neuralspace-ai 4.1.3

Cognitive Code Security Engine - Self-evolving, AI-powered threat detection

🧠 NeuralSpace – Cognitive Security Universe

NeuralSpace is a self-organizing, zero‑trust security mesh for code. It combines a Covalent Tree (self‑evolving topology), a Hive Mind (emergent intelligence), AST/CFG Data‑Flow Analysis, and a Zero‑Trust Security Mesh (RSA 2048 signing) into a single ultra‑lightweight (~8 KB) system.

📌 Version 4.1.0 — Ship pre‑trained weights, fixed importlib+chr() evasion, raised default threshold to 0.25, added --threshold CLI flag.

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🔥 The Problem We Solve

Current Tool	Limitation	NeuralSpace Advantage
Traditional AV	Relies on known signatures.	Blocks zero‑day obfuscated threats.
SAST (SonarQube)	99.5% false positives.	Contextual detection + Taint Analysis.
Transformer Models	Huge, slow, cloud‑dependent.	Lightweight (~8 KB), runs instantly on CPU.
File Watchers	React to files, don't understand content.	Routes files dynamically into a living knowledge tree.

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✨ Key Features (v4.1.0)

• 🧬 Self‑Evolving Topology – The Covalent Tree spawns new branches anticipatorily when it detects structural drift (drift velocity > 0.5).
• 🌊 True Data‑Flow Analysis – Tracks tainted input (input(), sys.argv) to dangerous sinks (exec, eval, os.system). Catches importlib + chr() evasions.
• 🔒 Zero‑Trust Security Mesh – All threat reports are cryptographically signed with RSA 2048. Nodes maintain a public key registry (PKI) and earn trust over time.
• 🧠 Hive Mind (Emergent Intelligence) – Multiple agents communicate and form a consensus on threats (consensus ≥ 0.7).
• 🛡️ Adversarial Robustness – Trained on obfuscated variants (getattr, string‑concat, __import__, chr()).
• 🌍 Polyglot – Scans Python, JavaScript, TypeScript, Go, and Rust (Tree‑Sitter AST parsing).
• ⚡ Ultra‑Lightweight & Local – ~8 KB model, trains in <60 seconds on CPU.
• 🔧 CLI Configurable – New --threshold flag to adjust sensitivity.
• 🤖 GitHub App Integration – Auto‑scans Pull Requests and posts comments.

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🏗️ How It Works

1. Tokenization + Data‑Flow – Code is parsed via Tree‑Sitter AST; data‑flow tracks tainted input to dangerous sinks.
2. Routing – The vector descends the Covalent Tree. If it matches a child node (cosine similarity > 0.85), it dives deeper.
3. Hive Mind Consensus – All active nodes vote on the threat. The collective decision overrides individual errors.
4. Judgment – The terminal node's PureNeuralAtom computes Sentinel (S) and Logic (L) scores.
5. Enforcement – If S > threshold (default 0.25) or L < 0.2, the file is quarantined and cryptographically reported.
6. Evolution – If the file is allowed but deviates significantly (drift velocity > 0.5), the tree anticipatorily fractures and spawns a new child node.

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🚀 Quick Start

Installation (One Command)

pip install neuralspace-ai