Engineering

Agentic harnesses change what an LLM can do in mobile app security testing. On its own, a model can name likely risks such as insecure storage, exposed secrets, risky permissions, vulnerable SDKs, backend issues, and privacy exposure, but the app may remain untouched. With the right tools, context, memory, prompts, execution loops, and runtime feedback around it, the model can inspect the app package, observe behavior, follow traffic, connect signals, and leave behind evidence a security team can review. From permission analysis to JEF-powered native exploitation, the difference is visible in the trace: app evidence, tool output, runtime proof, and reproducible steps instead of report-shaped text.