9.8
CVE-2025-47277
- EPSS 0.93%
- Veröffentlicht 20.05.2025 17:32:27
- Zuletzt bearbeitet 13.08.2025 16:35:57
- Quelle security-advisories@github.com
- CVE-Watchlists
- Unerledigt
vLLM Allows Remote Code Execution via PyNcclPipe Communication Service
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.
| Typ | Quelle | Score | Percentile |
|---|---|---|---|
| EPSS | FIRST.org | 0.93% | 0.558 |
| Quelle | Base Score | Exploit Score | Impact Score | Vector String |
|---|---|---|---|---|
| security-advisories@github.com | 9.8 | 3.9 | 5.9 |
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
|
CWE-502 Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
https://github.com/vllm-project/vllm/security/advisories/GHSA-hjq4-87xh-g4fv
https://github.com/vllm-project/vllm/pull/15988
https://github.com/vllm-project/vllm/commit/0d6e187e88874c39cda7409cf673f9e6546893e7
https://docs.vllm.ai/en/latest/deployment/security.html