5.5

CVSS Score

4.0

-

CVSS Score

Basic Information

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CVE-2026-9540

CVE-2026-9540: vllm has Improper Resource Shutdown or Release

A vulnerability was identified in vllm-project vllm 0.19.0. This issue affects some unknown processing of the component OpenAI-compatible Serving Path. Such manipulation leads to denial of service. It is possible to launch the attack remotely. The exploit is publicly available and might be used. The pull request to fix this issue awaits acceptance.

(GitHub Advisory)

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CVE-2026-9540

CVE-2026-9540:

5.5

CVSS Score

4.0

-

CVSS Score

Basic Information

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Technical Details

SSVC / BOD 26-04 ↗

Exploitation pocAutomatable YesTech Impact Partial

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Package Name	Ecosystem	Vulnerable Versions	First Patched Version
vllm	pip	<= 0.19.0

Technical Details

Vulnerability Intelligence
Miggo AI

Root Cause Analysis

The vulnerability analysis focused on the provided pull request, which directly addresses the denial of service issue. The PR description clearly explains that the root cause is the vLLM V1 scheduler's failure to account for the computational overhead of requests with high n and logprobs values. By examining the commit diffs, I identified that the core of the fix lies in the vllm/v1/core/sched/scheduler.py file. A new function, _get_request_logprobs_cost, was introduced to quantify the computational cost of a request. The main scheduling function, Scheduler.schedule, was then modified to use this cost calculation. It now maintains a budget for the total logprobs cost within a batch and will not add new requests if they would exceed this budget. This prevents a single computationally-intensive request from blocking an entire batch of otherwise lightweight requests. Therefore, the Scheduler.schedule function is the vulnerable function, as it previously lacked this essential resource management, making it susceptible to a denial-of-service attack.

Vulnerable functions

Scheduler.schedule

vllm/v1/core/sched/scheduler.py

The `schedule` function in the `Scheduler` class was vulnerable to a denial of service. It would batch incoming requests without considering the computational cost associated with generating a large number of log probabilities (`logprobs`). A single request with a high number of completions (`n`) and a high `logprobs` value could monopolize compute resources during the synchronous decoding step. This would dramatically increase the processing time for the entire batch, causing a significant delay (high Time-To-First-Token) for all other co-scheduled requests, effectively denying them service. The vulnerability was fixed by introducing a budgeting system that calculates the 'cost' of each request's logprobs and prevents overly expensive requests from being batched with others.

Vulnerability Intelligence
Miggo AI

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WAF Protection Rules

WAF Rule

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Reasoning

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