How can organisations compare risky extensions against each other?

Why This Matters for Security Teams

Comparing risky extensions only by category name or store rating misses the operational question: what can the extension actually do once it runs inside the browser, desktop, or SaaS workflow? Security teams need a shared taxonomy that scores persistence, credential access, content manipulation, and exfiltration because those behaviors map to real blast radius. This becomes more urgent when extensions can read sessions, inject prompts, or silently pivot into connected systems.

The same issue shows up in broader NHI governance, where hidden identity capabilities are often underestimated until an incident forces a review. NHI compromise is not rare: the 2024 ESG Report: Managing Non-Human Identities found that 72% of organisations have experienced or suspect a breach of non-human identities. That is why risk comparison must be behavior-led, not name-led. For teams already mapping extension exposure, the Top 10 NHI Issues provides useful context on how identity abuse tends to recur across environments.

In practice, many security teams encounter extension abuse only after credentials, data, or session state have already been exposed, rather than through intentional pre-deployment review.

How It Works in Practice

The practical approach is to compare extensions against a common behavior model, then rank them by the controls they can bypass and the assets they can reach. Start by assigning each extension to a small set of capabilities: persistence, privileged access, data read/write scope, network reach, and exfiltration paths. Then normalize those findings into a scoring rubric so reviewers can compare dissimilar extensions on the same scale.

That rubric should distinguish between static installation risk and runtime risk. An extension that only renders content is not comparable to one that can access tokens, inject DOM changes, or call external APIs on behalf of the user. Current guidance suggests treating those as different classes of exposure because the latter can chain privileges and extend trust far beyond the browser boundary. The NIST Cybersecurity Framework 2.0 is useful here because it pushes teams to connect asset inventory, access control, and monitoring rather than reviewing software in isolation.

• Score persistence: can the extension survive logoff, restart, or policy change?
• Score credential reach: can it read cookies, tokens, API keys, or SSO state?
• Score manipulation power: can it alter content, approvals, prompts, or workflow inputs?
• Score exfiltration paths: can it send data to external endpoints or hidden channels?
• Score control bypass: can it evade RBAC, browser policy, or tenant-level guardrails?

For NHI-specific comparison models, the OWASP NHI Top 10 helps teams separate surface-level similarity from actual identity abuse patterns. These controls tend to break down when extensions are granted broad workspace permissions in environments where users routinely approve prompts without reading the resulting scope.

Common Variations and Edge Cases

Tighter scoring often increases review overhead, requiring organisations to balance comparability against analyst time and change velocity. That tradeoff is real, especially where hundreds of extensions are already approved and business teams want fast deployment. Best practice is evolving, but current guidance suggests using a tiered model: lightweight scoring for low-impact extensions, deeper review for anything that can access identity, content, or outbound network paths.

Edge cases matter. Two extensions can look equally risky on paper but differ sharply in how they are used: one may be installed by a single admin account, while another is embedded across a managed fleet and can touch regulated data. Comparisons also get harder when extensions operate through delegated trust, inherited browser sessions, or hidden service integrations. In those cases, the extension is part of a broader identity chain, not a standalone app. The Ultimate Guide to NHIs — Key Challenges and Risks is a useful reference for understanding why identity-bound access changes the risk picture.

For teams building a repeatable program, compare extensions on what they can do, what they can reach, and how hard they are to remove after compromise. That framing remains valid even when the underlying store rating or vendor category is misleading. In environments with heavy shadow IT, compare risk only after full permission discovery, because hidden installs and unmanaged browser profiles distort every score.

Related resources from NHI Mgmt Group

• How do organisations operationalise NHI ownership at scale?
• How should security teams handle risks from AI browser extensions?
• When should organisations treat an NHI as a high-priority risk?
• How can organisations reduce the blast radius of compromised agent identities?