Memory safety in AI systems

Technical note. AI trustworthiness includes memory safety, execution isolation, and application behavior.

Follow the dependencies

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A typical AI application combines model code, native kernels, parsers, storage clients, drivers, and orchestration. Those components may have different privileges and failure modes. A useful threat model identifies who can supply inputs, load code, read credentials, and update the environment.

The design objective is to limit how a fault in one component affects another. A sandbox can restrict execution authority; memory-protection mechanisms can constrain accesses within the native stack; application policy governs permitted actions. CHERI’s research overview explains the memory-protection and compartmentalization primitives.

Validate consequences

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Choose a representative workload and inject a fault at a documented boundary. Record the denied access, the state of neighboring components, recovery behavior, and latency or throughput impact. A native memory check cannot determine whether a model’s recommendation is correct or safe.

This evaluation approach applies to cloud services, enterprise workflows, and embedded systems. Industrial and embodied AI are useful cases when failures have operational consequences.