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VulnerabilityPublished Updated

CVE-2025-62426: vllm · Unthrottled Resource Allocation

Affects vllmSource CVE Database V5

0

Not known to be exploited · CVSS 6.5 Medium : upgrade to 0.11.1 when convenient.

Data as of 26 Oct 2026, 15:59 UTC

CVSS 3.1 base score
6.5Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.5% chance of exploitation in 30 days – higher than 45% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.11.1
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 21 Nov 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

Upgrade vllm-project/vllm to 0.11.1.

Affected software

ProductAffectedFixed in
GitHubvllm-project/vllmpkg:github/vllm-project/vllmOther threatsAffected
  • <0.11.1 >=0.5.5
Fixed in
  • 0.11.1

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

AI-suggested

To mitigate this vulnerability, European organizations should promptly upgrade all vllm deployments to version 0.11.1 or later, where the issue is patched. Implement strict input validation and sanitization for the chat_template_kwargs parameter to prevent malformed or malicious payloads from triggering resource exhaustion. Deploy rate limiting and throttling mechanisms on the affected API endpoints (/v1/chat/completions and /tokenize) to limit the impact of any single client or request. Monitor API usage patterns for anomalies indicative of exploitation attempts, such as unusually long processing times or repeated requests with suspicious parameters. Restrict access to the API endpoints to trusted users and systems, enforcing strong authentication and authorization controls. Employ network segmentation and firewall rules to limit exposure of the inference engine to only necessary clients. Regularly audit and review logs for signs of abuse or performance degradation. Finally, maintain an incident response plan that includes procedures for mitigating denial of service conditions affecting AI services.

Description

vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before 0.11.1, the /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests. This issue has been patched in version 0.11.1.

References

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