Hard-Coded Credentials and Cleartext Transmission Expose ELD Telemetry Data
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Bransys ELD devices shipped with hard-coded MQTT and FTP credentials embedded in the application code, meaning any attacker who reverse-engineers the app can gain unauthorized access to telemetry data and firmware across all deployed devices simultaneously. Transmitting sensitive information in cleartext compounds the risk by allowing passive interception on any network the device communicates over. These are foundational secure-development failures — hard-coded credentials and unencrypted channels should never reach production in safety-critical transportation technology. The widespread impact is especially concerning because ELD devices are federally mandated for commercial vehicles, making them a high-value target with a large installed base.
Tactical Insight
Immediate actions
- Update all Bransys ELD apps immediately to Android ≥11.00.00 and iOS ≥1.1.54 to remediate known vulnerabilities.
- Rotate any MQTT and FTP credentials that may have been exposed and audit broker/server access logs for unauthorized connections.
- Enforce TLS/SSL encryption for all data channels transmitting telemetry or firmware to eliminate cleartext exposure.
Long-term improvements
- Integrate static application security testing (SAST) and secret-scanning tools into CI/CD pipelines to detect hard-coded credentials before release.
- Adopt a secrets management solution (e.g., HashiCorp Vault, AWS Secrets Manager) so credentials are injected at runtime rather than embedded in binaries.
- Establish a vendor security assessment program that requires IoT/ELD suppliers to demonstrate secure coding practices and regular penetration testing.
Detection measures
- Deploy network monitoring to alert on unencrypted protocol usage (plain MQTT port 1883, plain FTP port 21) originating from ELD devices.
- Implement anomaly detection on MQTT broker and FTP server access to flag logins from unexpected geographic locations or IP ranges.
- Conduct periodic firmware and mobile app reverse-engineering reviews to proactively identify hard-coded secrets before threat actors do.