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.
Description
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-20-compliance-architecture) [](#reinforcement-learning-environment-formulation) [](#4-agent-ooda-loop-architecture) --- ## 🌑 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