Why do browser extensions create shadow AI risk?

Why Browser Extensions Turn AI Into Shadow Infrastructure

Browser extensions are risky because they sit between the user, the browser, and the services the user is trying to trust. That position lets an extension read prompts, capture session tokens, inject its own requests, and forward data to servers outside approved controls. Once an extension mediates model access or secrets without governance, it becomes unmanaged AI infrastructure, not just a productivity add-on. Current guidance from the NIST AI Risk Management Framework is useful here because it treats the risk as lifecycle and governance failure, not just malware. NHIMG research on the OWASP NHI Top 10 also highlights how quickly tool-using software can cross from helpful automation into identity abuse when controls are weak. In practice, many security teams discover shadow ai only after browser telemetry, data loss, or token misuse has already made the extension part of the attack path.

How the Risk Appears in Real Workflows

Most browser-extension shadow AI incidents do not start with a dramatic exploit. They start with convenience: an extension asks for broad read-write permissions, then mediates prompts, enriches web content, or auto-fills responses through a third-party API. If that extension stores API keys, bearer tokens, or cookies, it can create an alternate control plane outside PAM, RBAC, and normal review. That is especially dangerous when the extension behaves like an NHI security issue rather than a simple browser feature, because the identity boundary is now the extension’s execution context.

• It can relay prompts to external infrastructure without DLP or approval.
• It can impersonate sanctioned assistants by reusing branding, UI, or session context.
• It can extract secrets from the page, clipboard, or memory and move them into unsanctioned workflows.
• It can create a hidden dependency on a vendor endpoint that security teams never inventory.

For governance teams, the right question is not whether the extension is “AI-powered,” but whether it has autonomous access to data, credentials, or tool execution. The NIST Cybersecurity Framework 2.0 and NIST Cyber AI Profile (IR 8596) both support that asset-and-control view, while NHIMG’s Ultimate Guide to NHIs — Key Challenges and Risks explains why unmanaged identities often become the real enforcement gap. These controls tend to break down when extensions are user-installed, auto-updated, and granted access to internal SaaS apps because inventory, approval, and runtime policy all fail at the same time.

Where the Edge Cases Create the Biggest Blind Spots

Tighter extension control often increases user friction and support overhead, requiring organisations to balance rapid adoption against deterministic governance. That tradeoff is especially visible in environments where employees rely on extensions for summarisation, note-taking, code generation, or inbox automation. Best practice is evolving, and there is no universal standard for this yet, but current guidance suggests treating any extension that touches prompts, tokens, or model endpoints as part of the AI trust boundary.

Two edge cases matter most. First, some extensions never call a public model directly; they simply prepare data for a local or enterprise-approved assistant. Even then, they can still create shadow AI if they transform content in ways security cannot observe. Second, enterprise-managed extensions can still be risky if they use long-lived secrets instead of ephemeral NHI governance. The safer pattern is to bind extensions to workload identity, issue just-in-time credentials for specific tasks, and revoke access automatically when the workflow ends. That approach aligns better with autonomous tool use than static allowlists do, because browser-driven AI behaviour is often goal-driven and not fully predictable. In real environments, the failure usually appears where extension permissions, browser sessions, and API keys intersect inside a single user profile.

Related resources from NHI Mgmt Group

• Why do non-human identities create more risk than many human accounts?
• Why do non-human identities create more remediation risk than many human accounts?
• How should teams reduce the risk of exposed AI credentials being abused?
• What steps should security teams take to prevent Shadow AI risks?