CVE-2025-61620

Published Oct 7, 2025

vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server

Summary

A resource-exhaustion (denial-of-service) vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the chat_template and chat_template_kwargs parameters. If an attacker can supply these parameters to the API, they can cause a service outage by exhausting CPU and/or memory resources.

Details

When using an LLM as a chat model, the conversation history must be rendered into a text input for the model. In hf/transformer, this rendering is performed using a Jinja template. The OpenAI-Compatible Server launched by vllm serve exposes a chat_template parameter that lets users specify that template. In addition, the server accepts a chat_template_kwargs parameter to pass extra keyword arguments to the rendering function.

Because Jinja templates support programming-language-like constructs (loops, nested iterations, etc.), a crafted template can consume extremely large amounts of CPU and memory and thereby trigger a denial-of-service condition.

Importantly, simply forbidding the chat_template parameter does not fully mitigate the issue. The implementation constructs a dictionary of keyword arguments for apply_hf_chat_template and then updates that dictionary with the user-supplied chat_template_kwargs via dict.update. Since dict.update can overwrite existing keys, an attacker can place a chat_template key inside chat_template_kwargs to replace the template that will be used by apply_hf_chat_template.

# vllm/entrypoints/openai/serving_engine.py#L794-L816
_chat_template_kwargs: dict[str, Any] = dict(
    chat_template=chat_template,
    add_generation_prompt=add_generation_prompt,
    continue_final_message=continue_final_message,
    tools=tool_dicts,
    documents=documents,
)
_chat_template_kwargs.update(chat_template_kwargs or {})

request_prompt: Union[str, list[int]]
if isinstance(tokenizer, MistralTokenizer):
    ...
else:
    request_prompt = apply_hf_chat_template(
        tokenizer=tokenizer,
        conversation=conversation,
        model_config=model_config,
        **_chat_template_kwargs,
    )

Impact

If an OpenAI-Compatible Server exposes endpoints that accept chat_template or chat_template_kwargs from untrusted clients, an attacker can submit a malicious Jinja template (directly or by overriding chat_template inside chat_template_kwargs) that consumes excessive CPU and/or memory. This can result in a resource-exhaustion denial-of-service that renders the server unresponsive to legitimate requests.

Fixes

https://github.com/vllm-project/vllm/pull/25794

Technologies

Vllm Vllm

Weaknesses

CWE-770CWE-20CWE-400

CVSS scores

v3.16.5CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

MITRE ATT&CK

Inferred

Where this vulnerability fits in an attack

ReconImpact

Initial Access Impact

T1190Exploit Public-Facing Application T1498.001Direct Network Flood T1498.002Reflection Amplification T1499Endpoint Denial of Service T1499.001OS Exhaustion Flood T1499.002Service Exhaustion Flood T1499.003Application Exhaustion Flood

References

Potentially impacted assets

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Google Brotli decoder - Remote Code Execution via compressed requests (Exploitation Check)

This control is the second step of the Patrowl response to CVE-2026-111497 (Node2Shell). It complements the first control of 22 October 2026, which focused on the Node.js runtime, and does not replace it. Why a second control CVE-2026-111497 is an out-of-bounds write in the Google Brotli decoder, the library that decompresses Brotli ("br") data. It was first published as a Node.js vulnerability, because Node.js bundles this decoder. Later publications showed that the flaw is in the library itself. Every product that embeds an affected version of the decoder is concerned, whatever its language or vendor. A public proof of concept demonstrates code execution. Any server that decompresses request bodies sent with "Content-Encoding: br" exposes the decoder to unauthenticated attackers. Known cases include Express 5 and NestJS 11 on Node.js, Koa and Fastify setups, Bun, Envoy and Istio with request decompression enabled, and any application or reverse proxy that decodes Brotli request bodies with an affected library. The flaw also works in the other direction. A malicious server can compromise an unpatched client that decodes a Brotli response, for example Node.js fetch(), Python clients (requests, httpx, aiohttp, Scrapy) with the Brotli or brotlicffi package, or libcurl builds with Brotli support. What this control checks Patrowl sends small, valid Brotli-compressed requests to each asset and observes whether the server decodes them. The check does not depend on the technology: it detects the behaviour, whatever the stack behind it. These requests are what any legitimate client could send. They never reach the vulnerable code path and cannot affect the availability of the service. How to read the result Potentially impacted means the asset decodes Brotli request bodies: the attack surface is exposed. It does not confirm that the asset is vulnerable. The version of the Brotli decoder in use is not visible from the outside. This check covers applications that parse request bodies on their root path. A Not impacted result does not rule out exposure on other routes, nor on the client side. Checking the versions on the servers remains the only exhaustive verification. Affected and fixed versions Google Brotli 1.0.3 to 1.2.0 are affected, fixed in 1.0.10, 1.1.1 and 1.2.1. Fixed releases of the main products embedding it: Node.js 22.23.4, 24.22.1 and 26.12.1 (end-of-life lines 20.x and older and 25.x receive no fix), Bun 1.4.4, Envoy 1.37.8, 1.38.6, 1.39.3 and 1.40.0, Istio 1.29.9, 1.30.6 and 1.31.2, Python Brotli 1.2.1 and brotlicffi 1.2.1.0. Products that link against the system library depend on the Brotli package of the Linux distribution. Recommendations Update every component that embeds the Brotli decoder (only real fix). Start with the assets flagged by this control. Restart the processes after the update. Rebuild containers, single-executable applications and appliances that bundle an affected runtime or library. Reduce the exposure until the update is deployed. Reject requests with "Content-Encoding: br" at the reverse proxy, load balancer or WAF, case-insensitively and including lists such as "gzip, br". On Envoy and Istio, disable request decompression for Brotli. Review outbound calls. List the features that fetch content from servers you do not control, and patch the services that host them first.