Mobile Protect Data Sheet
Mobile Protect's in-app runtime defenses for deployed iOS and Android apps: dynamic obfuscation, anti-tampering and anti-debugging, jailbreak and root detection, on-device anti-fraud scoring (account takeover, payment fraud, bot and emulator defense, fraud farms), defense against AI-augmented attacks (attack-path mapping, memory scraping, AI overlay malware), and the SDK's production footprint (500 KB, no startup tax on Android, ~10 KiB/s telemetry).
Key Areas Explored In This Resource
- Runtime protection for deployed iOS and Android apps. Dynamic obfuscation, anti-tampering, anti-debugging, and jailbreak/root detection that block reverse engineering and stop execution on untrusted devices.
- On-device anti-fraud. Scoring of transactions, sessions, and devices for bot, emulator, and fraud signals to catch account takeover, payment fraud, identity theft, and fraud farms before financial loss.
- Defense against AI-augmented attacks. Attack-path mapping inside the deployed app, memory scraping and data exfiltration defense, and detection of AI-orchestrated overlay malware, scraping bots, password spraying, and login automation.
- OWASP MASVS Resilience (MASVS-R). The Resilience category of the Mobile Application Security Verification Standard is the canonical specification for in-app protection: anti-tampering, anti-reverse-engineering, anti-debugging, and runtime integrity (jailbreak/root detection). Direct mapping to Mobile Protect's capability tiles 1, 2, and 3.
- MITRE ATLAS. Adversarial Threat Landscape for Artificial-Intelligence Systems is the framework that catalogs AI-augmented attack tactics targeting deployed apps. Maps to the new 05 AI Defense section: attack-path mapping, memory scraping, and AI-orchestrated overlays.