We reject the division between machine intelligence and classical systems. Neural models provide cognitive exploration across massive software surfaces, while deterministic systems enforce mathematical invariants that prevent catastrophic agentic failure.
Leveraging deep semantic intelligence to deconstruct complex software architectures, isolate subtle vulnerabilities, and synthesize provable remediations.
∃ σ ∈ Inputs, π ∈ CFG : π ⊨ Taint(src, sink) ∧ SMT(π) = SAT Constructing hardened hardware isolation, deterministic execution oracles, and formal constraint boundaries to govern autonomous agents.
∀ a ∈ Actions, (s ⊨ Inv ∧ s' = Apply(s, a)) ⟹ (s' ⊨ Inv) ∨ Rollback(s) SecDome is organized into three standalone domains: production-grade security systems, standardized empirical benchmarks, and meta-scientific research foundations.
Production-grade capabilities connecting compiler-level program analysis, SMT constraint solving, and zero-ambient-authority runtime micro-VM isolation.
Falsifiable evaluation suites for autonomous cyber agents and program analysis, rejecting synthetic shortcuts in favor of reproducible ground truth.
Our scientific mission, epistemological methodology supported by MinBeing meta-science, and dedication to open-access collaborative security engineering.
The software security industry is inundated with marketing buzzwords, unverified benchmarks, and speculative AI demos. SecDome operates strictly under open science and cryptographic transparency: every released tool, paper, and dataset is signed, reproducible, and verifiable.
Hermetic bit-for-bit reproducible release builds with cryptographic attestations.
All artifacts and vulnerability proofs immutably recorded in public transparency logs.
Git tags and releases cryptographically signed via physical FIDO2 / YubiKey hardware tokens.