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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: |
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- Qwen/Qwen2.5-Omni-7B |
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--- |
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# Model Card for AegisGuard-CyberDefender |
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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. |
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## Model Details |
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### Model Description |
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- **Developed by:** Alpha Singularity + Synthosense AI |
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- **Led by:** James R. Wagoner (Cosmic James), QubitScript Creator |
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- **Model Type:** Transformer-based multi-agent LLM with embedded autonomous actuation layer |
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- **Objective:** Achieve proactive cyber defense via intelligent sensing, decision-making, and execution |
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- **License:** Apache 2.0 |
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- **Fine-tuned from:** Qwen/Qwen2.5-Omni-7B |
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## Key Autonomous Agent Capabilities |
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### Core Autonomy Stack |
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- **Self-Adaptive Threat Intelligence Loop (SATIL):** |
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- Monitors live feeds (SIEM, XDR, NetFlow, syslogs) |
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- Auto-prioritizes threat alerts by severity and likelihood |
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- Adjusts defense posture dynamically (firewall rules, ACLs, endpoint protection) |
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- **Autonomous Response Execution Engine (AREE):** |
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- Executes containment actions (quarantine IPs, kill processes, revoke tokens) |
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- Launches live memory forensics and data exfiltrations scans |
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- Deploys honeypots or redirector traps autonomously |
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- **Agent Coordination Protocol (ACP):** |
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- Integrates with other agents (SOC assistant, red team simulant, forensics bot) |
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- Multi-agent orchestration for complex responses or audits |
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- **Live Threat Simulation & Red Teaming Module:** |
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- Runs controlled adversarial simulations (MITRE ATT&CK, APT clones) |
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- Stress-tests system defenses against known and novel exploits |
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- **Zero-Day Exploit Sensor (ZDES):** |
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- Predicts novel exploit patterns using fuzzy anomaly detection |
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- Integrates with open threat feeds and closed zero-day watchlists |
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- **Quantum-Safe Protocol Audit Layer:** |
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- Scans encryption protocols for post-quantum vulnerabilities |
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- Advises on migration to lattice-based or hybrid quantum-safe schemes |
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## Expanded Skills |
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### Detection |
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- Signature-based and behavioral-based threat analysis |
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- Kernel-level anomaly detection |
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- DNS tunneling detection and passive DNS intelligence |
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- Insider threat behavior profiling |
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- AI-driven phishing/malware detection (PDFs, scripts, emails, packets) |
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### Defense |
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- Autonomous firewall rule injection (based on telemetry context) |
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- Endpoint Defense Orchestration (EDO) |
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- Network segmentation reconfiguration |
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- Ransomware containment + real-time snapshot rollbacks |
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- Active deception and fake service deployment |
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### Response |
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- Auto-triage and incident ticket generation |
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- Live incident summary generation for analyst teams |
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- Legal/regulatory alert routing (HIPAA, GDPR, CMMC compliance mode) |
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- Blockchain evidence signing for tamper-proof forensics |
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### Intelligence Gathering |
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- Dark web monitoring for leaked assets/domains |
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- WHOIS recon and passive threat actor profiling |
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- CVE & NVD scraping for patch priority scoring |
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- Threat campaign attribution (APT family similarity analysis) |
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### Reinforcement + Learning |
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- Reinforcement-based feedback from analyst correction loops |
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- Contextual retraining via SOC event streams |
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- Self-evolution via red/blue agent duel outcomes |
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- Adaptive ruleset generation per environment |
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## Uses |
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### Direct Use |
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- Autonomous SOC augmentation |
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- Vulnerability and compliance audit agent |
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- On-device secure AI companion for cyber-aware environments |
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- Military/industrial network guardian agent |
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- Threat hunt assistant for elite blue teams |
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### Integrations |
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- SIEM platforms (Splunk, Sentinel, Elastic) |
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- SOAR platforms (Cortex XSOAR, Swimlane) |
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- Threat intelligence feeds (AlienVault, VirusTotal, GreyNoise) |
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- Secure gateway devices, honeypots, and deception frameworks |
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## Bias, Risks, and Limitations |
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- AI hallucination risk in unknown or sparse telemetry scenarios |
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- False positives under extreme obfuscation or low-signal environments |
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- Requires human SOC fallback in nuclear-grade or safety-critical networks |
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### Mitigation |
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- Feedback refinement loop with security analysts |
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- Confidence scoring & adjustable trust levels |
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- Shadow-mode deployment before full actuation |
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## Get Started |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("AlphaSingularity/AegisGuard-CyberDefender") |
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model = AutoModelForCausalLM.from_pretrained("AlphaSingularity/AegisGuard-CyberDefender") |
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prompt = "Detect and respond to lateral movement attempts in the east-1 subnet." |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0])) |
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