5ire MCP Vulnerability (CVE-2026-22792)

Key Takeaways

• AI MCP client rendered untrusted HTML without proper sanitization
• Injected scripts could execute in the client renderer context
• Attackers could create malicious MCP servers through exposed APIs
• No confirmed exploitation or real-world breach reported
• Patch released to remediate the issue

Unsafe Rendering Exposed AI Client to Code Execution

A vulnerability in the 5ire MCP client allowed untrusted HTML content to be rendered without adequate sanitization, enabling script execution within the client environment. This behavior could be chained to access privileged APIs and ultimately achieve remote command execution. While no active exploitation has been confirmed, the issue highlights the risks introduced when AI clients combine rich content rendering with powerful agent and tool integrations.

Source: GitHub Security Advisory

What We Know

The issue was disclosed via GitHub Security Advisory GHSA-p5fm-wm8g-rffx and assigned CVE-2026-22792. It affects the 5ire MCP client, an AI assistant interface designed to interact with Model Context Protocol servers.

According to the advisory, the client rendered attacker-controlled HTML without proper sanitization. This allowed injected elements, including JavaScript event handlers, to execute in the renderer context. Once code execution was achieved, the attacker could access exposed bridge APIs available to the client.

One such capability allowed the creation of new MCP servers. By abusing this functionality, an attacker could chain the vulnerability into arbitrary command execution on the host system. The maintainers released version 0.15.3 to address the issue. No public reports of exploitation were noted at the time of disclosure.

Source: GitHub Advisory GHSA-p5fm-wm8g-rffx
Source: NIST NVD CVE-2026-22792

How the Breach Happened

This incident resulted from unsafe handling of untrusted content within an AI client application. By rendering HTML without sufficient sanitization or isolation, the client allowed attacker-controlled code to execute in a privileged context.

Because the AI client exposed bridge APIs for interacting with MCP servers and system-level functionality, successful script execution granted access to powerful capabilities. This collapsed the separation between user-facing content and internal control mechanisms.

The combination of rich content rendering, agent orchestration, and insufficient isolation created a pathway from a simple rendering flaw to full system-level impact, even without exploiting the AI model itself.

Why It Matters

AI clients increasingly function as control hubs for agents, tools, and automated workflows. When such clients are compromised, attackers may gain access to capabilities far beyond simple data exposure.

Although no breach has been confirmed, the theoretical impact of remote command execution is severe. A compromised AI client could enable persistence, lateral movement, or unauthorized interaction with connected agents and MCP servers.

This incident reinforces the need for stronger isolation, input handling, and monitoring in AI client environments, especially as agent-based architectures become more common in enterprise and developer workflows.

PointGuard AI Perspective

This vulnerability illustrates how AI security risks often emerge at the intersection of user interfaces, agent frameworks, and tool integrations.

PointGuard AI helps organizations secure AI-driven environments by providing visibility into agent activity, client interactions, and tool usage across AI workflows. This enables detection of abnormal behavior that may indicate compromised clients or unsafe execution paths.

Policy-based controls allow teams to restrict what agents and tools are permitted to do, reducing the likelihood that a single client-side flaw can escalate into broader system compromise.

By tracking and analyzing real-world AI security incidents, PointGuard AI supports proactive risk management across AI supply chains, including third-party clients and MCP ecosystems.

Source: AI Runtime Defense
Source: AI Security Incident Tracker
Source: AI Supply Chain Security

Incident Scorecard Details

Total AISSI Score: 7.2/10

Criticality = 8.5, Remote command execution capability, AISSI weighting: 25%
Propagation = 7.5, Exploitable through malicious content and MCP interaction, AISSI weighting: 20%
Exploitability = 8.0, Low complexity once unsafe rendering is triggered, AISSI weighting: 15%
Supply Chain = 8.0, Impacts AI clients and MCP ecosystems, AISSI weighting: 15%
Business Impact = 6.5, No confirmed exploitation or breach reported, AISSI weighting: 25%

Sources

• GitHub Security Advisory GHSA-p5fm-wm8g-rffx
• NIST National Vulnerability Database CVE-2026-22792