We are witnessing the rapid integration of AI into mobile applications, from customer-facing assistants to intelligent automation. AI-driven mobile apps are becoming core to mobile UX and business logic. But they also bring new, often overlooked, attack surfaces.
As AI becomes more embedded, the mobile app threat surface expands well beyond what traditional mobile security was designed to handle. This blog post unpacks what security leaders need to consider as they adopt on-device AI, and why mobile AI security must evolve to meeting emerging AI security threats.
AI is no longer confined to the cloud. Businesses are moving toward embedding AI directly into mobile apps. The shift from cloud-based AI to on-device AI security—including small language models (SLMs), decision engines, multimodal AI, and federated learning—is driven by:
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From AI-powered mobile apps in banking, healthcare, and gaming to securing on-device AI assistants in smart devices, embedded AI is transforming mobile workflows across industries. But this evolution introduces entirely new mobile app AI risks. Most threat models haven’t caught up to this reality, so many apps today aren’t protected against AI-specific runtime threats. And the closer AI gets to the device edge, the more exposed it becomes to manipulation, extraction, and misuse.
“The global on-device AI market size was estimated at USD 8.60 billion in 2024 and is projected to reach USD 36.64 billion by 2030, growing at a CAGR of 27.8% from 2025 to 2030. The industry is driven by progress in AI technologies, growing demand for real-time data processing, and heightened concerns around privacy and security.” [1]
As businesses and organizations embrace embedding AI into mobile apps, they must understand the novel threats and risks it introduces.
Attackers can manipulate app memory at runtime to alter AI logic or bypass internal guardrails. For example, in a healthcare app, an attacker might modify AI logic to prioritize certain treatments for malicious or biased reasons. In a lending app, AI runtime manipulation could be used to force approval of high-risk or fraudulent applications.
AI models stored within mobile apps are valuable intellectual property. Without proper AI IP protection, attackers can extract, clone, or resell these models. This leads to loss of competitive advantage and may also trigger licensing violations or compliance risks.
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Apps using language models or dynamic inputs are particularly susceptible to prompt injection attacks. Malicious users can hijack input/output flows to corrupt model behavior, exfiltrate data, or trigger unintended decisions. This undermines AI data integrity and user trust.
Many embedded AI systems depend on local configuration files or decision trees. If these internal data sources are not properly secured, attackers can alter them to manipulate AI behavior or gain unauthorized access.
If the AI runtime environment isn’t hardened, attackers can inject or execute malware, gaining persistent access or compromising the system more broadly. This is a growing concern in AI-powered mobile apps operating offline or in untrusted environments.
Together, these risks represent a fast-expanding AI threat landscape and require a new defensive strategy.
Some industries face a particularly urgent need for AI model protection for their mobile developers and app security teams.
AI-powered advisors, diagnostics tools, fraud engines, and risk calculators are being deployed on-device to enable faster and more private decision-making. But this also introduces high-stakes risks:
“Software Solutions is the most lucrative component segment registering the fastest growth during the forecast period.” [2]
Read more: Financial App Security in 2025: Combating Traditional Malware and Emerging Ai Threats
AI models that control in-game logic or detect cheating are frequently targeted:
Threats to AI-driven apps on smartphones are expanding to edge devices. For example:
As the risks of embedded AI models grow, so does the regulatory spotlight. Even mobile apps that operate offline must now align with emerging legal frameworks.
Many apps unintentionally fall under “high-risk” categories, even when their AI features seem lightweight. Businesses need to align AI security for developers, compliance teams, and product leads, early and decisively.
Legacy mobile app protection tools were not built to handle embedded AI risks. Here are some reasons traditional mobile security tools fail to address today’s mobile AI threats:
The result is that many teams are still building powerful AI-driven mobile apps but without sufficient protection.
To stay ahead and stay safe, businesses need to shift security thinking and evolve beyond traditional app security. AI changes how mobile apps behave, how they can be attacked, and therefore how they should be protected. If the AI runs inside the app, so do the risks, and so can an attacker. Mobile AI security requires an updated risk model with the assumption of internal threats.
Protection against AI threats in mobile phones must work at the app level. Static controls or cloud-side safeguards are no longer sufficient. What this means is that modern AI model security must now include:
Read more: AI deobfuscators: Why AI won’t help hackers deobfuscate code (yet)
This isn’t future gazing. It is what is required of businesses today to protect trust, compliance, and IP in modern mobile apps.
While the AI security threats are evolving, so are the application protections. Promon has exciting ways to mitigate AI risks that empower mobile app teams to deploy proven, layered defenses that protect their embedded AI, while maintaining UX, compliance, and speed to market.
Businesses embedding AI in mobile apps need to rethink their threat models today. In our next post, we’ll explore how to secure AI in mobile apps using layered mobile app protection. We’ll show how you can keep your embedded AI secure while building trust, compliance, and resilience into AI-driven products.
[1] https://www.grandviewresearch.com/industry-analysis/on-device-ai-market-report
[2] https://www.grandviewresearch.com/horizon/outlook/ai-in-healthcare-market-size/global