CVE-2026-115918
MEDIUM
6,5
Source: [email protected]
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
DraftCommon 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
770
Allocation of Resources Without Limits or Throttling
IncompleteCommon 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
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