CVE-2025-46570: vllm · Timing Side Channel
Affects vllmSource CVE Database V5
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
- 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:NExploitation evidence
Disclosed: evidence found.
CVE published 29 May 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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
| Product | Affected | Fixed in |
|---|---|---|
GitHubvllm-project/vllmpkg:github/vllm-project/vllmOther threats | Affected
| Fixed in
|
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.