What is the CVSS score of CVE-2026-32617?

CVE-2026-32617 has a CVSS 3.1 base score of 7.1 (High). CVSS vector: CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:L. EPSS exploitation probability: 0.0%.

Which versions of AI / ML are affected by CVE-2026-32617?

AnythingLLM installations versions 1.11.1 and earlier are vulnerable to authentication bypass attacks, but exploitation is restricted to attackers already present on the same local network due to…

How to fix or mitigate CVE-2026-32617?

Within 24 hours: Inventory all AnythingLLM deployments and document their network location (internet-facing vs. internal-only) and data classification. Within 7 days: Implement network segmentation to restrict AnythingLLM access to trusted networks only, and disable the application if not actively required. Within 30 days: Monitor vendor communication for patches and plan immediate deployment upon availability; consider alternative solutions if patch timeline extends beyond 60 days.

AI / ML CVE-2026-32617

EUVD-2026-12103 HIGH

Permissive Cross-domain Security Policy with Untrusted Domains (CWE-942)

2026-03-13 GitHub_M

Information Disclosure Google Mozilla AI / ML Anything Llm Firefox Chrome

7.1

CVSS 3.1 · GitHub Advisory

Severity by source

GitHub Advisory PRIMARY

7.1 HIGH

AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:L

Primary rating from GitHub Advisory · only source for this CVE.

CVSS VectorGitHub Advisory

CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:L

Attack Vector

Network

Attack Complexity

High

Privileges Required

None

User Interaction

Required

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

Low

Lifecycle Timeline

EUVD ID Assigned

Mar 13, 2026 - 21:01 euvd

EUVD-2026-12103

Analysis Generated

Mar 13, 2026 - 21:01 vuln.today

CVE Published

Mar 13, 2026 - 20:07 nvd

HIGH 7.1

DescriptionGitHub Advisory

AnythingLLM is an application that turns pieces of content into context that any LLM can use as references during chatting. In 1.11.1 and earlier, On default installations where no password or API key has been configured, all HTTP endpoints and the agent WebSocket lack authentication, and the server's CORS policy accepts any origin. AnythingLLM Desktop binds to 127.0.0.1 (loopback) by default. Modern browsers (Chrome, Edge, Firefox) implement Private Network Access (PNA). This explicitly blocks public websites from making requests to local IP addresses. Exploitation is only viable from within the same local network (LAN) due to browser-level blocking of public-to-private requests.

AnalysisAI

AnythingLLM versions 1.11.1 and earlier contain an authentication bypass vulnerability on default installations where the application's HTTP endpoints and WebSocket connections lack proper authentication and accept requests from any origin. While rated CVSS 7.1, exploitation is limited to attackers on the same local network due to browser Private Network Access (PNA) protections, making this a medium-priority issue for most deployments.

Technical ContextAI

AnythingLLM (CPE: cpe:2.3:a:mintplex-labs:anything-llm:*:*:*:*:*:*:*:*) is an application that converts content into LLM-usable context. The vulnerability stems from CWE-942 (Overly Permissive Cross-domain Whitelist), where the server's CORS policy accepts any origin (*) combined with missing authentication on default installations. The AnythingLLM Desktop version binds to 127.0.0.1 (loopback), and while modern browsers (Chrome, Edge, Firefox) implement Private Network Access controls that block public-to-private network requests, the lack of authentication still exposes the service to same-network attacks.

RemediationAI

Upgrade to AnythingLLM version 1.11.2 or later. As an immediate workaround, configure authentication by setting a password or API key for the installation. The vendor advisory (https://github.com/Mintplex-Labs/anything-llm/security/advisories/GHSA-24qj-pw4h-3jmm) should be consulted for detailed patch information. For installations that must remain on vulnerable versions, ensure the application is only accessible from trusted networks and implement network segmentation.