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VulnerabilityPublished Updated

CVE-2024-9053: vllm-project/vllm · Deserialization of Untrusted Data

Affects vllm-project/vllmSource CVE Database V5

0

Not known to be exploited · CVSS 9.8 Critical.

Data as of 26 Oct 2026, 12:54 UTC

CVSS 3.0 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
1.3% chance of exploitation in 30 days – higher than 71% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 20 Mar 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

Affected software

ProductAffectedFixed in
GitHubvllm-project/vllmpkg:github/vllm-project/vllmOther threatsAffected
  • =0.6.0
Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

  1. Immediate mitigation should focus on restricting network access to the vllm RPC server, limiting exposure to trusted internal networks only.
  2. Implement strict input validation and filtering to prevent untrusted or malformed pickle data from reaching the deserialization function.
  3. Replace or modify the use of cloudpickle.loads() with safer deserialization alternatives or sandboxed environments that limit code execution capabilities.
  4. Monitor network traffic and logs for anomalous or unexpected RPC calls that could indicate exploitation attempts.
  5. Apply any forthcoming patches or updates from the vllm-project as soon as they become available.
  6. Consider deploying runtime application self-protection (RASP) or endpoint detection and response (EDR) tools that can detect and block suspicious deserialization activities.
  7. Educate developers and security teams about the risks of unsafe deserialization and enforce secure coding practices in AI/ML projects.
  8. If patching is delayed, consider isolating vllm instances in containerized or virtualized environments with minimal privileges to limit potential damage.

Description

vllm-project vllm version 0.6.0 contains a vulnerability in the AsyncEngineRPCServer() RPC server entrypoints. The core functionality run_server_loop() calls the function _make_handler_coro(), which directly uses cloudpickle.loads() on received messages without any sanitization. This can result in remote code execution by deserializing malicious pickle data.

References

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