vllm 0.18.1

Direct Vulnerabilities

Known vulnerabilities in the vllm package. This does not include vulnerabilities belonging to this package’s dependencies.

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Vulnerability	Vulnerable Version
H Allocation of Resources Without Limits or Throttling vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the lack of a frame count limit in the `load_base64` function when processing `video/jpeg` base64 data. An attacker can exhaust system memory and cause a server crash by submitting a request containing a large number of comma-separated base64-encoded JPEG frames. How to fix Allocation of Resources Without Limits or Throttling? Upgrade `vllm` to version 0.19.0 or higher.	[0.7.0,0.19.0)
M Server-side Request Forgery (SSRF) vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) via the `download_bytes_from_url` function. An attacker can cause the server to make arbitrary HTTP or HTTPS requests to internal or external resources by supplying a crafted `file_url` value in batch input JSON, potentially accessing sensitive internal services or causing denial of service. How to fix Server-side Request Forgery (SSRF)? A fix was pushed into the `master` branch but not yet published.	[0.16.0,)
H Allocation of Resources Without Limits or Throttling vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the lack of upper bound validation on the `n` parameter in the request handling process. An attacker can cause the server to exhaust system memory and become unresponsive by sending a single HTTP request with an extremely large `n` value, resulting in the allocation of millions of request object copies and monopolizing the event loop. How to fix Allocation of Resources Without Limits or Throttling? Upgrade `vllm` to version 0.19.0 or higher.	[0.1.0,0.19.0)
H Deserialization of Untrusted Data vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the `SUB` ZeroMQ socket, where the deserialization is performed using the unsafe `pickle` library. An attacker on the same cluster can execute arbitrary code on the remote machine by sending maliciously crafted deserialized payloads. Note The V0 engine is off by default since v0.8.0, and the V1 engine is not affected. Due to the V0 engine's deprecated status and the invasive nature of a fix, the developers recommend ensuring a secure network environment if the V0 engine with multi-host tensor parallelism is still in use. How to fix Deserialization of Untrusted Data? There is no fixed version for `vllm`.	[0.5.2,)
H Deserialization of Untrusted Data vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Deserialization of Untrusted Data in the `MessageQueue.dequeue()` API function. An attacker can execute arbitrary code by sending a malicious payload to the message queue. How to fix Deserialization of Untrusted Data? There is no fixed version for `vllm`.	[0,)

Vulnerability

Vulnerable Version

Allocation of Resources Without Limits or Throttling

vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the lack of a frame count limit in the load_base64 function when processing video/jpeg base64 data. An attacker can exhaust system memory and cause a server crash by submitting a request containing a large number of comma-separated base64-encoded JPEG frames.

How to fix Allocation of Resources Without Limits or Throttling?

Upgrade vllm to version 0.19.0 or higher.

[0.7.0,0.19.0)

Server-side Request Forgery (SSRF)

vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs

Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) via the download_bytes_from_url function. An attacker can cause the server to make arbitrary HTTP or HTTPS requests to internal or external resources by supplying a crafted file_url value in batch input JSON, potentially accessing sensitive internal services or causing denial of service.

How to fix Server-side Request Forgery (SSRF)?

A fix was pushed into the master branch but not yet published.

[0.16.0,)

Allocation of Resources Without Limits or Throttling

vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the lack of upper bound validation on the n parameter in the request handling process. An attacker can cause the server to exhaust system memory and become unresponsive by sending a single HTTP request with an extremely large n value, resulting in the allocation of millions of request object copies and monopolizing the event loop.

How to fix Allocation of Resources Without Limits or Throttling?

Upgrade vllm to version 0.19.0 or higher.

[0.1.0,0.19.0)

Deserialization of Untrusted Data

vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs

Affected versions of this package are vulnerable to Deserialization of Untrusted Data via the SUB ZeroMQ socket, where the deserialization is performed using the unsafe pickle library. An attacker on the same cluster can execute arbitrary code on the remote machine by sending maliciously crafted deserialized payloads.

Note The V0 engine is off by default since v0.8.0, and the V1 engine is not affected. Due to the V0 engine's deprecated status and the invasive nature of a fix, the developers recommend ensuring a secure network environment if the V0 engine with multi-host tensor parallelism is still in use.

How to fix Deserialization of Untrusted Data?

There is no fixed version for vllm.

[0.5.2,)

Deserialization of Untrusted Data

vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs

Affected versions of this package are vulnerable to Deserialization of Untrusted Data in the MessageQueue.dequeue() API function. An attacker can execute arbitrary code by sending a malicious payload to the message queue.

How to fix Deserialization of Untrusted Data?

There is no fixed version for vllm.

[0,)

vllm@0.18.1

A high-throughput and memory-efficient inference and serving engine for LLMs

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Direct Vulnerabilities

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