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VulnerabilityPublished

CVE-2025-46570: vllm · Timing Side Channel

Affects vllmSource CVE Database V5

0

Not known to be exploited · CVSS 2.6 Low : upgrade to 0.9.0 when convenient.

Data as of 26 Oct 2026, 14:05 UTC

CVSS 3.1 base score
2.6Low
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.4% chance of exploitation in 30 days – higher than 36% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.9.0
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 29 May 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 0.9.0.

Affected software

ProductAffectedFixed in
GitHubvllm-project/vllmpkg:github/vllm-project/vllmOther threatsAffected
  • <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

European organizations should upgrade all vLLM deployments to version 0.9.0 or later to eliminate this timing side-channel vulnerability. For environments where immediate upgrading is not feasible, consider implementing network-level controls to restrict access to the inference service, limiting exposure to untrusted users. Monitoring and logging of inference request patterns may help detect anomalous probing attempts exploiting timing differences. Additionally, organizations can introduce artificial delays or jitter in response times to obscure timing discrepancies, though this may impact performance. For highly sensitive use cases, isolating inference workloads and employing strict authentication and authorization mechanisms can further reduce risk. Finally, maintain awareness of vendor updates and security advisories related to vLLM and similar LLM serving engines.

Description

vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). These timing differences caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0.

References

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