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Argon CRS-RL

An Autonomous 4-Agent OODA Cyber Reasoning System & Reinforcement Learning Environment for Zero-Noise Vulnerability Remediation

Core trick

Four agents run an OODA loop, and a contextual-bandit Q-learner picks remediation actions gated by sandboxed unit tests.

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An Autonomous 4-Agent OODA Cyber Reasoning System & Reinforcement Learning Environment for Zero-Noise Vulnerability Remediation
mohnishb2025/argon-cyber-rl-environment★ 00 forksJavaScriptCreated Oct 25, 2026 · last push Oct 26, 2026

README excerpt

# 🛡️ Argon CRS-RL: Autonomous Cyber Reasoning System & Reinforcement Learning Environment

> **An Autonomous 4-Agent OODA Loop + Contextual Bandit Q-Learning Environment Engineered to Defend Against "Black October" Supply-Chain & Zero-Day Floods**

[![NIST CSF 2.0 Aligned](https://img.shields.io/badge/Framework-NIST_CSF_2.0-0066cc?style=for-the-badge)](#nist-csf-20-compliance-architecture)
[![RL Engine](https://img.shields.io/badge/RL_Policy-Contextual_Bandit_Q--Learning-7c3aed?style=for-the-badge)](#reinforcement-learning-environment-formulation)
[![Sandbox Verified](https://img.shields.io/badge/Sandbox_QA-100%25_Unit_Test_Gated-16a34a?style=for-the-badge)](#4-agent-ooda-loop-architecture)

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## 🌑 Why We Built This: A Countermeasure to "Black October"

In modern software security, **"Black October"** represents the nightmare scenario for SecOps and platform engineering teams: a synchronized wave of critical supply-chain disclosures, zero-day CVEs (`CISA KEV` / `GHSA`), and AI-accelerated exploit weaponization where attackers compress **Time-to-Exploit (TTE)** from weeks to minutes.

When a **Black October** event hits an enterprise codebase, traditional vulnerability management collapses under two compounding failures:

1. **Alert Fatigue Paralysis (False-Urgency Flood):** Conventional SCA scanners (`npm audit`, Snyk, Dependabot) blindly flag every vulnerable package listed in `package.json`—even when the vulnerable library is **never imported or invoked anywhere in the p

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