README.md

---
language:
- en
license: apache-2.0
base_model:
- Qwen/Qwen2.5-Omni-7B
---
# 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

```python
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]))