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| purl | pkg:pypi/vllm@0.8.2 |
| Vulnerability | Summary | Fixed by |
|---|---|---|
|
VCID-5ec1-1h6d-tuaq
Aliases: CVE-2025-48943 GHSA-9hcf-v7m4-6m2j PYSEC-2025-55 |
vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue. |
Affected by 2 other vulnerabilities. |
|
VCID-e8w2-9rwg-u7ba
Aliases: CVE-2025-46570 GHSA-4qjh-9fv9-r85r PYSEC-2025-53 |
vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). These timing differences caused by matching chunks are significant enough to be recognized and exploited. This issue has been patched in version 0.9.0. |
Affected by 2 other vulnerabilities. |
|
VCID-fxgs-s1vm-8bez
Aliases: CVE-2025-32444 PYSEC-2025-42 |
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.6.5 and prior to 0.8.5, having vLLM integration with mooncake, are vulnerable to remote code execution due to using pickle based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack. vLLM instances that do not make use of the mooncake integration are not vulnerable. This issue has been patched in version 0.8.5. |
Affected by 7 other vulnerabilities. |
|
VCID-nctw-rz8h-f3af
Aliases: CVE-2026-22773 GHSA-grg2-63fw-f2qr PYSEC-2026-143 |
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 specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0. |
Affected by 1 other vulnerability. |
|
VCID-qake-z4ec-wkdu
Aliases: CVE-2025-48942 GHSA-6qc9-v4r8-22xg PYSEC-2025-54 |
vLLM is an inference and serving engine for large language models (LLMs). In versions 0.8.0 up to but excluding 0.9.0, hitting the /v1/completions API with a invalid json_schema as a Guided Param kills the vllm server. This vulnerability is similar GHSA-9hcf-v7m4-6m2j/CVE-2025-48943, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue. |
Affected by 2 other vulnerabilities. |
|
VCID-svzy-7pke-2bdr
Aliases: CVE-2025-46722 GHSA-c65p-x677-fgj6 PYSEC-2025-43 |
vLLM is an inference and serving engine for large language models (LLMs). In versions starting from 0.7.0 to before 0.9.0, in the file vllm/multimodal/hasher.py, the MultiModalHasher class has a security and data integrity issue in its image hashing method. Currently, it serializes PIL.Image.Image objects using only obj.tobytes(), which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks. This issue has been patched in version 0.9.0. |
Affected by 2 other vulnerabilities. |
|
VCID-ugds-eqgw-fbbz
Aliases: CVE-2025-48887 PYSEC-2025-50 |
vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file `vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py` of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue. |
Affected by 2 other vulnerabilities. |
|
VCID-za3a-c9m1-jqgz
Aliases: CVE-2026-34755 GHSA-pq5c-rjhq-qp7p PYSEC-2026-144 |
vLLM is an inference and serving engine for large language models (LLMs). From 0.7.0 to before 0.19.0, the VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The num_frames parameter (default: 32), which is enforced by the load_bytes() code path, is completely bypassed in the video/jpeg base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM. This vulnerability is fixed in 0.19.0. |
Affected by 1 other vulnerability. |
| Vulnerability | Summary | Aliases |
|---|---|---|
| This package is not known to fix vulnerabilities. | ||