CVE-2026-115918

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 chat_template_kwargs[chat_template] when no server template is configured. ChatParams.get_apply_chat_template_kwargs() preserves that value without enforcing trust_request_chat_template, 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.

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 chat_template_kwargs[chat_template] when no server template is configured. ChatParams.get_apply_chat_template_kwargs() preserves that value without enforcing trust_request_chat_template, 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.

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:

{"chat_template_kwargs": {"chat_template": "<attacker Jinja 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:

>= 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.

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 chat_template_kwargs[chat_template] when no server template is configured. ChatParams.get_apply_chat_template_kwargs() preserves that value without enforcing trust_request_chat_template, 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.

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 chat_template_kwargs[chat_template] when no server template is configured. ChatParams.get_apply_chat_template_kwargs() preserves that value without enforcing trust_request_chat_template, 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.

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:

{"chat_template_kwargs": {"chat_template": "<attacker Jinja 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:

>= 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.

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 chat_template_kwargs[chat_template] when no server template is configured. ChatParams.get_apply_chat_template_kwargs() preserves that value without enforcing trust_request_chat_template, 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.