vLLM is an inference and serving engine for large language models (LLMs). The SSRF protection fix for CVE-2026-24779 add in 0.15.1 can be bypassed in the load_from_url_async method due to inconsistent URL parsing behavior between the validation layer and the actual HTTP client. The SSRF fix uses urllib3.util.parse_url() to validate and extract the hostname from user-provided URLs. However, load_from_url_async uses aiohttp for making the actual HTTP requests, and aiohttp internally uses the yarl library for URL parsing. This vulnerability in 0.17.0.
Security readout for executives and security teams
Plain-English summary
CVE-2026-25960 is a high-severity vLLM server-side request forgery protection bypass. An authenticated or otherwise low-privileged user who can supply a URL to affected vLLM functionality may cause the server to request unintended internal resources. The main business risk is exposure of internal data or services from AI infrastructure.
Executive priority
Prioritize remediation for internet-facing, multi-tenant, or internally privileged vLLM services. The issue is not listed as actively exploited in the supplied sources, but the confidentiality impact makes delayed patching risky.
Technical view
The issue affects vLLM versions >= 0.15.1 and < 0.17.0. The prior SSRF fix validates URLs with urllib3 parsing, but load_from_url_async performs requests through aiohttp/yarl, creating inconsistent hostname interpretation. CVSS 3.1 is 7.1: network attack, low complexity, low privileges, no user interaction, high confidentiality impact, low availability impact.
Likely exposure
Most relevant exposure is vLLM deployments in the affected version range that let users or tenants provide URLs for model, asset, or remote-loading workflows.
Exploitation context
The source bundle does not show KEV listing or confirmed active exploitation. Exploitation requires access to a vulnerable URL-loading path and may target internal network resources reachable by the vLLM server.
Researcher notes
Focus review on URL parser differentials between validation and request execution. The supplied record ties this to the earlier CVE-2026-24779 SSRF fix and load_from_url_async. Avoid assuming other vLLM components are affected without vendor evidence.
Mitigation direction
Upgrade vLLM outside the affected range, preferably 0.17.0 or later if vendor guidance confirms.
Review the GitHub advisory and Red Hat advisories for product-specific fixed builds.
Restrict URL-loading features to trusted users until patched.
Block vLLM server egress to internal metadata and sensitive service networks.
Add allowlists for approved remote hosts where operationally feasible.
Validation and detection
Inventory vLLM versions and flag >= 0.15.1 and < 0.17.0.
Identify services exposing URL-loading workflows to users, tenants, or API clients.
Review logs for unexpected outbound requests from vLLM hosts to internal addresses.
Confirm patched package versions against vendor advisories, not only application metadata.
Test egress controls from vLLM hosts to sensitive internal destinations.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Potential ATT&CK relevance
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
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cwe · low confidence lookup
CWE-474: Exact CWE lookup
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CWE-918: Information exposure and cloud metadata lookup
Information exposure and SSRF weaknesses can make discovery, cloud metadata, and credential material review relevant. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
The CVE wording references SSRF or metadata access, so cloud discovery and credential material review may help. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
We collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
CWE-474 · source CWE mapping
Use of Function with Inconsistent Implementations
Use of Function with Inconsistent Implementations represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.
Server-Side Request Forgery (SSRF) represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.