Django REST framework

Package Instance

Lookup for vulnerable packages by Package URL.

Purlpkg:pypi/vllm@0.9.0
Typepypi
Namespace
Namevllm
Version0.9.0
Qualifiers
Subpath
Is_vulnerabletrue
Next_non_vulnerable_version0.20.0
Latest_non_vulnerable_version0.20.0
Affected_by_vulnerabilities
0
url VCID-nctw-rz8h-f3af
vulnerability_id VCID-nctw-rz8h-f3af
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm
1
reference_url https://github.com/vllm-project/vllm/commit/0ec84221718d920c3f46da879cc354f94b8fb59e
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/0ec84221718d920c3f46da879cc354f94b8fb59e
2
reference_url https://github.com/vllm-project/vllm/pull/29881
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/29881
3
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-grg2-63fw-f2qr
reference_id
reference_type
scores
0
value 7.5
scoring_system cvssv3.1
scoring_elements CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
url https://github.com/vllm-project/vllm/security/advisories/GHSA-grg2-63fw-f2qr
4
reference_url https://nvd.nist.gov/vuln/detail/CVE-2026-22773
reference_id CVE-2026-22773
reference_type
scores
url https://nvd.nist.gov/vuln/detail/CVE-2026-22773
5
reference_url https://github.com/advisories/GHSA-grg2-63fw-f2qr
reference_id GHSA-grg2-63fw-f2qr
reference_type
scores
url https://github.com/advisories/GHSA-grg2-63fw-f2qr
fixed_packages
0
url pkg:pypi/vllm@0.12.0
purl pkg:pypi/vllm@0.12.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.12.0
aliases CVE-2026-22773, GHSA-grg2-63fw-f2qr, PYSEC-2026-143
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-nctw-rz8h-f3af
1
url VCID-za3a-c9m1-jqgz
vulnerability_id VCID-za3a-c9m1-jqgz
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p
reference_id
reference_type
scores
0
value 6.5
scoring_system cvssv3.1
scoring_elements CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
url https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p
fixed_packages
0
url pkg:pypi/vllm@0.19.0
purl pkg:pypi/vllm@0.19.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-jzjy-kj6h-4bas
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.19.0
aliases CVE-2026-34755, GHSA-pq5c-rjhq-qp7p, PYSEC-2026-144
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-za3a-c9m1-jqgz
Fixing_vulnerabilities
0
url VCID-5ec1-1h6d-tuaq
vulnerability_id VCID-5ec1-1h6d-tuaq
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm/commit/08bf7840780980c7568c573c70a6a8db94fd45ff
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/08bf7840780980c7568c573c70a6a8db94fd45ff
1
reference_url https://github.com/vllm-project/vllm/issues/17313
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/issues/17313
2
reference_url https://github.com/vllm-project/vllm/pull/17623
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/17623
3
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-9hcf-v7m4-6m2j
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-9hcf-v7m4-6m2j
fixed_packages
0
url pkg:pypi/vllm@0.9.0
purl pkg:pypi/vllm@0.9.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-nctw-rz8h-f3af
1
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.9.0
aliases CVE-2025-48943, GHSA-9hcf-v7m4-6m2j, PYSEC-2025-55
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-5ec1-1h6d-tuaq
1
url VCID-e8w2-9rwg-u7ba
vulnerability_id VCID-e8w2-9rwg-u7ba
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm/commit/77073c77bc2006eb80ea6d5128f076f5e6c6f54f
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/77073c77bc2006eb80ea6d5128f076f5e6c6f54f
1
reference_url https://github.com/vllm-project/vllm/pull/17045
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/17045
2
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-4qjh-9fv9-r85r
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-4qjh-9fv9-r85r
fixed_packages
0
url pkg:pypi/vllm@0.9.0
purl pkg:pypi/vllm@0.9.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-nctw-rz8h-f3af
1
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.9.0
aliases CVE-2025-46570, GHSA-4qjh-9fv9-r85r, PYSEC-2025-53
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-e8w2-9rwg-u7ba
2
url VCID-qake-z4ec-wkdu
vulnerability_id VCID-qake-z4ec-wkdu
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm/commit/08bf7840780980c7568c573c70a6a8db94fd45ff
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/08bf7840780980c7568c573c70a6a8db94fd45ff
1
reference_url https://github.com/vllm-project/vllm/issues/17248
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/issues/17248
2
reference_url https://github.com/vllm-project/vllm/pull/17623
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/17623
3
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-6qc9-v4r8-22xg
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-6qc9-v4r8-22xg
fixed_packages
0
url pkg:pypi/vllm@0.9.0
purl pkg:pypi/vllm@0.9.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-nctw-rz8h-f3af
1
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.9.0
aliases CVE-2025-48942, GHSA-6qc9-v4r8-22xg, PYSEC-2025-54
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-qake-z4ec-wkdu
3
url VCID-svzy-7pke-2bdr
vulnerability_id VCID-svzy-7pke-2bdr
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848
1
reference_url https://github.com/vllm-project/vllm/pull/17378
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/17378
2
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6
fixed_packages
0
url pkg:pypi/vllm@0.9.0
purl pkg:pypi/vllm@0.9.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-nctw-rz8h-f3af
1
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.9.0
aliases CVE-2025-46722, GHSA-c65p-x677-fgj6, PYSEC-2025-43
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-svzy-7pke-2bdr
4
url VCID-ugds-eqgw-fbbz
vulnerability_id VCID-ugds-eqgw-fbbz
summary 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.
references
0
reference_url https://github.com/vllm-project/vllm/commit/4fc1bf813ad80172c1db31264beaef7d93fe0601
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/4fc1bf813ad80172c1db31264beaef7d93fe0601
1
reference_url https://github.com/vllm-project/vllm/pull/18454
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/18454
2
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-w6q7-j642-7c25
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-w6q7-j642-7c25
fixed_packages
0
url pkg:pypi/vllm@0.9.0
purl pkg:pypi/vllm@0.9.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-nctw-rz8h-f3af
1
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.9.0
aliases CVE-2025-48887, PYSEC-2025-50
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-ugds-eqgw-fbbz
Risk_scorenull
Resource_urlhttp://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.9.0