Security

MIND is designed for safety-critical AI deployments. This page covers the security model, memory safety guarantees, and deterministic execution features.

Memory Safety

MIND inherits Rust-inspired memory safety guarantees, eliminating entire classes of vulnerabilities at compile time:

  • No null pointer dereferences — optional types make nullability explicit
  • No buffer overflows — bounds checking with compile-time shape verification
  • No data races — ownership and borrowing rules prevent concurrent mutation
  • No use-after-free — deterministic resource management without garbage collection

Deterministic Execution

By default, MIND guarantees bit-exact reproducibility across runs:

  • IEEE 754 strict compliance — floating-point operations follow the standard precisely
  • No non-deterministic optimizations — reordering that affects results is disabled by default
  • Explicit RNG seeding — all random operations require explicit seeds
  • Reproducible builds — same source produces identical binaries

Audit Trail Support

For regulated industries, MIND provides features to support audit and compliance:

  • Full execution traces available in debug mode
  • Immutable IR representations for model versioning
  • Cryptographic hashing of compiled artifacts
  • Integration points for external logging systems

Threat Model

The MIND security model assumes:

  • Source code and compiler are trusted
  • Runtime environment provides standard OS protections
  • Input data may be adversarial (tensor bounds are checked)
  • Side-channel attacks are out of scope for the base runtime

Sandboxing (Planned)

Future versions will support optional sandboxing for untrusted model execution:

// Planned syntax
@sandbox(memory_limit: 1GB, time_limit: 10s)
fn untrusted_inference(input: Tensor<f32, N, M>) -> Tensor<f32, N, K> {
    // ...
}

Learn More

See the full security specification at mind-spec/security.md.