CVE-2024-11041: Vllm Vllm RCE Vulnerability

CVE-2024-11041 Overview

CVE-2024-11041 is an insecure deserialization vulnerability affecting vllm-project vLLM version v0.6.2. The vulnerability exists in the MessageQueue.dequeue() API function, which uses Python's pickle.loads to parse received socket data directly without proper validation. This unsafe deserialization practice enables remote code execution, allowing an attacker to send a malicious payload to the MessageQueue and execute arbitrary code on the victim's machine.

Critical Impact

Remote code execution via unsafe pickle deserialization in vLLM's MessageQueue API allows attackers to execute arbitrary code on systems running vulnerable versions, potentially leading to complete system compromise.

Affected Products

• vLLM version 0.6.2

Discovery Timeline

• 2025-03-20 - CVE-2024-11041 published to NVD
• 2025-07-31 - Last updated in NVD database

Technical Details for CVE-2024-11041

Vulnerability Analysis

This vulnerability is classified as CWE-502 (Deserialization of Untrusted Data). The core issue lies in the use of Python's pickle module to deserialize data received over network sockets without any validation or sanitization. Python's pickle module is inherently unsafe when processing untrusted data, as it can execute arbitrary code during the deserialization process.

The MessageQueue.dequeue() function directly passes socket-received data to pickle.loads, creating a direct path for malicious payloads to achieve code execution. In multi-node distributed inference deployments of vLLM, this vulnerability is particularly dangerous as it could allow lateral movement across the cluster.

Root Cause

The root cause is the use of Python's pickle.loads function to deserialize data received from network sockets without implementing any input validation, type checking, or secure deserialization alternatives. The pickle module in Python can instantiate arbitrary objects and call arbitrary functions during deserialization, making it inherently dangerous for processing untrusted input.

Attack Vector

The attack vector is network-based, requiring no authentication or user interaction. An attacker can exploit this vulnerability by:

1. Identifying a vLLM instance with an exposed MessageQueue endpoint
2. Crafting a malicious Python pickle payload that executes arbitrary code when deserialized
3. Sending the malicious payload to the MessageQueue
4. Achieving remote code execution when the dequeue() function processes the payload with pickle.loads

The vulnerability is exploited by sending specially crafted serialized Python objects that, when unpickled, execute malicious code through mechanisms such as the __reduce__ method, which can return arbitrary callable objects with arguments.

Detection Methods for CVE-2024-11041

Indicators of Compromise

• Unexpected network connections to vLLM MessageQueue endpoints from external or unauthorized sources
• Unusual process spawning or command execution originating from Python/vLLM processes
• Anomalous pickle-formatted network traffic containing suspicious class references or code execution patterns
• Unauthorized file system modifications or new files created by vLLM worker processes

Detection Strategies

• Monitor network traffic for pickle-formatted payloads being sent to vLLM services, particularly looking for serialized objects with __reduce__ or other code execution primitives
• Implement endpoint detection rules to identify unusual child processes spawned by Python processes running vLLM
• Deploy application-level logging to capture MessageQueue operations and flag unexpected deserialization activities
• Use intrusion detection systems to identify patterns consistent with pickle-based exploitation attempts

Monitoring Recommendations

• Enable verbose logging on vLLM instances to capture MessageQueue activity
• Implement network segmentation to restrict access to MessageQueue endpoints to trusted hosts only
• Deploy file integrity monitoring on systems running vLLM to detect post-exploitation file modifications
• Configure SIEM alerts for process execution anomalies associated with Python/vLLM services

How to Mitigate CVE-2024-11041

Immediate Actions Required

• Restrict network access to vLLM MessageQueue endpoints using firewall rules and network segmentation
• Audit all systems running vLLM v0.6.2 and identify exposed MessageQueue services
• Implement network-level controls to ensure only trusted internal hosts can communicate with vLLM services
• Consider deploying vLLM behind a secure proxy or VPN if remote access is required

Patch Information

Check the vLLM project repository for updated versions that address this vulnerability. The Huntr Bounty Report contains additional details about the vulnerability disclosure and remediation.

Organizations should upgrade to a patched version of vLLM as soon as one becomes available that replaces the unsafe pickle deserialization with secure alternatives.

Workarounds

• Implement network segmentation to isolate vLLM instances from untrusted networks and restrict MessageQueue access to authorized hosts only
• Deploy a security wrapper or proxy that validates incoming MessageQueue data before it reaches the vLLM service
• If feasible, modify the application code to replace pickle.loads with safer serialization alternatives such as JSON or Protocol Buffers with strict schema validation
• Run vLLM services in containerized environments with minimal privileges and restricted network policies to limit the impact of potential exploitation