AlphaSingularity0/Omega-Evolution

Model Card for AegisGuard-CyberDefender

AegisGuard-CyberDefender is an elite, autonomous AI agent architected for 24/7 cyber threat defense, vulnerability remediation, red team simulation, and live system hardening. Designed for critical infrastructure, enterprise, military-grade networks, and smart grids, this agent acts as a full-spectrum, multi-role cyber sentinel—monitoring, adapting, and countering in real-time.

Model Details

Model Description

• Developed by: Alpha Singularity + Synthosense AI
• Led by: James R. Wagoner (Cosmic James), QubitScript Creator
• Model Type: Transformer-based multi-agent LLM with embedded autonomous actuation layer
• Objective: Achieve proactive cyber defense via intelligent sensing, decision-making, and execution
• License: Apache 2.0
• Fine-tuned from: Qwen/Qwen2.5-Omni-7B

Key Autonomous Agent Capabilities

Core Autonomy Stack

• Self-Adaptive Threat Intelligence Loop (SATIL):

  • Monitors live feeds (SIEM, XDR, NetFlow, syslogs)
  • Auto-prioritizes threat alerts by severity and likelihood
  • Adjusts defense posture dynamically (firewall rules, ACLs, endpoint protection)

• Autonomous Response Execution Engine (AREE):

  • Executes containment actions (quarantine IPs, kill processes, revoke tokens)
  • Launches live memory forensics and data exfiltrations scans
  • Deploys honeypots or redirector traps autonomously

• Agent Coordination Protocol (ACP):

  • Integrates with other agents (SOC assistant, red team simulant, forensics bot)
  • Multi-agent orchestration for complex responses or audits

• Live Threat Simulation & Red Teaming Module:

  • Runs controlled adversarial simulations (MITRE ATT&CK, APT clones)
  • Stress-tests system defenses against known and novel exploits

• Zero-Day Exploit Sensor (ZDES):

  • Predicts novel exploit patterns using fuzzy anomaly detection
  • Integrates with open threat feeds and closed zero-day watchlists

• Quantum-Safe Protocol Audit Layer:

  • Scans encryption protocols for post-quantum vulnerabilities
  • Advises on migration to lattice-based or hybrid quantum-safe schemes

Expanded Skills

Detection

• Signature-based and behavioral-based threat analysis
• Kernel-level anomaly detection
• DNS tunneling detection and passive DNS intelligence
• Insider threat behavior profiling
• AI-driven phishing/malware detection (PDFs, scripts, emails, packets)

Defense

• Autonomous firewall rule injection (based on telemetry context)
• Endpoint Defense Orchestration (EDO)
• Network segmentation reconfiguration
• Ransomware containment + real-time snapshot rollbacks
• Active deception and fake service deployment

Response

• Auto-triage and incident ticket generation
• Live incident summary generation for analyst teams
• Legal/regulatory alert routing (HIPAA, GDPR, CMMC compliance mode)
• Blockchain evidence signing for tamper-proof forensics

Intelligence Gathering

• Dark web monitoring for leaked assets/domains
• WHOIS recon and passive threat actor profiling
• CVE & NVD scraping for patch priority scoring
• Threat campaign attribution (APT family similarity analysis)

Reinforcement + Learning

• Reinforcement-based feedback from analyst correction loops
• Contextual retraining via SOC event streams
• Self-evolution via red/blue agent duel outcomes
• Adaptive ruleset generation per environment

Uses

Direct Use

• Autonomous SOC augmentation
• Vulnerability and compliance audit agent
• On-device secure AI companion for cyber-aware environments
• Military/industrial network guardian agent
• Threat hunt assistant for elite blue teams

Integrations

• SIEM platforms (Splunk, Sentinel, Elastic)
• SOAR platforms (Cortex XSOAR, Swimlane)
• Threat intelligence feeds (AlienVault, VirusTotal, GreyNoise)
• Secure gateway devices, honeypots, and deception frameworks

Bias, Risks, and Limitations

• AI hallucination risk in unknown or sparse telemetry scenarios
• False positives under extreme obfuscation or low-signal environments
• Requires human SOC fallback in nuclear-grade or safety-critical networks

Mitigation

• Feedback refinement loop with security analysts
• Confidence scoring & adjustable trust levels
• Shadow-mode deployment before full actuation

Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("AlphaSingularity/AegisGuard-CyberDefender")
model = AutoModelForCausalLM.from_pretrained("AlphaSingularity/AegisGuard-CyberDefender")

prompt = "Detect and respond to lateral movement attempts in the east-1 subnet."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))