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PYSEC-2026-2298
Vulnerability from pysec - Published: 2026-04-06 16:16 - Updated: 2026-07-13 05:52vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, 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. This vulnerability is fixed in 0.19.0.
| Name | purl | vllm | pkg:pypi/vllm |
|---|
{
"affected": [
{
"ecosystem_specific": {},
"package": {
"ecosystem": "PyPI",
"name": "vllm",
"purl": "pkg:pypi/vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0.1.0"
},
{
"fixed": "0.19.0"
}
],
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}
],
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"0.1.1",
"0.1.2",
"0.1.3",
"0.1.4",
"0.1.5",
"0.1.6",
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"0.10.0",
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"0.10.1.1",
"0.10.2",
"0.11.0",
"0.11.1",
"0.11.2",
"0.12.0",
"0.13.0",
"0.14.0",
"0.14.1",
"0.15.0",
"0.15.1",
"0.16.0",
"0.17.0",
"0.17.1",
"0.18.0",
"0.18.1",
"0.2.0",
"0.2.1",
"0.2.1.post1",
"0.2.2",
"0.2.3",
"0.2.4",
"0.2.5",
"0.2.6",
"0.2.7",
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"GHSA-3mwp-wvh9-7528"
],
"details": "vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, 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. This vulnerability is fixed in 0.19.0.",
"id": "PYSEC-2026-2298",
"modified": "2026-07-13T05:52:25.124808Z",
"published": "2026-04-06T16:16:36.610Z",
"references": [
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"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2026-34756"
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},
{
"type": "FIX",
"url": "https://github.com/vllm-project/vllm/commit/b111f8a61f100fdca08706f41f29ef3548de7380"
},
{
"type": "FIX",
"url": "https://github.com/vllm-project/vllm/pull/37952"
},
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"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
CVE-2026-34756 (GCVE-0-2026-34756)
Vulnerability from cvelistv5 – Published: 2026-04-06 15:40 – Updated: 2026-08-25 12:05| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.1.0, < 0.19.0
|
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951012 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951244 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151769 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151840 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151771 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151774 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787310717 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253912 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253989 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787254059 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787243961 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253774 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787244006 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
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"time": "2026-04-06T16:03:45.222Z",
"value": "Reported to Red Hat."
},
{
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"time": "2026-04-06T15:40:03.448Z",
"value": "Made public."
}
],
"title": "vllm: vLLM: Denial of Service via excessively large \u0027n\u0027 parameter in OpenAI-compatible API",
"workarounds": [
{
"lang": "en",
"value": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability."
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"value": "vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, 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. This vulnerability is fixed in 0.19.0."
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"baseScore": 6.5,
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
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"scope": "UNCHANGED",
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GHSA-3MWP-WVH9-7528
Vulnerability from github – Published: 2026-04-03 15:35 – Updated: 2026-07-17 16:18Summary
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:
- Protocol Layer:
In
vllm/entrypoints/openai/chat_completion/protocol.py, thenparameter is defined simply as an integer without anypydantic.Fieldconstraints for an upper bound.
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
- SamplingParams Layer (Incomplete Validation):
When the API request is converted to internal
SamplingParamsinvllm/sampling_params.py, the_verify_argsmethod only checks the lower bound (self.n < 1), entirely omitting an upper bounds check.
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}.")
- 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 requestntimes to generate identical independent sequences within a synchronous loop.
# 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.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0.1.0"
},
{
"fixed": "0.19.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-34756"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": true,
"github_reviewed_at": "2026-04-03T15:35:48Z",
"nvd_published_at": "2026-04-06T16:16:36Z",
"severity": "MODERATE"
},
"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 \u003c 1`), entirely omitting an upper bounds check.\n```python\n def _verify_args(self) -\u003e 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 \u003c 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\u003e1).\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\u0027s `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\u0027s 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.",
"id": "GHSA-3mwp-wvh9-7528",
"modified": "2026-07-17T16:18:45Z",
"published": "2026-04-03T15:35:48Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528"
},
{
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-34756"
},
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},
{
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"url": "https://access.redhat.com/security/cve/CVE-2026-34756"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455425"
},
{
"type": "WEB",
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},
{
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}
],
"summary": "vLLM: Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server"
}
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.