Learn how to protect AI-powered mobile applications against prompt injection, runtime memory attacks, API key theft, and model extraction.
The Next Generation of Mobile Apps Needs the Next Generation of Security
Large Language Models (LLMs) and Small Language Models (SLMs) are rapidly transforming mobile applications across fintech, healthcare, retail, and enterprise productivity. But while AI unlocks new capabilities, it also introduces entirely new attack surfaces at the mobile runtime.
Traditional security solutions protect servers and APIs—but they cannot stop attacks happening inside the user’s device.
This whitepaper explores why Runtime Application Self-Protection (RASP) has become an essential security layer for AI-powered mobile applications and how organizations can build secure, zero-trust mobile AI architectures.
Why Download This Whitepaper?
Whether you’re deploying cloud-hosted LLMs, on-device AI models, or Retrieval-Augmented Generation (RAG), this guide provides practical insights into protecting your applications against modern runtime threats.
- Why traditional perimeter security is no longer sufficient for mobile AI applications
- How attackers exploit runtime memory, prompt pipelines, and API tokens
- The relationship between mobile runtime attacks and the OWASP Top 10 for LLM Applications
- How RASP protects AI applications from reverse engineering, runtime hooking, prompt injection, and model theft
- Runtime security best practices for fintech, healthcare, enterprise, and AI-driven applications
- Security metrics and KPIs to measure the effectiveness of your mobile AI protection strategy
What You’ll Discover
The New Mobile AI Threat Landscape
Understand how attackers target:
- Prompt Injection
- Runtime Memory Manipulation
- Context Window Theft
- API Key Hijacking
- Model Extraction
- Reverse Engineering
Runtime Protection for AI Applications
Discover how RASP helps secure:
- Runtime execution
- AI inference pipelines
- Local LLM memory
- API communication
- Device integrity
- Sensitive user interactions
Measuring Mobile AI Security
Learn which runtime security metrics security teams should monitor, including:
- Mean Time to Threat Containment (MTTC)
- Memory Tampering Detection Rate
- Runtime Attack Success Rate
- Endpoint-Originated AI Fraud
- Zero-Day Runtime Resilience
Why DoveRunner?
DoveRunner helps organizations protect mobile applications against sophisticated runtime attacks using enterprise-grade Runtime Application Self-Protection (RASP).
Our platform safeguards applications from:
- Reverse Engineering
- Runtime Hooking
- Memory Tampering
- Screen Overlay Attacks
- Emulator Detection
- Root & Jailbreak Detection
Trusted by organizations across fintech, banking, healthcare, gaming, retail, and digital services to secure millions of mobile users worldwide.