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AI security incident: Libredesk has a SSRF Vulnerability in Webhooks (GHSA-wgm6-9rvv-3438)

Incident date: February 18, 2026 | Published: February 25, 2026 | Source: GitHub Security Advisory | Classification confidence: 99%

This incident is part of the public archive. AI-specific signals are limited in the current source material, so source citations should be reviewed closely during triage. Review methodology.

**Date:** 2025-12-07 **Vulnerability:** Server-Side Request Forgery (SSRF) **Component:** Webhooks Module ## Executive Summary A critical security vulnerability exists in the LibreDesk Webhooks module that allows an authenticated "Application Admin" to compromise the underlying cloud infrastructure or internal corporate network where this service is being hosted. The application fails to validate destination URLs for webhooks. This allows an attacker to force the server to make HTTP requests to arbitrary internal destinations. ## Confirmed Attack Vectors ### 1. Internal Port Scanning (Network Mapping) Attackers can map the internal network by observing the difference between successful connections and connection errors.

Why This Is AI-Related

This advisory is part of the public incident archive, but the current source material uses limited explicit AI terminology, so the cited sources should be reviewed carefully when judging AI relevance and exposure.

  • Explicit AI-specific signals are limited in the current source material, so use the cited advisory to validate scope during triage.

Affected Workflow

Review connectors, retrieval plugins, webhook targets, file access paths, and outbound network policies around AI services.

Likely Attack Path

The flaw creates an unauthorized path to fetch, read, or exfiltrate sensitive data from connected systems or local files.

Impact

The flaw can expose internal data, local files, or connected systems through AI workflow connectors and supporting services. Severity HIGH. Classification confidence 99%. Source channel GHSA.

Detection And Triage Signals

  • Unexpected outbound requests from AI application components
  • Access to internal metadata endpoints, local files, or restricted datasets
  • Downloads or responses that contain internal documents, secrets, or embeddings

Recommended Response

  • Confirm whether the affected component can reach internal metadata, local files, or connected data stores.
  • Restrict outbound requests and sensitive data access paths until a patch or mitigation is in place.
  • Inspect logs for unusual downloads, webhook calls, retrieval requests, or responses containing internal content.

Compliance And Business Impact

Data exposure creates direct confidentiality risk and can trigger incident notification, contractual, and regulatory obligations.

Sources

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