CVE-2025-32444 vulnerabilities

Vulnerabilities for packages: py3.10-vllm-cuda-12.6...

GHSA-VC6M-HM49-G9QG vulnerabilities

Vulnerabilities for packages: py3.10-vllm-cuda-11.8...

GHSA-HJ4W-HM2G-P6W5 vulnerabilities

Vulnerabilities for packages: py3.10-vllm-cuda-12.6...

CVE-2025-30202 vulnerabilities

Vulnerabilities for packages: py3.10-vllm-cuda-11.8...

CVE-2025-30202

A flaw was found in vLLM's multi-node setup, which exposes sensitive data over a ZeroMQ XPUB socket bound to all interfaces. This vulnerability allows unauthorized clients to intercept and read internal communications if they can access the network. Mitigation Mitigation for this issue is either...

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 vulnerab...

CVE-2025-32444

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 vulnerab...

CVE-2025-30202

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-no...

Deserialization of Untrusted Data

Overview 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 Mooncake integration. An attacker can execute arbitrary code by sending malicious payloads to a pickle base...

CVE-2025-32444

vLLM (0.6.5–0.8.4) with mooncake integration is vulnerable to remote code execution due to pickle-based serialization over unsecured ZeroMQ sockets that were listening on all interfaces. This could be exploited remotely; non-mooncake deployments are not affected. The issue is fixed in vLLM 0.8.5....

CVE-2025-32444 vLLM Vulnerable to Remote Code Execution via Mooncake Integration

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 vulnerab...

Inefficient Algorithmic Complexity

Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Inefficient Algorithmic Complexity through the inputprocessorforphi4mm function. An attacker can cause the application to consume excessive resource...

CVE-2025-46560 vLLM phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens...

CVE-2025-46560 vLLM phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens...

CVE-2025-46560 vLLM phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens...

CVE-2025-30202

CVE-2025-30202 affects vLLM versions 0.5.2 up to 0.8.4 (prior to 0.8.5) in multi-node deployments. The root cause is an XPUB ZeroMQ socket bound to ALL interfaces on the primary host used for tensor parallelism, which can be accessed by any client with network access. This allows potential data e...

CVE-2025-30202 Data exposure via ZeroMQ on multi-node vLLM deployment

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-no...

CVE-2025-30202 Data exposure via ZeroMQ on multi-node vLLM deployment

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-no...

CVE-2025-30202 Data exposure via ZeroMQ on multi-node vLLM deployment

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-no...

vLLM 代码问题漏洞

vLLM is a vLLM open source high throughput and memory efficient inference and service engine for LLM. A code issue vulnerability exists in vLLM versions prior to 0.6.5 to 0.8.5, which stems from the use of pickle-based serialization and could lead to remote code execution...