How we built a real-time DNS and VPN leak detection engine (architecture and methodology)

Source: Dev.to

The core problem

VPN tunnels encrypt traffic between the user’s device and the VPN server. However, several components of the network stack may bypass this tunnel:

• DNS resolvers
• WebRTC STUN requests
• IPv6 routing
• OS‑level fallback mechanisms

If any of these expose the original network interface, privacy is compromised. Leak detection must therefore validate actual network behavior, not just connection status.

System architecture overview

The leak detection engine is built using a layered validation model:

• Detection layer – identifies observable network parameters.
• Verification layer – checks whether those parameters belong to the expected VPN endpoints.
• Classification layer – categorises the integrity of the connection.

Detection layer

This layer collects all externally observable network parameters:

• Public IPv4 address
• IPv6 presence
• DNS resolver identity
• WebRTC STUN responses

Data is gathered via server‑side request analysis combined with client‑side execution. The key principle is direct observation rather than client‑reported state.

Verification layer

The verification layer determines whether the detected network endpoints correspond to:

• VPN tunnel endpoints
• Data‑center infrastructure
• Residential ISP networks

If a residential ISP endpoint is observed while a VPN tunnel is expected, the system flags potential exposure.

Classification layer

The engine classifies connection integrity into categories:

• Secure
• DNS leak detected
• WebRTC leak detected
• IPv6 exposure detected
• Tunnel integrity failure

This allows clear identification of privacy risks.

Real‑time analysis approach

Leak detection operates in real time, performing analysis during active network requests rather than relying on cached or historical data. This ensures an accurate representation of the current network state.

Privacy model

The system is designed to operate without storing personal identifiers. It does not require:

• User accounts
• Persistent identifiers
• Tracking cookies

Only temporary technical network parameters are analyzed.

Implementation context

The leak detection engine powers a live privacy testing tool available here:

https://smartadvisoronline.com/tools/leak-test.html

Technical documentation of the detection logic is available here:

https://github.com/Dloran1/smartadvisor-leak-test-engine

Why leak detection matters

VPN software may appear connected while still exposing critical network information. Independent validation is necessary to confirm tunnel integrity. Leak detection engines provide objective verification of network privacy.

Final thoughts

Network privacy cannot rely on assumptions; it requires direct validation of observable network behavior. Leak detection systems provide a critical layer of transparency for users relying on encrypted tunnels.