods like __reduce__(). When jsonpickle deserializes a JSON payload, it reconstructs Python objects including any malicious code embedded within the serialized representation.

The decode() function does not distinguish between trusted and untrusted input sources, making any application that processes external data vulnerable to this attack vector.

Attack Vector

The attack is executed remotely over the network. An attacker crafts a malicious JSON payload containing a serialized Python object that includes code execution logic. When the target application calls jsonpickle.decode() on this payload, the malicious object is reconstructed and its embedded code is executed in the context of the application.

The exploitation typically involves crafting a payload that leverages Python's __reduce__ method to specify arbitrary function calls during object reconstruction. Common exploitation targets include executing system commands via os.system(), subprocess.Popen(), or similar functions.

For detailed exploitation techniques and payload generation, refer to the Versprite Blog on jsonpickle Exploitation and the GitHub Python Deserialization Tool.

Detection Methods for CVE-2020-22083

Indicators of Compromise

• Unexpected process spawning from Python application processes, particularly shell commands or system utilities
• Network connections originating from Python processes to unknown external hosts
• Unusual file system modifications in application directories or system paths
• Log entries showing deserialization errors or unusual JSON payload structures containing Python object references

Detection Strategies

• Monitor for jsonpickle.decode() calls processing data from external sources such as HTTP requests, message queues, or file uploads
• Implement application-level logging to capture incoming JSON payloads before deserialization for forensic analysis
• Use static analysis tools to identify code paths where untrusted input reaches jsonpickle.decode() calls
• Deploy runtime application self-protection (RASP) solutions that can detect and block suspicious deserialization patterns

Monitoring Recommendations

• Configure endpoint detection and response (EDR) solutions to alert on child process creation from Python application processes
• Monitor network egress for connections initiated by Python processes that should not have outbound network requirements
• Implement file integrity monitoring on critical system directories to detect unauthorized modifications
• Review application logs for JSON payloads containing suspicious keys like py/object, py/reduce, or references to dangerous modules such as os, subprocess, or commands

How to Mitigate CVE-2020-22083

Immediate Actions Required

• Audit all application code to identify uses of jsonpickle.decode() with untrusted input sources
• Replace jsonpickle with standard json module for deserializing untrusted data where complex object serialization is not required
• Implement strict input validation and sanitization before any deserialization operations
• Consider using jsonpickle's safe mode or restricted unpicklers where available for controlled deserialization scenarios

Patch Information

The jsonpickle maintainers have indicated this is documented expected behavior consistent with pickle's security model. Applications should not use jsonpickle to deserialize untrusted data. For more information, see the GitHub jsonpickle Issue Discussion and the Red Hat CVE Security Advisory.

Organizations should implement architectural changes to avoid deserializing untrusted data with jsonpickle rather than waiting for a library-level fix.

Workarounds

• Use Python's standard json library instead of jsonpickle for processing any untrusted JSON input
• Implement a whitelist-based approach if jsonpickle must be used, validating that incoming data does not contain serialized object references before deserialization
• Deploy network segmentation to limit the impact of potential code execution by restricting what compromised application processes can access
• Run applications using jsonpickle with minimal privileges and in sandboxed environments to contain potential exploitation

# Example: Check for jsonpickle usage in your Python codebase
grep -r "jsonpickle.decode" --include="*.py" /path/to/application/

# Example: Search for vulnerable patterns in dependencies
pip show jsonpickle | grep Version
# If version <= 1.4.1, review usage patterns for untrusted input processing