Why your device's biometric lock can't save your data
As developers, we often pride ourselves on the sophisticated "front door" security we build into our applications. We implement biometric hooks like FaceID or Android’s BiometricPrompt API, ensuring that only the authorized user can physically unlock the interface. But there is a dangerous architectural fallacy growing in the industry: the belief that securing the device-level access point secures the data in transit.
The recent news surrounding carrier-level breaches and the systemic vulnerability of SMS highlights a massive disconnect between local authentication and protocol security. While we are busy refining UI/UX for facial recognition and secure enclaves, the underlying data—specifically SMS—is still being transmitted via protocols designed in the 1980s.
The OSI Model Failure: Securing Layer 7 While Layer 2 Is Leaking
From a technical perspective, the problem is a mismatch of security across the stack. Local biometric authentication (like the facial comparison technology we work with at CaraComp) is a high-integrity "who" check. However, if that "who" then sends an unencrypted SMS, the data is exfiltrated at the network carrier level before it ever reaches the recipient.
The CISA advisory from December 2024 is a wake-up call for anyone building authentication flows. They are explicitly telling us to stop using SMS as a second factor (2FA). For developers, this means the phone_number field can no longer be a trusted anchor for identity. Whether it's the Salt Typhoon operation targeting carrier infrastructure or the massive AT&T breach exposing metadata for 300 million users, the "green bubble" is essentially cleartext sitting in a third-party database.
Identity Verification vs. Network Transit
At CaraComp, we focus on facial comparison—specifically using Euclidean distance analysis to determine the probability that two images represent the same individual. This is a stateless, math-heavy process used by investigators to build cases. The investigative community is realizing that they can no longer rely on "who the phone says they are" via a text message. They need verifiable, biometric evidence that holds up in court.
If a carrier's database can be breached to intercept 2FA codes, the entire chain of custody for digital identity is broken. This is why we advocate for Euclidean distance analysis as a more robust method of identity verification. It doesn't rely on a vulnerable network carrier; it relies on the immutable geometry of the human face.
Deployment Implications for Devs
If your current codebase still relies on SMS for sensitive alerts or identity verification, you are essentially delegating your security to the weakest link in the telecom chain. Here is what we should be looking at instead:
- Move to WebAuthn: Shift from SMS to hardware-backed passkeys.
- Encrypted Payloads: If you must use mobile messaging, leverage APIs that support end-to-end encryption (E2EE) rather than standard telephony gateways.
- Euclidean Analysis for Case Work: For those in the OSINT or investigative space, stop trusting "selfie-verified" profiles that only check for a phone number. Use professional comparison tools that analyze facial landmarks and provide a confidence score based on actual geometry.
The "locked phone" is a UI comfort, not a network-layer reality. As we continue to advance computer vision and facial comparison tech, we must ensure the data we are identifying is as secure as the algorithms we use to analyze it.
As developers, are we doing enough to move users away from SMS-based 2FA, or are we still prioritizing "onboarding friction" over actual data integrity?
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