CVE-2025-46570

LOW
2025-05-29 [email protected]
2.6
CVSS 3.1

CVSS Vector

CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N
Attack Vector
Network
Attack Complexity
High
Privileges Required
Low
User Interaction
Required
Scope
Unchanged
Confidentiality
Low
Integrity
None
Availability
None

Lifecycle Timeline

3
Analysis Generated
Mar 28, 2026 - 18:44 vuln.today
Patch Released
Mar 28, 2026 - 18:44 nvd
Patch available
CVE Published
May 29, 2025 - 17:15 nvd
LOW 2.6

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.

Analysis

vLLM is an inference and serving engine for large language models (LLMs). Rated low severity (CVSS 2.6), this vulnerability is remotely exploitable.

Technical Context

This vulnerability is classified under CWE-208. 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. Affected products include: Vllm. Version information: version 0.9.0.

Affected Products

Vllm.

Remediation

A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.

Priority Score

13
Low Medium High Critical
KEV: 0
EPSS: +0.2
CVSS: +13
POC: 0

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CVE-2025-46570 vulnerability details – vuln.today

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