CVE-2026-115918

Published: Ott 26, 2026 Last Modified: Ott 26, 2026
ExploitDB:
Other exploit source:
Google Dorks:
MEDIUM 6,5
Attack Vector: network
Attack Complexity: low
Privileges Required: low
User Interaction: none
Scope: unchanged
Confidentiality: none
Integrity: none
Availability: high

Description

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.

400

Uncontrolled Resource Consumption

Draft
Common Consequences
Security Scopes Affected:
Availability Access Control Other
Potential Impacts:
Dos: Crash, Exit, Or Restart Dos: Resource Consumption (Cpu) Dos: Resource Consumption (Memory) Dos: Resource Consumption (Other) Bypass Protection Mechanism Other
Applicable Platforms
Technologies: Not Technology-Specific, AI/ML
View CWE Details
770

Allocation of Resources Without Limits or Throttling

Incomplete
Common Consequences
Security Scopes Affected:
Availability
Potential Impacts:
Dos: Resource Consumption (Cpu) Dos: Resource Consumption (Memory) Dos: Resource Consumption (Other)
Applicable Platforms
All platforms may be affected
View CWE Details
https://github.com/vllm-project/vllm/commit/cff08b461e086523cee8bba5bd3c66d3026…
https://github.com/vllm-project/vllm/pull/50300
https://github.com/vllm-project/vllm/releases/tag/v0.32.0
https://github.com/vllm-project/vllm/security/advisories/GHSA-x567-49g5-p4cf