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Package Instance

Lookup for vulnerable packages by Package URL.

Purlpkg:pypi/vllm@0.5.5
Typepypi
Namespace
Namevllm
Version0.5.5
Qualifiers
Subpath
Is_vulnerabletrue
Next_non_vulnerable_version0.20.0
Latest_non_vulnerable_version0.20.0
Affected_by_vulnerabilities
0
url VCID-737m-tpkz-qffm
vulnerability_id VCID-737m-tpkz-qffm
summary vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.
references
0
reference_url https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7
reference_id
reference_type
scores
url https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7
1
reference_url https://github.com/vllm-project/vllm/pull/12621
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/12621
2
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8
fixed_packages
0
url pkg:pypi/vllm@0.7.2
purl pkg:pypi/vllm@0.7.2
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-e8w2-9rwg-u7ba
1
vulnerability VCID-fxgs-s1vm-8bez
2
vulnerability VCID-k1qz-xe9c-2bg3
3
vulnerability VCID-nctw-rz8h-f3af
4
vulnerability VCID-svzy-7pke-2bdr
5
vulnerability VCID-u659-sd9h-tkf3
6
vulnerability VCID-ugds-eqgw-fbbz
7
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.7.2
aliases CVE-2025-25183, GHSA-rm76-4mrf-v9r8, PYSEC-2025-62
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-737m-tpkz-qffm
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-f8nw-x5ug-kfh7
vulnerability_id VCID-f8nw-x5ug-kfh7
summary 
vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs
Users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct `ndim` but incorrect `shape` (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page).

