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VulnerabilityPublished Updated

CVE-2025-71379: vllm · ReDoS

Affects vllmSource CVE Database V5

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Not known to be exploited · CVSS 5.3 Medium : upgrade to 0.9.0 when convenient.

Data as of 8 Nov 2026, 14:20 UTC

CVSS 4.0 base score
5.3Medium
How severe the flaw is on the 0–10 CVSS scale.

Thin evidence

One scorer or none
CVSS scores by source
CNA (VulnCheck)CVSS 4.05.3
CNA (VulnCheck)CVSS 3.14.3
CISA SSVC
Exploitation
No evidence of active exploitation and no public proof of concept
Automatable
Attackers cannot reliably automate steps 1–4 of the kill chain
Technical impact
Limited control over the software or limited information exposure

CISA SSVC assessment, 2026-06-22.

Patch
Available – fixed in 0.9.0
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 20 Jun 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: 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 8 Nov 2026.

What to do

Upgrade vllm to 0.9.0.

Affected software

ProductAffectedFixed in
vllmvllmAffected
  • >=0.6.3 <0.9.0
Fixed in
  • 0.9.0
PyPIvllmpkg:pypi/vllmOther threatsAffected
  • >=0.6.3 <0.9.0
Fixed in
  • 0.9.0

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

AI-suggested

Upgrade vLLM to version 0.9.0 or later, where these ReDoS vulnerabilities have been fixed. No additional mitigation steps are indicated.

Description

vLLM versions from 0.6.3 up to but not including 0.9.0 contain multiple regular expression denial of service (ReDoS) vulnerabilities. These vulnerabilities arise from inefficient regex patterns in several components, including vllm/lora/utils.py, the phi4mini tool parser, and the OpenAI-compatible serving chat endpoint. Crafted input exploiting nested or repeated structures can cause excessive CPU usage and degrade performance, leading to denial of service. The issue is fixed in version 0.9.0.

References

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