CVE-2025-46570: Vllm Potential Timing Side-Channel Vulnerability Vulnerability

LOW
Advisory Identifier: GHSA-4qjh-9fv9-r85r
CVSS 2.6
CWE-208
Published
Advisory Summary

CVE-2025-46570 is a LOW (CVSS 2.6) potential timing side-channel vulnerability vulnerability (CWE-208: Observable Timing Discrepancy) affecting Vllm libraries in the PyPI ecosystem. The issue involves potential timing side-channel vulnerability in vllm’s chunk-based prefix caching. Remediation is available by upgrading affected packages to version 0.9.0.

CVE-2025-46570 at a Glance

CVE Identifier CVE-2025-46570
GHSA Identifier GHSA-4qjh-9fv9-r85r
Severity LOW
CVSS v3.1 Base Score 2.6
CWE Classification CWE-208: Observable Timing Discrepancy
Affected Product Vllm
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 2.6

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N).

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

How to Fix CVE-2025-46570

To remediate this vulnerability, upgrade all affected Vllm dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

vllm fixed: 0.9.0
pip install vllm>=0.9.0
Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release:

pip show vllm

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2025-46570, inspect your installed packages using pip:

pip show vllm

Analyze the terminal output to locate any version of Vllm in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 0.9.0), your application is affected.

Note: The pip show vllm command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-208: Observable Timing Discrepancy, where two separate operations in a product require different amounts of time to complete, in a way that is observable to an actor and reveals security-relevant information about the state of the product, such as whether a particular operation was successful or not. In Vllm, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

This issue arises from the prefix caching mechanism, which may expose the system to a timing side-channel attack. ## Description 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). Our tests revealed that the timing differences caused by matching chunks are significant enough to be recognized and exploited. For instance, if the victim has submitted a sensitive prompt or if a valuable system prompt has been cached, an attacker sharing the same backend could attempt to guess the victim's input. By measuring the TTFT based on prefix matches, the attacker could verify if their guess is correct, leading to potential leakage of private information. Unlike token-by-token sharing mechanisms, vLLM’s chunk-based approach (PageAttention) processes tokens in larger units (chunks). In our tests, with chunk_size=2, the timing differences became noticeable enough to allow attackers to infer whether portions of their input match the victim's prompt at the chunk level. ## Environment - GPU: NVIDIA A100 (40G) - CUDA: 11.8 - PyTorch: 2.3.1 - OS: Ubuntu 18.04 - vLLM: v0.5.1 Configuration: We launched vLLM using the default settings and adjusted chunk_size=2 to evaluate the TTFT. ## Leakage We conducted our tests using LLaMA2-70B-GPTQ on a single device. We analyzed the timing differences when prompts shared prefixes of 2 chunks, and plotted the corresponding ROC curves. Our results suggest that timing differences can be reliably used to distinguish prefix matches, demonstrating a potential side-channel vulnerability. <img src="https://github.com/user-attachments/assets/db3491e9-02b7-424c-9b6d-56f553b39f2f" alt="roc_curves_combined_block_2" width="400"/> ## Results In our experiment, we analyzed the response time differences between cache hits and misses in vLLM's PageAttention mechanism. Using ROC curve analysis to assess the distinguishability of these timing differences, we observed the following results: - With a 1-token prefix, the ROC curve yielded an AUC value of 0.571, indicating that even with a short prefix, an attacker can reasonably distinguish between cache hits and misses based on response times. - When the prefix length increases to 8 tokens, the AUC value rises significantly to 0.99, showing that the attacker can almost perfectly identify cache hits with a longer prefix. ## Fixes * https://github.com/vllm-project/vllm/pull/17045

CVE-2025-46570 Affected Vllm Versions

1 component(s)
Fixed Versions:
0.9.0
Product / Package Ecosystem Affected Versions Fixed Version
vllm
PyPI
< 0.9.0
0.9.0

