Danger-OS: Spiking Neural Danger Theory — Affective Neuromodulatory Arbitration for Real-Time Behavioural Anomaly Detection
Venkatesh Swaminathan · Zenodo (CERN European Organization for Nuclear Research) · 2026
Version 2 (2026-08-04). Revised to the NLL Universal Paper Format v6. The original text is retained in full; nothing has been deleted. Corrections appear as marked blocks placed at the section that carries the claim, and each one states what the paper said, what the data show, the corrected claim, why it happened, and what still stands. This version withdraws two findings.0.315% is not a detection rate: no attack occurs anywhere in the 5,710 ticks and there is no ground truth, so no true positive, false positive, precision or recall exists — the figure is how often a threshold on ordinary CPU usage was crossed; and Finding 5 ("KILL clusters at Vairagya = 0") is the arbitration rule in §3.3 restated.The system description and the tick-level logging stand; the adversarial-robustness reading is corrected, since injected Bhaya is measured after a τ = 3 leak and is overwritten by construction. Everything below this line is the original description from version 1. It is retained unchanged for the record. Where it conflicts with the corrections above, the corrections stand. Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. Danger-OS is a neuromorphic behavioural anomaly detection system that replaces hardcoded decision logic with four biologically-inspired spiking neurons — Bhaya (fear), Vairagya (wisdom), Shraddha (trust), and Spanda (aliveness) — whose affective voltage dynamics continuously govern how an operating system responds to live threat signals across a 500 ms tick cadence. Rather than matching signatures or applying static rules, every action class from idle monitoring to process termination emerges organically from the interplay of these neuromodulatory states, with all OS-level interventions gated behind an explicit human consent barrier by architectural default. Across eight experimental scenarios totalling 5,710 ticks of continuous operation — spanning adversarial voltage-injection stress, controlled threat escalation, a 45-minute long-horizon quiescence test, and mixed interactive load — the system produced a terminal-action rate of just 0.315% (18 events across 5,710 ticks), remaining bounded and proportionally stable across all durations. This work confirms the Bhaya Quiescence Law in a defence-grade deployment context: the fear neuron's natural decay dynamics suppress runaway escalation without requiring explicit suppression logic, a property that held across two independent reproducibility runs. Danger-OS is the first entry in the Maya-Defence Series, extending the Maya affective SNN architecture into AI safety and adversarial defence applications. Series: Part of the Maya-Defence Series — AI safety and adversarial defence systems built on Maya neuromorphic foundations. Bhaya Quiescence Law confirmed in defence-grade deployment contexts. Links: GitHub Repository (private — to request access: email [email protected] with subject Code Access Request — Danger-OS and your research context) | Interactive Dashboard | FAQ | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 — an ORCID-derived provenance mark, not an experimental parameter. Each paper's disclosure block states whether it reached a result in that paper.Canary: MayaNexusVS2026NLL_Bengaluru