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Emerging Tech7 min read

Securing AI-Powered Apps: The New Attack Surface

MW

Marcus Webb

Principal Security Engineer · June 4, 2026

Illustration of a mobile app connected to an AI neural network with a padlock

Almost every new app now ships an AI feature, a chat assistant, a recommendation model, or a call to an external LLM. These capabilities create an attack surface that did not exist two years ago, and most security tooling has not caught up. Here is what teams building AI features need to watch for.

Prompt injection reaches the mobile client

When an app passes user input or untrusted content into an LLM prompt, an attacker can craft input that overrides your instructions, leaks system prompts, or triggers unintended actions. On mobile, this often combines with insecure deep links or clipboard data to escalate quickly.

We analyze how your app constructs prompts and where untrusted data flows into them, flagging the paths most likely to be abused.

Leaking model and provider keys

Calling an AI provider directly from the client almost always means an API key is sitting in the binary. Beyond the obvious credential theft, attackers can rack up usage costs or abuse your account. RASMISER detects embedded keys and flags direct client-to-provider calls that should be proxied through your backend.

On-device models and data exposure

On-device inference is great for privacy and latency, but bundled model files, training data, and inference logs can expose intellectual property or sensitive user data if stored insecurely.

  • Model files left world-readable in app storage
  • Inference inputs and outputs written to plaintext logs
  • Insufficient validation of model responses before acting on them

How we help teams ship AI features safely

RASMISER treats AI integrations as first-class citizens in the scan. We identify where untrusted input meets model calls, where keys are exposed, and where AI-derived data is stored or logged insecurely, then explain each risk in terms your team can act on.

Combined with runtime protection, teams can ship AI-powered apps with anti-tampering and TLS pinning in place, so the model endpoints and the data flowing to them stay protected after launch.

The takeaway

AI features expand what your app can do and what attackers can target. Treating the AI layer as part of your security scan, not an afterthought, is the difference between a confident launch and an expensive incident.

Scan your app free

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