References

Frequently Asked Questions

What is CVE-2025-46570?
CVE-2025-46570 is a security vulnerability in Vllm involving Potential Timing Side-Channel Vulnerability in vLLM’s Chunk-Based Prefix Caching under CWE-208: Observable Timing Discrepancy.
Which Vllm versions are affected?
Affected versions include releases prior to 0.9.0 across affected packages: vllm.
What version fixes CVE-2025-46570?
Upgrading to 0.9.0 (or the latest patched release for your respective branch) resolves the vulnerability.
Is vllm affected?
Yes, vllm is affected in releases prior to 0.9.0. Upgrade your dependency to 0.9.0 or later.
What is the CVSS score of CVE-2025-46570?
CVE-2025-46570 has a CVSS v3.1 base score of 2.6 (LOW) with vector string CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N.
What CWE is associated with CVE-2025-46570?
CVE-2025-46570 is classified under CWE-208: Observable Timing Discrepancy (Two separate operations in a product require different amounts of time to complete, in a way that is observable to an actor and reveals security-relevant information about the state of the product, such as whether a particular operation was successful or not.).
How can I check PyPI dependencies for CVE-2025-46570?
Run the dependency tree command: 'pip show vllm'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-4qjh-9fv9-r85r",
  "modified" : "2025-06-27T21:06:46Z",
  "published" : "2025-05-28T18:02:24Z",
  "aliases" : [ "CVE-2025-46570" ],
  "summary" : "Potential Timing Side-Channel Vulnerability in vLLM’s Chunk-Based Prefix Caching",
  "details" : "This issue arises from the prefix caching mechanism, which may expose the system to a timing side-channel attack.\n\n## Description\nWhen 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). Our tests revealed that the timing differences caused by matching chunks are significant enough to be recognized and exploited.\n\nFor instance, if the victim has submitted a sensitive prompt or if a valuable system prompt has been cached, an attacker sharing the same backend could attempt to guess the victim's input. By measuring the TTFT based on prefix matches, the attacker could verify if their guess is correct, leading to potential leakage of private information.\n\nUnlike token-by-token sharing mechanisms, vLLM’s chunk-based approach (PageAttention) processes tokens in larger units (chunks). In our tests, with chunk_size=2, the timing differences became noticeable enough to allow attackers to infer whether portions of their input match the victim's prompt at the chunk level.\n\n## Environment\n\n- GPU: NVIDIA A100 (40G)\n- CUDA: 11.8\n- PyTorch: 2.3.1\n- OS: Ubuntu 18.04\n- vLLM: v0.5.1\nConfiguration: We launched vLLM using the default settings and adjusted chunk_size=2 to evaluate the TTFT.\n\n## Leakage\nWe conducted our tests using LLaMA2-70B-GPTQ on a single device. We analyzed the timing differences when prompts shared prefixes of 2 chunks, and plotted the corresponding ROC curves. Our results suggest that timing differences can be reliably used to distinguish prefix matches, demonstrating a potential side-channel vulnerability.\n<img src=\"https://github.com/user-attachments/assets/db3491e9-02b7-424c-9b6d-56f553b39f2f\" alt=\"roc_curves_combined_block_2\" width=\"400\"/>\n\n\n## Results\nIn our experiment, we analyzed the response time differences between cache hits and misses in vLLM's PageAttention mechanism. Using ROC curve analysis to assess the distinguishability of these timing differences, we observed the following results:\n- With a 1-token prefix, the ROC curve yielded an AUC value of 0.571, indicating that even with a short prefix, an attacker can reasonably distinguish between cache hits and misses based on response times.\n- When the prefix length increases to 8 tokens, the AUC value rises significantly to 0.99, showing that the attacker can almost perfectly identify cache hits with a longer prefix.\n\n## Fixes\n\n* https://github.com/vllm-project/vllm/pull/17045",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "vllm"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "0.9.0"
      } ]
    } ]
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/vllm-project/vllm/security/advisories/GHSA-4qjh-9fv9-r85r"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2025-46570"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/vllm-project/vllm/pull/17045"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/vllm-project/vllm/commit/77073c77bc2006eb80ea6d5128f076f5e6c6f54f"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-53.yaml"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/vllm-project/vllm"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-208" ],
    "severity" : "LOW",
    "github_reviewed" : true,
    "github_reviewed_at" : "2025-05-28T18:02:24Z",
    "nvd_published_at" : "2025-05-29T17:15:21Z"
  }
}