CVE-2024-0960: Flink-extended Aiflow RCE Vulnerability

CVE-2024-0960 Overview

A critical insecure deserialization vulnerability has been identified in flink-extended ai-flow version 0.3.1. The vulnerability exists in the cloudpickle.loads function within the file ai_flow/cli/commands/workflow_command.py. This deserialization flaw allows remote attackers to potentially execute arbitrary code by supplying maliciously crafted serialized data to the affected function.

Critical Impact

Remote attackers can exploit insecure deserialization in the workflow command processing to achieve arbitrary code execution on systems running vulnerable versions of ai-flow.

Affected Products

• flink-extended aiflow version 0.3.1
• Applications utilizing ai-flow's workflow command functionality
• Systems processing untrusted pickle data through the ai-flow CLI

Discovery Timeline

• 2024-01-27 - CVE-2024-0960 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2024-0960

Vulnerability Analysis

This vulnerability is classified as CWE-502 (Deserialization of Untrusted Data). The vulnerable code path involves the cloudpickle.loads function in the workflow command handler. Python's pickle module, and by extension cloudpickle, is inherently unsafe when processing untrusted data because the deserialization process can execute arbitrary Python code embedded within the serialized object.

When the ai-flow CLI processes workflow commands, it deserializes data using cloudpickle without proper validation of the input source. An attacker who can supply malicious serialized data (such as through a crafted dataset file) can achieve remote code execution on the target system.

Root Cause

The root cause of this vulnerability stems from the unsafe use of the cloudpickle.loads function to deserialize data without implementing proper safeguards. Cloudpickle, like Python's standard pickle module, allows arbitrary code execution during deserialization because the pickle protocol can invoke arbitrary callable objects. The ai-flow application fails to validate or sanitize input before passing it to the deserialization function, creating a direct path for attackers to inject malicious payloads.

Attack Vector

The attack can be launched remotely by providing a specially crafted pickle file (such as the demonstrated dataset.pkl file) to the vulnerable workflow command processing functionality. When the ai-flow application processes this malicious file, the cloudpickle.loads function deserializes the attacker-controlled data, triggering the execution of embedded malicious code.

The exploitation requires the attacker to deliver a malicious pickle file to the target system and have it processed by the vulnerable ai-flow component. While the vulnerability description notes that attack complexity is high, the public disclosure of a proof-of-concept lowers the barrier to exploitation.

A proof-of-concept demonstrating this vulnerability is available at the GitHub PoC Repository, which includes a malicious dataset file that triggers the deserialization vulnerability.

Detection Methods for CVE-2024-0960

Indicators of Compromise

• Unexpected pickle file processing or loading activities in ai-flow logs
• Unusual child processes spawned by Python processes running ai-flow
• Suspicious network connections originating from ai-flow workflow command execution
• Presence of unexpected .pkl files in workflow directories

Detection Strategies

• Monitor for calls to cloudpickle.loads or pickle.loads with untrusted input sources
• Implement application-level logging to track all deserialization operations in ai-flow
• Deploy endpoint detection to identify suspicious Python process behavior following workflow command execution
• Use file integrity monitoring on ai-flow configuration and data directories

Monitoring Recommendations

• Enable verbose logging for ai-flow CLI commands, particularly workflow-related operations
• Monitor system call activity for processes associated with ai-flow
• Implement network traffic analysis to detect anomalous outbound connections from workflow processing
• Review access patterns to pickle files used by ai-flow applications

How to Mitigate CVE-2024-0960

Immediate Actions Required

• Restrict access to the ai-flow CLI to trusted administrators only
• Ensure all pickle/dataset files processed by ai-flow originate from trusted sources
• Consider disabling or isolating workflow command functionality until a patch is available
• Implement network segmentation to limit the impact of potential exploitation

Patch Information

At the time of publication, no official vendor patch has been released for this vulnerability. Organizations should monitor the flink-extended ai-flow project for security updates. Additional information can be found at the VulDB entry for this vulnerability.

Workarounds

• Validate and sanitize all input data before processing with cloudpickle deserialization
• Replace pickle-based serialization with safer alternatives like JSON where possible
• Implement strict file source validation to reject pickle files from untrusted origins
• Run ai-flow in a sandboxed or containerized environment to limit the impact of successful exploitation

# Example: Restrict ai-flow execution to specific users and directories
# Create dedicated user for ai-flow with limited permissions
useradd -r -s /bin/false aiflow_user

# Set restrictive permissions on ai-flow directories
chmod 750 /path/to/ai_flow
chown -R aiflow_user:aiflow_user /path/to/ai_flow

# Ensure workflow data directories are not world-writable
chmod 700 /path/to/ai_flow/data