The issue has existed ever since we added support for image embedding inputs, i.e. #6613 (released in v0.5.5)
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/58fab50d82838d5014f4a14d991fdb9352c9c84b
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b
2
reference_url https://github.com/vllm-project/vllm/pull/27204
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/27204
3
reference_url https://github.com/vllm-project/vllm/pull/6613
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/6613
4
reference_url https://nvd.nist.gov/vuln/detail/CVE-2025-62372
reference_id CVE-2025-62372
reference_type
scores
url https://nvd.nist.gov/vuln/detail/CVE-2025-62372
5
reference_url https://github.com/advisories/GHSA-pmqf-x6x8-p7qw
reference_id GHSA-pmqf-x6x8-p7qw
reference_type
scores
url https://github.com/advisories/GHSA-pmqf-x6x8-p7qw
6
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-pmqf-x6x8-p7qw
reference_id GHSA-pmqf-x6x8-p7qw
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-pmqf-x6x8-p7qw
fixed_packages
0
url pkg:pypi/vllm@0.11.1
purl pkg:pypi/vllm@0.11.1
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@0.11.1
aliases CVE-2025-62372, GHSA-pmqf-x6x8-p7qw
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-f8nw-x5ug-kfh7
3
url VCID-k1qz-xe9c-2bg3
vulnerability_id VCID-k1qz-xe9c-2bg3
summary vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. The outlines library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server. The affected code in vLLM is vllm/model_executor/guided_decoding/outlines_logits_processors.py, which unconditionally uses the cache from outlines. A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space. Note that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the guided_decoding_backend key of the extra_body field of the request. This issue applies only to the V0 engine and is fixed in 0.8.0.
references
0
reference_url https://github.com/vllm-project/vllm/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py
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/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py
1
reference_url https://github.com/vllm-project/vllm/pull/14837
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/pull/14837
2
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-mgrm-fgjv-mhv8
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-mgrm-fgjv-mhv8
fixed_packages
0
url pkg:pypi/vllm@0.8.0
purl pkg:pypi/vllm@0.8.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-5ec1-1h6d-tuaq
1
vulnerability VCID-e8w2-9rwg-u7ba
2
vulnerability VCID-fxgs-s1vm-8bez
3
vulnerability VCID-nctw-rz8h-f3af
4
vulnerability VCID-qake-z4ec-wkdu
5
vulnerability VCID-svzy-7pke-2bdr
6
vulnerability VCID-ugds-eqgw-fbbz
7
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.8.0
aliases CVE-2025-29770, GHSA-mgrm-fgjv-mhv8, PYSEC-2025-223
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-k1qz-xe9c-2bg3
4
url VCID-nhwm-kq25-t3dt
vulnerability_id VCID-nhwm-kq25-t3dt
summary 
vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`
The /v1/chat/completions and /tokenize endpoints allow a `chat_template_kwargs` request parameter that is used in the code before it is properly validated against the chat template. With the right `chat_template_kwargs` parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests
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/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610
2
reference_url https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814
3
reference_url https://github.com/vllm-project/vllm/commit/3ada34f9cb4d1af763fdfa3b481862a93eb6bd2b
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/commit/3ada34f9cb4d1af763fdfa3b481862a93eb6bd2b
4
reference_url https://github.com/vllm-project/vllm/pull/27205
reference_id
reference_type
scores
url https://github.com/vllm-project/vllm/pull/27205
5
reference_url https://nvd.nist.gov/vuln/detail/CVE-2025-62426
reference_id CVE-2025-62426
reference_type
scores
url https://nvd.nist.gov/vuln/detail/CVE-2025-62426
6
reference_url https://github.com/advisories/GHSA-69j4-grxj-j64p
reference_id GHSA-69j4-grxj-j64p
reference_type
scores
url https://github.com/advisories/GHSA-69j4-grxj-j64p
7
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-69j4-grxj-j64p
reference_id GHSA-69j4-grxj-j64p
reference_type
scores
url https://github.com/vllm-project/vllm/security/advisories/GHSA-69j4-grxj-j64p
fixed_packages
0
url pkg:pypi/vllm@0.11.1
purl pkg:pypi/vllm@0.11.1
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@0.11.1
aliases CVE-2025-62426, GHSA-69j4-grxj-j64p
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-nhwm-kq25-t3dt
5
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
6
url VCID-w9kt-yaqy-47fb
vulnerability_id VCID-w9kt-yaqy-47fb
summary vLLM is a library for LLM inference and serving. vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It uses the torch.load function and the weights_only parameter defaults to False. When torch.load loads malicious pickle data, it will execute arbitrary code during unpickling. This vulnerability is fixed in v0.7.0.
references
0
reference_url https://github.com/vllm-project/vllm/commit/d3d6bb13fb62da3234addf6574922a4ec0513d04
reference_id
reference_type
scores
0
value 8.8
scoring_system cvssv3.1
scoring_elements CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
url https://github.com/vllm-project/vllm/commit/d3d6bb13fb62da3234addf6574922a4ec0513d04
1
reference_url https://github.com/vllm-project/vllm/pull/12366
reference_id
reference_type
scores
0
value 8.8
scoring_system cvssv3.1
scoring_elements CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
url https://github.com/vllm-project/vllm/pull/12366
2
reference_url https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54
reference_id
reference_type
scores
0
value 8.8
scoring_system cvssv3.1
scoring_elements CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
url https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54
3
reference_url https://pytorch.org/docs/stable/generated/torch.load.html
reference_id
reference_type
scores
0
value 8.8
scoring_system cvssv3.1
scoring_elements CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
url https://pytorch.org/docs/stable/generated/torch.load.html
fixed_packages
0
url pkg:pypi/vllm@0.7.0
purl pkg:pypi/vllm@0.7.0
is_vulnerable true
affected_by_vulnerabilities
0
vulnerability VCID-737m-tpkz-qffm
1
vulnerability VCID-e8w2-9rwg-u7ba
2
vulnerability VCID-fxgs-s1vm-8bez
3
vulnerability VCID-k1qz-xe9c-2bg3
4
vulnerability VCID-nctw-rz8h-f3af
5
vulnerability VCID-svzy-7pke-2bdr
6
vulnerability VCID-u659-sd9h-tkf3
7
vulnerability VCID-ugds-eqgw-fbbz
8
vulnerability VCID-za3a-c9m1-jqgz
resource_url http://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.7.0
aliases CVE-2025-24357, GHSA-rh4j-5rhw-hr54, PYSEC-2025-58
risk_score null
exploitability null
weighted_severity null
resource_url http://public2.vulnerablecode.io/vulnerabilities/VCID-w9kt-yaqy-47fb
Fixing_vulnerabilities
Risk_scorenull
Resource_urlhttp://public2.vulnerablecode.io/packages/pkg:pypi/vllm@0.5.5