PYSEC-2026-4349: vLLM: Responses API bypasses trust_request_chat_template for chat_template_kwargs

Published Oct 26, 2026
·
Updated

Summary

vLLM's /v1/responses endpoint accepts request-level chattemplatekwargs, but does not apply the --trust-request-chat-template gate that protects Chat Completions, render, tokenize, and pooling paths.

When the server is run with the documented default trustrequestchattemplate=False and no explicit --chat-template, a Responses request can pass:

json {"chattemplatekwargs": {"chattemplate": "<attacker Jinja template>"}}

That key is preserved by ChatParams.getapplychattemplatekwargs() and then bound as the chattemplate parameter to the shared HF renderer. Prior vLLM advisories establish that request-controlled Jinja chat templates can cause CPU/memory exhaustion. This report covers the current-head Responses sibling path missed by the earlier fixes.

Technical Details

The shared OpenAI frontend has a validation helper that rejects request-supplied chat templates unless the trust flag is enabled. It explicitly checks both top-level chattemplate and chattemplatekwargs["chattemplate"].

Responses is a separate serving path:

- vllm/entrypoints/openai/responses/protocol.py exposes chattemplatekwargs. - ResponsesRequest.buildchatparams() merges request kwargs. - OpenAIServingResponses.effectivechattemplatekwargs() uses those kwargs. - ChatParams.getapplychattemplatekwargs() preserves a request chattemplate key when the server chat template is None. - vllm/renderers/hf.py unpacks the result into safeapplychattemplate.

There is no equivalent trust gate in vllm/entrypoints/openai/responses/serving.py.

Impact

An authenticated caller who can submit /v1/responses requests can supply a request-controlled Jinja chat template despite the trust flag being disabled. The expected fixed behavior is the same as Chat Completions: reject chattemplatekwargs["chattemplate"] unless the server was started with --trust-request-chat-template.

Suggested Fix

- Add trustrequestchattemplate to OpenAIServingResponses. - Pass args.trustrequestchattemplate when constructing the Responses serving object. - Reject request.chattemplatekwargs["chattemplate"] before rendering when trust is false. - Keep ordinary kwargs such as enablethinking working. - Add regression tests for default-deny and opt-in-allow behavior on /v1/responses.

Affected Package/Versions

Source sweep found the affected shape in:

- v0.16.0 - v0.18.0 - v0.20.0 - v0.21.0 - v0.22.0 - v0.22.1 - v0.23.0 - current checkout 470229c37efaf69c86e8bc97482b0b1ff7551c65 - current origin/main b3f0a0a0df76dda92ec4b2c9335f77e84adad911

Suggested affected range:

text = 0.16.0, <= 0.23.0

Appendix: Duplicate Research

This is related to, but distinct from:

- GHSA-6fvq-23cw-5628 / CVE-2025-61620 - GHSA-69j4-grxj-j64p / CVE-2025-62426

Those advisories establish the security impact of request-controlled Jinja templates and chattemplatekwargs, but they cover older Chat Completions/tokenize implementations. This report covers a current /v1/responses sibling path that still lacks the trust gate.

Local prior submissions this report..029 do not cover this root cause.

This advisory was fixed in MR50300.

Other sources

vLLM is an inference and serving engine for large language models. From 0.16.0 until 0.32.0, the /v1/responses endpoint allows an authenticated caller to place an attacker-controlled template in chattemplatekwargs[chattemplate] when no server template is configured. ChatParams.getapplychattemplatekwargs() preserves that value without enforcing trustrequestchattemplate, so the request can bypass the intended trust policy and cause CPU or memory exhaustion through Jinja rendering. This issue is fixed in version 0.32.0.

— OSV

Affected Software

1 affected componentFixes available
pip/vllm>=0.16.0<0.32.0
0.32.0

Remediation

Recommended actions to resolve this vulnerability, in priority order.

  1. Upgrade

    Upgrade pip/vllm to a version that resolves this vulnerability.

    Fixed in 0.32.0
  2. Upgrade

    Upgrade vllm to a version that resolves this vulnerability.

    Fixed in 0.32.0Patch MR50300

Event History

Oct 26, 2026
CVE Published
via OSV·05:21 PM
Data Sourced
via OSV·05:21 PM
DescriptionSeverityWeakness
Advisory Published
via OSV·06:17 PM
Data Sourced
via OSV·06:17 PM
Affected Software

Contact

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