| --- |
| 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])) |
| |
