Security-First AI Architecture

Most AI systems treat security as an afterthought. You build the model, then you add a filter, then you patch the vulnerability when someone finds it. This is backwards.

SneppX-ALG embeds security at the architectural level. The Adversarial Robustness Core (ARC) is not a module — it is the input layer, the gradient path, and the output verifier.

S0 (cryptographic primitives) gives us Ed25519 signatures, ChaCha20-Poly1305 encryption, SHA-3 and BLAKE3 hashing, and Argon2id key derivation. All implemented in C with constant-time operations where needed.

S1 (secure memory) uses guard pages via mprotect, stack canaries, ASLR compatibility, mlock for sensitive data, and constant-time comparison to prevent timing side channels.

S2 (obfuscation) makes reverse engineering harder. Control flow flattening replaces sequential code with state machines. String encryption hides literals. VM obfuscation runs critical paths in custom bytecode.

S3 (behavioral monitor) tracks API calls and detects anomalous execution patterns. This is the runtime defense against active attacks.

S4-S9 are planned for v0.5.0 and v1.0: ZK proofs, on-device attestation, federated security, formal verification in Lean 4, and third-party penetration testing.

Every line of S0-S1 has been audited by hand. S2-S3 are implemented but not formally verified. S4-S9 will be.