How can security teams detect attempts to determine physical locations?

What security teams should look for in app and device telemetry

Detection starts with access patterns that do not fit the stated purpose of the application. A legitimate map, fleet, delivery, safety, or travel app may need location access, but many other apps only need coarse context or no location at all. Teams should baseline which permissions are normal, then flag apps, scripts, or SDKs that request GPS, Wi-Fi, cellular, Bluetooth, or system location services without a clear business need.

Behavior matters as much as permissions. Repeated location lookups, background polling, sudden changes in consent prompts, and unusual fusion of device signals with network metadata can indicate attempts to infer where a user is. Correlate mobile, endpoint, and proxy telemetry so you can see whether the same process is also querying nearby networks, scanning SSIDs, or collecting identifiers that improve geolocation accuracy.

For higher-confidence detection, look for combinations rather than isolated events. A single permission request may be harmless, but repeated requests across sessions, process injection into a trusted app, or scripts that call location APIs after launch can show active probing. The most useful signal is a mismatch between the app's function and the volume, timing, or persistence of its location-related access.

How to separate legitimate use from location probing

The key question is whether location access is proportional to the business function. A logistics app may legitimately read precise location during active routing, but a calculator, document viewer, or internal utility usually should not. Security teams should review app inventories, mobile device management policies, and runtime telemetry together so that approved use cases are distinguished from opportunistic collection.

Consent prompts are useful evidence, but not proof of legitimate intent. Some applications trigger prompts repeatedly to coax approval, while others request access once and silently continue collecting data in the background. Watch for apps that ask for location after a feature update, a permission reset, or a change in SDK behavior, because that can indicate a new collection path rather than a user-facing feature.

Data flows often reveal the real objective. When a process combines device identifiers, browser data, wireless observations, and location-service responses, it may be trying to infer a precise physical position even if no single source appears sensitive on its own. That fusion is especially important to inspect in regulated environments, employee devices, and systems that interact with physical premises or safety controls.

What defenders can do once suspicious location access appears

Once the telemetry points to suspicious geolocation activity, teams should confirm whether the app is sanctioned, whether the permission is expected, and whether the data is leaving the environment. If the app is not part of an approved business workflow, treat the activity as potential surveillance, privacy abuse, or pre-attack reconnaissance. The next step is to preserve logs, isolate the affected device or workload if needed, and determine whether the behavior is user-driven or automated.

If the access is tied to a trusted application, focus on the embedded component that is making the requests. Mobile SDKs, advertising libraries, analytics modules, and injected scripts can introduce location collection without the owning team understanding the full data path. Reviewing code provenance, update history, and outbound connections often matters more than checking the front-end app name alone.

Location data can also become a downstream dependency for other abuse, including stalking, targeted fraud, facility mapping, and physical intrusion planning. When the signal indicates deliberate inference rather than normal app behavior, teams should consider whether the same actor is also enumerating networks, harvesting device attributes, or testing consent controls to widen the available geolocation methods.

Risk and Threat Considerations

Attempts to determine physical location are risky because they can expose people, facilities, and movement patterns even when no single permission looks sensitive. The same telemetry that supports benign navigation can also be used to profile routines, identify high-value targets, or reduce the effort needed for follow-on intrusion.

Failure mechanism: An app or script gathers precise or inferred location by combining GPS, Wi-Fi, cellular, Bluetooth, and device metadata, then keeps querying until it can build a reliable location profile.

Impact: Defenders may miss surveillance or reconnaissance in progress, and exposed location intelligence can increase personal safety risk, physical security risk, and the success rate of targeted attacks.

Practitioner Guidance

What to prioritise: Focus first on the applications and processes that have both persistent background execution and access to multiple geolocation signals. Those are the cases most likely to move from normal feature use into inference or tracking.

What to verify: Confirm whether the permission aligns with the app's business function, whether the access is foreground only or background, and whether the same process is also reading nearby-network or device metadata. That combination is often more telling than a single permission event.

Common mistake: Treating consent pop-ups as sufficient evidence of legitimate collection. A user can approve access while the application still exceeds its necessary scope or continues to query location after the expected workflow is complete.

Practitioner takeaway: The most reliable detection comes from context, not just permission state, because location inference is often built from ordinary signals that become suspicious only when they are combined, repeated, or out of purpose.

Related resources from NHI Mgmt Group

• Guide: MITRE ATT&CK Guide for Defenders
• Why are NHIs a critical concern for security teams?
• What steps should security teams take to prevent Shadow AI risks?
• Why is the abuse of NHIs a priority for security teams?
• What is the difference between SAST and DAST for security teams?