CVE-2026-34756: vLLM Denial of Service Vulnerability

MODERATE
Advisory Identifier: GHSA-3mwp-wvh9-7528
CVSS 6.5
CWE-770
Published
Advisory Summary

CVE-2026-34756 is a MODERATE (CVSS 6.5) denial of service vulnerability (CWE-770: Allocation of Resources Without Limits or Throttling) affecting vLLM libraries in the PyPI ecosystem. The issue involves unauthenticated oom denial of service via unbounded `n` parameter in openai api server. Remediation is available by upgrading affected packages to version 0.19.0.

CVE-2026-34756 at a Glance

CVE Identifier CVE-2026-34756
GHSA Identifier GHSA-3mwp-wvh9-7528
Severity MODERATE
CVSS v3.1 Base Score 6.5
CWE Classification CWE-770: Allocation of Resources Without Limits or Throttling
Affected Product vLLM
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 6.5

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

Attack Vector Network (AV:N)
Attack Complexity Low (AC:L)
Privileges Required Low (PR:L)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality None (C:N)
Integrity None (I:N)
Availability High (A:H)

How to Fix CVE-2026-34756

To remediate this vulnerability, upgrade all affected vLLM dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

vllm fixed: 0.19.0
pip install vllm>=0.19.0
Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release:

pip show vllm

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2026-34756, inspect your installed packages using pip:

pip show vllm

Analyze the terminal output to locate any version of vLLM in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 0.19.0), your application is affected.

Note: The pip show vllm command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-770: Allocation of Resources Without Limits or Throttling, where the product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated. In vLLM, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

### Summary A Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the `n` parameter in the `ChatCompletionRequest` and `CompletionRequest` Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large `n` value. This completely blocks the Python `asyncio` event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. ### Details The root cause of this vulnerability lies in the missing upper bound checks across the request parsing and asynchronous scheduling layers: 1. **Protocol Layer:** In `vllm/entrypoints/openai/chat_completion/protocol.py`, the `n` parameter is defined simply as an integer without any `pydantic.Field` constraints for an upper bound. ```python class ChatCompletionRequest(OpenAIBaseModel): # Ordered by official OpenAI API documentation # https://platform.openai.com/docs/api/reference/chat/create messages: list[ChatCompletionMessageParam] model: str | None = None frequency_penalty: float | None = 0.0 logit_bias: dict[str, float] | None = None logprobs: bool | None = False top_logprobs: int | None = 0 max_tokens: int | None = Field( default=None, deprecated="max_tokens is deprecated in favor of " "the max_completion_tokens field", ) max_completion_tokens: int | None = None n: int | None = 1 presence_penalty: float | None = 0.0 ``` 1. **SamplingParams Layer (Incomplete Validation):** When the API request is converted to internal `SamplingParams` in `vllm/sampling_params.py`, the `_verify_args` method only checks the lower bound (`self.n < 1`), entirely omitting an upper bounds check. ```python def _verify_args(self) -> None: if not isinstance(self.n, int): raise ValueError(f"n must be an int, but is of type {type(self.n)}") if self.n < 1: raise ValueError(f"n must be at least 1, got {self.n}.") ``` 1. **Engine Layer (The OOM Trigger):** When the malicious request reaches the core engine (`vllm/v1/engine/async_llm.py`), the engine attempts to fan out the request `n` times to generate identical independent sequences within a synchronous loop. ```python # Fan out child requests (for n>1). parent_request = ParentRequest(request) for idx in range(parent_params.n): request_id, child_params = parent_request.get_child_info(idx) child_request = request if idx == parent_params.n - 1 else copy(request) child_request.request_id = request_id child_request.sampling_params = child_params await self._add_request( child_request, prompt_text, parent_request, idx, queue ) return queue ``` Because Python's `asyncio` runs on a single thread and event loop, this monolithic `for`-loop monopolizes the CPU thread. The server stops responding to all other connections (including liveness probes). Simultaneously, the memory allocator is overwhelmed by cloning millions of request object instances via `copy(request)`, driving the host's Resident Set Size (RSS) up by gigabytes per second until the OS `OOM-killer` terminates the vLLM process. ### Impact **Vulnerability Type:** Resource Exhaustion / Denial of Service **Impacted Parties:** - Any individual or organization hosting a public-facing vLLM API server (`vllm.entrypoints.openai.api_server`), which happens to be the primary entrypoint for OpenAI-compatible setups. - SaaS / AI-as-a-Service platforms acting as reverse proxies sitting in front of vLLM without strict HTTP body payload validation or rate limitations. Because this vulnerability exploits the control plane rather than the data plane, an unauthenticated remote attacker can achieve a high success rate in taking down production inference hosts with a single HTTP request. This effectively circumvents any hardware-level capacity planning and conventional bandwidth stress limitations.

