CVE-2025-47277 | vLLM Allows Remote Code Execution via PyNcclPipe Communication Service

Exp

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.

Published: 2025-05-20 Last update: 2025-08-13 Assigner: [email protected] Source: [email protected]

Conclusion & alert: CVE-2025-47277 is rated High Exploit Risk (80.9/100): CVSS Critical severity, with medium exploitation likelihood (EPSS 0.86%). Core evidence: 1 public exploit reference(s) are indexed (Exploit-DB). Mandatory action: Public exploits are available—assess exposure, apply mitigations, and prioritize patching.

Risk is dynamic; we continuously reassess and refresh what is shown on this page as upstream context changes.

Public exploit references (Exploit-DB) for CVE-2025-47277

EDB-ID Source Kind Published Link
nvd_ref exploit_tag Exploit-DB ↗

Exploit prediction scoring system (EPSS) score for CVE-2025-47277

EPSS lead: Daily EPSS estimates relative likelihood of exploitation; percentile ranks this CVE among scored vulnerabilities (higher = more severe relative rank).

# Date Old EPSS score New EPSS score Delta (New - Old)
1 2026-03-16 0.37% 0.86% +0.49%
2 2026-03-02 0.56% 0.37% -0.18%
3 2026-02-13 0.56%

Full EPSS history (14 records total)

Common vulnerability scoring system (CVSS) metrics for CVE-2025-47277

CVSS metrics for this CVE.

Base score Version Severity Vector Exploitability Impact Score source
9.8 3.1 CRITICAL
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H Click to expand
Attack vector (AV:N)
Could be attacked over the internet or any normal routed network—not just someone sitting at the machine.
Attack complexity (AC:L)
Once they can reach the bug, pulling it off is straightforward—no weird race conditions or rare setup.
Privileges required (PR:N)
No account or special rights needed—anonymous or random user is enough.
User interaction (UI:N)
Nobody has to click “OK” or open a trap file; it can work without a victim helping.
Scope (S:U)
Damage stays in the same “trust bubble” as the broken component—no big spill into unrelated systems.
Confidentiality (C:H)
Serious risk that confidential data gets exposed in a big way.
Integrity (I:H)
They could widely tamper with or forge data—trust in the data is badly hurt.
Availability (A:H)
Could take the service down hard or make it unusable for people who depend on it.
3.9 5.9 [email protected]

Weakness enumeration for CVE-2025-47277

GitHub Security Advisory for CVE-2025-47277

GHSA-hjq4-87xh-g4fv · Severity: critical · Ecosystem: pip — vLLM Allows Remote Code Execution via PyNcclPipe Communication Service

OS Trackers for CVE-2025-47277

vendor priority summary link
redhat medium https://access.redhat.com/security/cve/CVE-2025-47277

Affected software / configurations for CVE-2025-47277

Vendor Product Version Raw CPE
vllm vllm >= 0.6.5, < 0.8.5 cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*

References for CVE-2025-47277

cvelogic Threat Intelligence