CVE-2025-29783: Vllm Vllm RCE Vulnerability

CVE-2025-29783 Overview

CVE-2025-29783 is a critical insecure deserialization vulnerability affecting vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). When vLLM is configured to use Mooncake for distributed key-value (KV) cache distribution, unsafe deserialization is exposed directly over ZMQ/TCP on all network interfaces. This vulnerability allows attackers with adjacent network access to execute arbitrary code on distributed hosts running vLLM with the Mooncake integration enabled.

Critical Impact

Remote code execution vulnerability enabling attackers to compromise distributed LLM inference infrastructure through unsafe deserialization over ZMQ/TCP communications.

Affected Products

• vLLM versions prior to 0.8.0
• vLLM deployments configured with Mooncake integration
• Distributed vLLM environments using KV cache distribution

Discovery Timeline

• 2025-03-19 - CVE-2025-29783 published to NVD
• 2025-07-01 - Last updated in NVD database

Technical Details for CVE-2025-29783

Vulnerability Analysis

This vulnerability stems from CWE-502 (Deserialization of Untrusted Data). The vLLM framework, when configured with the Mooncake integration for distributing KV cache across multiple hosts, exposes an unsafe deserialization endpoint over ZMQ (ZeroMQ) messaging protocol. The affected component binds to all network interfaces (0.0.0.0), accepting serialized data from any source within the adjacent network without proper validation or authentication.

The attack requires adjacent network access and low privileges to exploit, but successful exploitation allows the attacker to break out of the initial scope and impact other resources. This results in high impact to confidentiality, integrity, and availability of the affected systems and potentially the broader LLM infrastructure.

Root Cause

The root cause of this vulnerability is the improper handling of serialized data received over ZMQ/TCP connections. The Mooncake integration accepts and deserializes objects from network sources without validating the origin or content of the serialized payload. Python's pickle serialization, commonly used in such scenarios, is inherently unsafe when deserializing untrusted data as it can execute arbitrary code during the deserialization process.

Attack Vector

The vulnerability is exploitable from the adjacent network, meaning attackers must have access to the same network segment as the vLLM deployment. The attack complexity is low, requiring only minimal privileges. An attacker can craft a malicious serialized payload and send it to the exposed ZMQ/TCP endpoint. Upon deserialization, the malicious payload executes arbitrary code with the privileges of the vLLM process.

The attack does not require user interaction and can be performed against any vLLM deployment that has Mooncake enabled for distributed KV cache sharing. Given that LLM inference engines often run with elevated privileges to access GPU resources and sensitive model data, successful exploitation could lead to complete compromise of the inference infrastructure.

Detection Methods for CVE-2025-29783

Indicators of Compromise

• Unexpected network connections to ZMQ ports from unknown or untrusted sources within the adjacent network
• Anomalous process spawning from the vLLM process or Python interpreter
• Unusual outbound network traffic from hosts running vLLM with Mooncake integration
• Suspicious pickle deserialization activity or error logs in vLLM service logs

Detection Strategies

• Monitor network traffic for unusual ZMQ protocol communications targeting vLLM distributed hosts
• Implement application-level logging to capture deserialization events and flag unexpected payloads
• Deploy endpoint detection solutions capable of identifying pickle-based deserialization exploitation patterns
• Audit vLLM configurations to identify deployments using Mooncake integration that may be exposed

Monitoring Recommendations

• Enable verbose logging for vLLM Mooncake communications to capture incoming serialized payloads
• Implement network segmentation monitoring to detect lateral movement attempts following exploitation
• Configure alerts for new process creation events originating from vLLM service accounts
• Monitor for file system changes in directories associated with vLLM deployments

How to Mitigate CVE-2025-29783

Immediate Actions Required

• Upgrade vLLM to version 0.8.0 or later immediately
• Audit all vLLM deployments to identify instances using Mooncake integration
• Implement network segmentation to restrict access to ZMQ/TCP endpoints used by Mooncake
• Review network access controls to ensure only authorized hosts can communicate with distributed vLLM nodes

Patch Information

The vulnerability has been addressed in vLLM version 0.8.0. The fix was implemented through pull request #14228 with the specific commit 288ca110f68d23909728627d3100e5a8db820aa2. Organizations should upgrade to the patched version as soon as possible. For more details, refer to the GitHub Security Advisory GHSA-x3m8-f7g5-qhm7.

Workarounds

• Disable Mooncake integration if not required for production operations until patching is complete
• Implement strict network access controls limiting ZMQ/TCP access to trusted internal hosts only
• Deploy firewall rules blocking external access to ports used by vLLM Mooncake communications
• Consider running vLLM in an isolated network segment with no direct external connectivity

# Example: Restrict ZMQ port access using iptables (adjust port number as needed)
# Only allow connections from trusted internal network
iptables -A INPUT -p tcp --dport <zmq_port> -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport <zmq_port> -j DROP