CVE-2026-34756 Affected vLLM Versions

1 component(s)
Fixed Versions:
0.19.0
Product / Package Ecosystem Affected Versions Fixed Version
vllm
PyPI
≥ 0.1.0
< 0.19.0
0.19.0

References

Frequently Asked Questions

What is CVE-2026-34756?
CVE-2026-34756 is a security vulnerability in vLLM involving Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server under CWE-770: Allocation of Resources Without Limits or Throttling.
Which vLLM versions are affected?
Affected versions include releases prior to 0.19.0 across affected packages: vllm.
What version fixes CVE-2026-34756?
Upgrading to 0.19.0 (or the latest patched release for your respective branch) resolves the vulnerability.
Is vllm affected?
Yes, vllm is affected in releases prior to 0.19.0. Upgrade your dependency to 0.19.0 or later.
What is the CVSS score of CVE-2026-34756?
CVE-2026-34756 has a CVSS v3.1 base score of 6.5 (MODERATE) with vector string CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.
What CWE is associated with CVE-2026-34756?
CVE-2026-34756 is classified under CWE-770: Allocation of Resources Without Limits or Throttling (The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.).
How can I check PyPI dependencies for CVE-2026-34756?
Run the dependency tree command: 'pip show vllm'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
{
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  "id" : "GHSA-3mwp-wvh9-7528",
  "modified" : "2026-07-17T16:18:45Z",
  "published" : "2026-04-03T15:35:48Z",
  "aliases" : [ "CVE-2026-34756" ],
  "summary" : "vLLM: Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server",
  "details" : "### Summary\nA Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the `n` parameter in the `ChatCompletionRequest` and `CompletionRequest` Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large `n` value. This completely blocks the Python `asyncio` event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue.\n\n### Details\nThe root cause of this vulnerability lies in the missing upper bound checks across the request parsing and asynchronous scheduling layers:\n\n1. **Protocol Layer:**\n   In `vllm/entrypoints/openai/chat_completion/protocol.py`, the `n` parameter is defined simply as an integer without any `pydantic.Field` constraints for an upper bound.\n```python\nclass ChatCompletionRequest(OpenAIBaseModel):\n    # Ordered by official OpenAI API documentation\n    # https://platform.openai.com/docs/api/reference/chat/create\n    messages: list[ChatCompletionMessageParam]\n    model: str | None = None\n    frequency_penalty: float | None = 0.0\n    logit_bias: dict[str, float] | None = None\n    logprobs: bool | None = False\n    top_logprobs: int | None = 0\n    max_tokens: int | None = Field(\n        default=None,\n        deprecated=\"max_tokens is deprecated in favor of \"\n        \"the max_completion_tokens field\",\n    )\n    max_completion_tokens: int | None = None\n    n: int | None = 1\n    presence_penalty: float | None = 0.0\n```\n\n1. **SamplingParams Layer (Incomplete Validation):**\n   When the API request is converted to internal `SamplingParams` in `vllm/sampling_params.py`, the `_verify_args` method only checks the lower bound (`self.n < 1`), entirely omitting an upper bounds check.\n```python\n    def _verify_args(self) -> None:\n        if not isinstance(self.n, int):\n            raise ValueError(f\"n must be an int, but is of type {type(self.n)}\")\n        if self.n < 1:\n            raise ValueError(f\"n must be at least 1, got {self.n}.\")\n```\n\n1. **Engine Layer (The OOM Trigger):**\n   When the malicious request reaches the core engine (`vllm/v1/engine/async_llm.py`), the engine attempts to fan out the request `n` times to generate identical independent sequences within a synchronous loop.\n```python\n        # Fan out child requests (for n>1).\n        parent_request = ParentRequest(request)\n        for idx in range(parent_params.n):\n            request_id, child_params = parent_request.get_child_info(idx)\n            child_request = request if idx == parent_params.n - 1 else copy(request)\n            child_request.request_id = request_id\n            child_request.sampling_params = child_params\n            await self._add_request(\n                child_request, prompt_text, parent_request, idx, queue\n            )\n        return queue\n```\n   Because Python's `asyncio` runs on a single thread and event loop, this monolithic `for`-loop monopolizes the CPU thread. The server stops responding to all other connections (including liveness probes). Simultaneously, the memory allocator is overwhelmed by cloning millions of request object instances via `copy(request)`, driving the host's Resident Set Size (RSS) up by gigabytes per second until the OS `OOM-killer` terminates the vLLM process.\n\n### Impact\n**Vulnerability Type:** Resource Exhaustion / Denial of Service\n\n**Impacted Parties:**\n- Any individual or organization hosting a public-facing vLLM API server (`vllm.entrypoints.openai.api_server`), which happens to be the primary entrypoint for OpenAI-compatible setups.\n- SaaS / AI-as-a-Service platforms acting as reverse proxies sitting in front of vLLM without strict HTTP body payload validation or rate limitations.\n\nBecause this vulnerability exploits the control plane rather than the data plane, an unauthenticated remote attacker can achieve a high success rate in taking down production inference hosts with a single HTTP request. This effectively circumvents any hardware-level capacity planning and conventional bandwidth stress limitations.",
  "severity" : [ {
    "type" : "CVSS_V3",
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  }, {
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    "type" : "WEB",
    "url" : "https://access.redhat.com/errata/RHSA-2026:36005"
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    "url" : "https://access.redhat.com/errata/RHSA-2026:36006"
  }, {
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    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-2298.yaml"
  }, {
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    "url" : "https://github.com/vllm-project/vllm"
  }, {
    "type" : "WEB",
    "url" : "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-34756.json"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-770" ],
    "severity" : "MODERATE",
    "github_reviewed" : true,
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    "nvd_published_at" : "2026-04-06T16:16:36Z"
  }
}