Collaborative robots, or “cobots,” are designed to operate alongside humans, dissolving the physical barriers that define traditional industrial automation. This proximity is their greatest strength and, for a red teamer, their most compelling weakness. Unlike their caged predecessors, cobots are rich with sensors and complex decision-making logic intended for safety. Your task is to turn this sophisticated awareness into a liability.
From Safety Feature to Attack Vector
Where the previous chapter focused on bypassing gross safety mechanisms like light curtains and safety PLCs, cobot exploitation is a more subtle art. You are not just disabling a fence; you are manipulating the robot’s perception of reality. The core principle is to exploit the trust placed in the cobot’s own senses and logic.
| Vulnerability Type | Traditional Industrial Robot | Collaborative Robot (Cobot) |
|---|---|---|
| Primary Control | Programmable Logic Controller (PLC), dedicated robot controller. | Higher-level OS (Linux-based, ROS), complex software stack, APIs. |
| Human Interaction | Minimal; through teach pendant in a locked-down state. | Constant; through force sensors, vision, proximity detectors, direct physical guidance. |
| Key Attack Surface | Network access to the controller, physical safety bypasses. | Sensor data streams, HMI, learning algorithms, API endpoints. |
| Impact of Compromise | High-force, predictable but catastrophic failure. | Subtle manipulation, unpredictable behavior, safety degradation disguised as normal operation. |
Attack Vector 1: Sensory Deception
A cobot’s ability to safely share a workspace hinges on its sensors: force/torque sensors in its joints, computer vision, and proximity detectors. By feeding these sensors false data, you can trick the cobot into making unsafe decisions.
Force/Torque Sensor Spoofing
Most cobots stop when they feel unexpected resistance. This is a core safety feature. Your goal is to either suppress this signal to allow for dangerous forces or inject false signals to trigger nuisance stops, reducing productivity and trust in the system.
Technique: If the sensor data is transmitted over an unencrypted network protocol (common in older ROS versions or proprietary systems), a man-in-the-middle (MITM) attack can intercept and modify the force feedback values. You can script a filter that caps all reported force values below the safety threshold, effectively blinding the robot to collisions.
Vision System Manipulation
Cobots using vision for obstacle avoidance or part recognition are vulnerable to adversarial attacks on their perception models. This goes beyond simply blocking the camera.
Technique: Introduce a carefully crafted physical object or projected pattern into the cobot’s workspace. This could be an “adversarial patch” that, when viewed by the cobot’s camera, is misclassified as empty space, causing the robot to move into an occupied area. Alternatively, you could manipulate lighting conditions or use infrared projectors to saturate the camera’s sensor, effectively blinding it without triggering a “camera offline” fault.
Attack Vector 2: Exploiting Learning and Programming Interfaces
Cobots are designed for ease of use. Their programming interfaces, often graphical and accessible over a network, are a prime target. Many also feature “learning by demonstration,” where an operator physically guides the arm to teach it a path. This intuitive feature can be subverted.
HMI and API Abuse
The tablet-based Human-Machine Interface (HMI) is often just a web app running on a standard OS, communicating with the robot controller via a REST API or similar protocol. These APIs are frequently undocumented and lack proper authentication.
Technique: Use network scanning tools to identify the cobot’s IP address and open ports. Intercept HMI traffic to reverse-engineer the API endpoints. Once you understand the commands for moving joints or changing tool states, you can write scripts to send malicious commands directly to the controller, bypassing the HMI’s safety checks.
# Pseudocode: Python script exploiting a hypothetical unsecured cobot API
import requests
import json
COBOT_IP = "192.168.1.50"
# Assumes a discovered, unauthenticated endpoint
MOVE_ENDPOINT = f"http://{COBOT_IP}/api/v1/move/joint"
# Craft a malicious move command that exceeds safe speed/acceleration limits
# which might be unchecked on the controller's API level.
malicious_payload = {
"joint_angles": [90.0, -45.0, 90.0, 0.0, 0.0, 0.0],
"speed": 1.5, # Exceeds safe collaborative speed of 0.25 m/s
"acceleration": 5.0 # Unsafe acceleration
}
try:
# Send the command directly, bypassing the HMI's sanity checks
response = requests.post(MOVE_ENDPOINT, json=malicious_payload, timeout=2)
if response.status_code == 200:
print("Malicious move command sent successfully.")
except requests.exceptions.RequestException as e:
print(f"Failed to connect to cobot: {e}")
Corruption of “Hand-Guiding” Programs
In “hand-guiding” or “lead-through” programming, the cobot records a path as a human operator physically moves the arm. These recorded paths are stored as files on the controller. If you can gain access to the controller’s filesystem, you can subtly alter these programs.
Technique: Gain a foothold on the cobot’s controller (e.g., via an unpatched OS vulnerability). Locate the stored program files (often XML or a proprietary format). Modify a single waypoint in a complex path, shifting it slightly outside the intended safe zone or changing the tool’s state at a critical moment. The change is too small to be noticed during a casual review but could result in product damage, tool breakage, or a safety incident during high-speed execution.
Attack Vector 3: Logic and State Machine Degradation
The most sophisticated attacks target the cobot’s internal logic, causing it to fail in ways that appear accidental. The goal is not a single, dramatic failure, but a persistent, trust-eroding degradation of performance or safety.
Inducing Race Conditions
A cobot’s software stack involves multiple processes communicating with each other: one for motion planning, one for sensor processing, another for safety monitoring. If you can disrupt the timing of these communications, you can create race conditions that lead to unsafe states.
Technique: Flood the cobot’s network interface with traffic (a localized DoS) to introduce latency in sensor data packets. This delay might cause the motion planner to act on stale data. For example, the robot might begin moving into a space that was free 100 milliseconds ago but is now occupied by a human. The safety system will eventually stop the robot, but the “near miss” erodes trust and may not be correctly diagnosed as a malicious act.