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CVE-2026-22773VulnerabilityPublished Updated

CVE-2026-22773: vllm · Unthrottled Resource Allocation

Affects vllmSource CVE Database V5

0

Not known to be exploited · CVSS 6.5 Medium : upgrade to 0.12.0 when convenient.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score
6.5Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 38% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.12.0
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 10 Jan 2026

  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 2026-10-25.

What to do

Upgrade vllm and vllm-project/vllm to 0.12.0.

Affected software

ProductAffectedFixed in
vllm-projectvllmAffected
  • >=0.6.4 <0.12.0
Fixed in
  • 0.12.0
GitHubvllm-project/vllmpkg:github/vllm-project/vllmOther threatsAffected
  • >=0.6.4 <0.12.0
Fixed in
  • 0.12.0

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

AI-suggested

1. Upgrade all vLLM deployments to version 0.12.0 or later, where the vulnerability is patched. 2. Implement strict input validation on all image inputs, rejecting malformed or suspiciously small images such as 1x1 pixels before processing. 3. Deploy rate limiting and throttling mechanisms on inference request endpoints to prevent resource exhaustion from repeated crafted inputs. 4. Monitor inference engine logs and alerts for abnormal crashes or runtime errors indicative of exploitation attempts. 5. Isolate AI inference services in segmented network zones to limit exposure and impact of potential DoS attacks. 6. Conduct regular security assessments and penetration testing focused on AI model serving infrastructure. 7. Maintain an incident response plan that includes recovery procedures for AI service disruptions. 8. Engage with the vLLM project community for updates and security advisories.

Description

vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.

References

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