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