CVE-2024-9052 in vLLM
Summary
by MITRE • 03/20/2025
vllm-project vllm version 0.6.0 contains a vulnerability in the distributed training API. The function vllm.distributed.GroupCoordinator.recv_object() deserializes received object bytes using pickle.loads() without sanitization, leading to a remote code execution vulnerability.
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Analysis
by VulDB Data Team • 03/20/2025
The vulnerability identified as CVE-2024-9052 affects the vllm-project vllm version 0.6.0 and resides within the distributed training application programming interface. This flaw manifests in the vllm.distributed.GroupCoordinator.recv_object() function which processes incoming object bytes through pickle.loads() without implementing proper sanitization mechanisms. The issue represents a critical security weakness that can be exploited by malicious actors to execute arbitrary code on systems running vulnerable versions of the software.
The technical implementation of this vulnerability stems from the dangerous practice of using pickle.loads() for deserializing untrusted data streams. This deserialization method in python is inherently insecure when processing external inputs because it can execute arbitrary code during the deserialization process. The vulnerability directly maps to CWE-502 which specifically addresses "Deserialization of Untrusted Data" and falls under the broader category of code injection attacks. When the recv_object() function receives data from distributed training nodes, it blindly deserializes the payload using pickle.loads() without validating or sanitizing the incoming bytes, creating an attack surface where malicious actors can craft specially crafted pickle payloads to execute arbitrary commands on the target system.
The operational impact of this vulnerability extends beyond simple code execution to encompass complete system compromise in distributed training environments. In multi-node machine learning deployments where vllm is used for large language model training, attackers who gain access to the network communication between training nodes can inject malicious payloads that will execute on any node receiving the serialized data. This creates a significant risk for organizations running distributed machine learning workloads, as the attack vector can be exploited through network-based communication channels without requiring direct system access. The vulnerability affects the integrity and confidentiality of distributed training operations, potentially allowing attackers to access training data, modify model parameters, or even use compromised nodes for further attacks within the network.
Mitigation strategies for CVE-2024-9052 should prioritize immediate patching of affected vllm versions to address the insecure deserialization implementation. Organizations should also implement network segmentation and access controls to limit communication between distributed training nodes, reducing the attack surface available to potential adversaries. The use of alternative serialization methods such as JSON or msgpack instead of pickle should be considered for future implementations, as these formats do not execute arbitrary code during deserialization. Additionally, implementing network monitoring and intrusion detection systems can help identify suspicious communication patterns that may indicate exploitation attempts. According to ATT&CK framework, this vulnerability relates to T1059.001 for command and scripting interpreter and T1566 for malicious file execution, highlighting the need for comprehensive security controls that address both network and endpoint security measures to prevent exploitation of this remote code execution vulnerability.