External risk intelligence

vLLM SSRF Protection Bypass Vulnerability

CVE advisorySeverity: CRITICAL (CVSS 9.8)

CVE-2026-25960

vLLM is an inference and serving engine designed to host and serve LLM APIs. These services are commonly deployed as internet-facing or network-accessible endpoints to allow applications and users to query the model, making the vulnerable URL parsing functionality a common part of the exposed service surface.

Server-Side Request Forgery

Vllm

0.15.1 to before 0.17.0

Halo Surface Signal: 4 out of 5 — likely to be public-facing.

External exposure likelihood

Horizon Alert

Summary of the vulnerability and why it matters

A critical vulnerability exists in the vLLM model serving engine, potentially allowing attackers to bypass security measures and execute arbitrary code. This issue arises from inconsistent URL parsing between the validation layer and the actual request handler, impacting how external resources are accessed. The main concern is confirming the relevance and exposure of this vulnerability within your environment.

Inconsistent URL parsing can bypass security checks.
Affects systems using vLLM for model serving.
Confirm relevance and assess potential exposure.

Attack Path

How an attacker could exploit the issue

An attacker can exploit a vulnerability in vLLM by providing a specially crafted URL. This URL bypasses the intended security checks for loading models from external sources due to differences in how URLs are parsed by the validation layer and the actual request mechanism. Successfully bypassing these checks could allow the attacker to execute arbitrary code or access internal network resources.

No authentication or special access required.
Attacker supplies a malicious URL.
Enables remote code execution and data breaches.

Live Threat

Current exploitation, exposure, and threat context

This vulnerability could allow an attacker to bypass Server-Side Request Forgery (SSRF) protections when loading external resources. This could potentially lead to the LLM engine making requests to internal or unauthorized external network locations, which might expose sensitive system information or allow for further network reconnaissance.

LLM engine and its network access.
Bypassing SSRF protection via crafted URLs.
Unauthorized network access to internal systems.

Operational Fix

Recommended remediation, mitigation, and detection steps

The platform or infrastructure team responsible for vLLM deployments should take the lead, as this vulnerability impacts the core serving engine's network-facing functionality. The first actionable step is to identify all vLLM instances, determine their network exposure and business criticality, and then confirm the specific accountable owner before planning remediation actions.

Identify responsible platform owners.
Verify external network exposure.
Plan risk-based remediation.

Supplementary metadata

CVSS vector

Validate whether this threat affects your internet-facing exposure.

Halo Threat Intelligence helps prioritize remediation with Halo Surface Signal and H/A/L/O context. Start exposure validation with a free external attack surface trial.

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Frequently asked questions

What is vLLM and how is it used?

vLLM is an open-source software engine designed to serve and run large language models (LLMs) efficiently. Developers use it to host these models as APIs, allowing external applications or users to send prompts and receive responses. By managing how models are loaded and accessed, it enables high-performance machine learning inference in production environments.

What does CVE-2026-25960 mean for security?

This CVE identifies a Server-Side Request Forgery (SSRF) flaw, classified under CWE-918. It happens because vLLM uses two different libraries to parse web addresses—one for security checks and another for making network requests. An attacker can craft a specific URL that appears safe to the first library but is interpreted differently by the second, effectively bypassing the security filter meant to stop unauthorized requests.

Do I need to authenticate to trigger this bug?

No, authentication is not required to trigger this vulnerability. The flaw exists in the URL processing logic used to fetch external model resources. If an attacker can reach an input field or API endpoint that accepts a URL for loading models, they can supply a malicious link. Requests made using standard, well-formed URLs that do not attempt to exploit parsing differences will not trigger the vulnerability.

Is my instance at risk according to Halo Surface Signal?

Halo Surface Signal indicates that vLLM services are frequently deployed as network-accessible or internet-facing endpoints to facilitate model querying. Because this configuration makes the URL parsing functionality easily reachable, the risk is elevated for most standard deployments. You should verify if your vLLM endpoints are reachable from untrusted networks.

How should I respond to this vLLM vulnerability?

Your first step is to inventory all active vLLM instances across your infrastructure to determine which ones are running affected versions. Focus on identifying instances that are exposed to the internet or internal networks. Once mapped, coordinate with the platform owners to prioritize these systems for updates or necessary configuration changes to mitigate unauthorized access.

References

Written by Nicholas Merritt

Nicholas Merritt is VP of Security Solutions at Halo Security. With more than 25 years across application security, vulnerability prioritization, and offensive security, he helps teams translate threat data into practical exposure validation and remediation focus. He authors Halo Threat Intelligence CVE advisories using Halo Surface Signal and H/A/L/O context to prioritize internet-facing risk.