CVE-2025-32444
- EPSS 2.27%
- Veröffentlicht 30.04.2025 00:25:00
- Zuletzt bearbeitet 28.05.2025 19:12:58
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 serializat...
CVE-2025-46560
- EPSS 0.11%
- Veröffentlicht 30.04.2025 00:24:53
- Zuletzt bearbeitet 28.05.2025 19:15:56
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. T...
CVE-2025-30202
- EPSS 0.15%
- Veröffentlicht 30.04.2025 00:24:45
- Zuletzt bearbeitet 14.05.2025 19:59:42
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...
CVE-2024-11041
- EPSS 1.18%
- Veröffentlicht 20.03.2025 10:10:40
- Zuletzt bearbeitet 31.07.2025 14:48:32
vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. The function uses pickle.loads to parse received sockets directly, leading to a remote code execution vulnerability. An attacker can exploit this by...
- EPSS 1.61%
- Veröffentlicht 19.03.2025 15:33:28
- Zuletzt bearbeitet 01.07.2025 20:52:17
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. When vLLM is configured to use Mooncake, unsafe deserialization exposed directly over ZMQ/TCP on all network interfaces will allow attackers to execute remote code ...
CVE-2025-29770
- EPSS 0.32%
- Veröffentlicht 19.03.2025 15:31:00
- Zuletzt bearbeitet 31.07.2025 15:58:58
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...
CVE-2025-25183
- EPSS 0.06%
- Veröffentlicht 07.02.2025 20:15:34
- Zuletzt bearbeitet 01.07.2025 20:58:00
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 behavio...
CVE-2025-24357
- EPSS 0.27%
- Veröffentlicht 27.01.2025 18:15:41
- Zuletzt bearbeitet 27.06.2025 19:30:59
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 paramet...