vllm has 63 CVEs on record between 2024 and 2026. Cadence is steady at roughly 12 per quarter. The busiest recent month was June 2026 with 11. The median CVSS is 6.5 (medium), with 9 rated critical. None have a confirmed exploitation report. The dominant weakness classes are CWE-770 (7) and CWE-400 (5).

CVEs per month

Last 12 months, by publish date

1025/101125/111225/120126/010226/020326/030426/040526/050626/060726/070826/080926/09

Exploited share: 0% vs 1% corpus
Median CVSS: 6.5
Publish → KEV: —
Last 90 days: 12 prev 18

Weakness classes

Products

vllm 63

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Total CVEs

Critical

CISA KEV

Exploited

Worst active — by depth score

vllm vulnerabilities

CVEs affecting vllm, newest first. Open any entry for full detail, references, and exploit status.

63 CVEsRSS

CVE-2026-27893High· 8.8

6mo ago

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.18.0, two model implementation files hardcode `trust_remote_code=True` when loading sub-components, bypassing the…

▾ Twilightvllm · vllmEPSS 1.8%via NVD

CVE-2026-25960Medium· 5.4

6mo ago

vLLM has SSRF Protection Bypass

▾ Sunlitvllm · vllmEPSS 0.72%via OSV

CVE-2026-22778Critical· 9.8PoC

7mo ago

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs). From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a hea…

▾ Abyssalvllm · vllmEPSS 10%via NVD

CVE-2026-24779High· 7.1

8mo ago

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. …

▾ Twilightvllm · vllmEPSS 0.59%via NVD

CVE-2026-22807High· 8.8PoC

8mo ago

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_…

▾ Midnightvllm · vllmEPSS 0.83%via NVD

CVE-2026-22773Medium· 6.5

8mo ago

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a s…

▾ Sunlitvllm · vllmEPSS 0.45%via NVD

CVE-2025-62372Medium· 6.5

10mo ago

vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs

▾ Sunlitvllm · vllmEPSS 0.38%via OSV

CVE-2025-62164High· 8.8

10mo ago

vLLM deserialization vulnerability leading to DoS and potential RCE

▾ Twilightvllm · vllmEPSS 0.93%via OSV

CVE-2025-61620Medium· 6.5

11mo ago

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

▾ Sunlitvllm · vllmvia OSV

CVE-2025-6242High· 7.1

CVEs per month

Weakness classes

Products

Worst active — by depth score

vllm vulnerabilities

vLLM is an inference and serving engine for large language models (LLMs)

vLLM has SSRF Protection Bypass

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs)

vLLM is an inference and serving engine for large language models (LLMs)

vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs

vLLM deserialization vulnerability leading to DoS and potential RCE

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

vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class

vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder

vLLM has a Regular Expression Denial of Service (ReDoS, Exponential Complexity) Vulnerability in `pythonic_tool_parser.py`

vLLM has a Weakness in MultiModalHasher Image Hashing Implementation

vLLM allows clients to crash the openai server with invalid regex

vLLM DOS: Remotely kill vllm over http with invalid JSON schema

Potential Timing Side-Channel Vulnerability in vLLM’s Chunk-Based Prefix Caching

vLLM Tool Schema allows DoS via Malformed pattern and type Fields

vLLM vulnerable to Regular Expression Denial of Service

vLLM Allows Remote Code Execution via PyNcclPipe Communication Service

Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration

vLLM Vulnerable to Remote Code Execution via Mooncake Integration

phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service

Data exposure via ZeroMQ on multi-node vLLM deployment

CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0

vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object

vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints

vLLM Deserialization of Untrusted Data vulnerability

vLLM Allows Remote Code Execution via Mooncake Integration

vLLM denial of service via outlines unbounded cache on disk

vLLM uses Python 3.12 built-in hash() which leads to predictable hash collisions in prefix cache

phi4mm: Quadratic Time Complexity in Input Token Processing leads to denial of service