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vLLM Allows Remote Code Execution via Mooncake Integration

Critical severity GitHub Reviewed Published Mar 19, 2025 in vllm-project/vllm • Updated Mar 22, 2025

Package

pip vllm (pip)

Affected versions

>= 0.6.5, < 0.8.0

Patched versions

0.8.0

Description

Summary

When vLLM is configured to use Mooncake, unsafe deserialization exposed directly over ZMQ/TCP will allow attackers to execute remote code on distributed hosts.

Details

  1. Pickle deserialization vulnerabilities are well documented.
  2. The mooncake pipe is exposed over the network (by design to enable disaggregated prefilling across distributed environments) using ZMQ over TCP, greatly increasing exploitability. Further, the mooncake integration opens these sockets listening on all interfaces on the host, meaning it can not be configured to only use a private, trusted network.

Only sender_socket and receiver_ack are allowed to be accessed publicly, while the data actually decompressed by pickle.loads() comes from recv_bytes. Its interface is defined as self.receiver_socket.connect(f\"tcp://{d_host}:{d_rank_offset + 1}\"), where d_host is decode_host, a locally defined address 192.168.0.139,from mooncake.json (https://github.com/kvcache-ai/Mooncake/blob/main/doc/en/vllm-integration-v0.2.md?plain=1#L36).

  1. The root problem is recv_tensor() calls _recv_impl which passes the raw network bytes to pickle.loads(). Additionally, it does not appear that there are any controls (network, authentication, etc) to prevent arbitrary users from sending this payload to the affected service.

Impact

This is a remote code execution vulnerability impacting any deployments using Mooncake to distribute KV across distributed hosts.

Remediation

This issue is resolved by vllm-project/vllm#14228

References

@russellb russellb published to vllm-project/vllm Mar 19, 2025
Published to the GitHub Advisory Database Mar 19, 2025
Reviewed Mar 19, 2025
Published by the National Vulnerability Database Mar 19, 2025
Last updated Mar 22, 2025

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v3 base metrics

Attack vector
Adjacent
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Changed
Confidentiality
High
Integrity
High
Availability
High

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(66th percentile)

Weaknesses

CVE ID

CVE-2025-29783

GHSA ID

GHSA-x3m8-f7g5-qhm7

Source code

Credits

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