GitHub Advisoryadvisory · PyPI
GHSA-x567-49g5-p4cf vLLM: Responses API bypasses trust_request_chat_template for chat_template_kwargs
MEDIUM
Official page ↗
- Aliases
- CVE-2026-115918
- Ecosystem
- PyPI
- Packages
- vllm
- Severity
- Moderate
- CVSS vector
- CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Weaknesses
- CWE-400, CWE-770
- Reviewed
- 2026-10-28
Affected packages
• PyPI vllm: >= 0.16.0, < 0.32.0 → fixed in 0.32.0
Description
Summary
vLLM's `/v1/responses` endpoint accepts request-level `chat_template_kwargs`, 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 `trust_request_chat_template=False` and no explicit `--chat-template`, a Responses request can pass:
```json
{"chat_template_kwargs": {"chat_template": ""}}
```
That key is preserved by `ChatParams.get_apply_chat_template_kwargs()` and then bound as the `chat_template` 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 `chat_template` and `chat_template_kwargs["chat_template"]`.
Responses is a separate serving path:
• `vllm/entrypoints/openai/responses/protocol.py` exposes `chat_template_kwargs`.
• `ResponsesRequest.build_chat_params()` merges request kwargs.
• `OpenAIServingResponses._effective_chat_template_kwargs()` uses those kwargs.
• `ChatParams.get_apply_chat_template_kwargs()` preserves a request `chat_template` key when the server chat template is `None`.
• `vllm/renderers/hf.py` unpacks the result into `safe_apply_chat_template`.
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 `chat_template_kwargs["chat_template"]` unless the server was started with `--trust-request-chat-template`.
Suggested Fix
• Add `trust_request_chat_template` to `OpenAIServingResponses`.
• Pass `args.trust_request_chat_template` when constructing the Responses serving object.
• Reject `request.chat_template_kwargs["chat_template"]` before rendering when trust is false.
• Keep ordinary kwargs such as `enable_thinking` 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 `chat_template_kwargs`, 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.
Related
Alias · CVE (1)
- CVE-2026-115918 vLLM is an inference and serving engine for large language models. vulnerability
References
- https://github.com/vllm-project/vllm/security/advisories/GHSA-x567-49g5-p4cf web
- https://nvd.nist.gov/vuln/detail/CVE-2026-115918 advisory
- https://github.com/vllm-project/vllm/pull/50300 web
- https://github.com/vllm-project/vllm/commit/cff08b461e086523cee8bba5bd3c66d302625134 web
- https://github.com/vllm-project/vllm package
- https://github.com/vllm-project/vllm/releases/tag/v0.32.0 web