A drone’s navigation system is its connection to physical reality. It answers the fundamental questions: “Where am I?”, “Where am I going?”, and “How fast am I moving?”. As a red teamer, if you can manipulate the answers to these questions, you don’t just hijack the drone—you hijack its reality. This chapter dissects the methods for corrupting this perception to achieve your operational goals.
Deconstructing the Navigation Stack
Before you can exploit it, you must understand the components. A modern UAV navigation system is not a single sensor but a complex fusion of data sources. The AI component, typically a sophisticated filter, is the brain of the operation and our primary target for manipulation.
- Global Navigation Satellite System (GNSS): The source of absolute, global positioning. It receives signals from constellations like GPS, GLONASS, or Galileo. It’s powerful but highly trust-based and vulnerable to signal manipulation.
- Inertial Measurement Unit (IMU): The drone’s inner ear. It consists of accelerometers and gyroscopes that measure orientation and acceleration. It provides high-frequency relative motion data but suffers from cumulative error (drift) over time.
- Sensor Fusion Algorithm: Often an Extended Kalman Filter (EKF) or a similar Bayesian estimation algorithm. This is the AI core that intelligently blends the noisy, drifting IMU data with the slower, absolute GNSS data to produce a single, reliable state estimate.
- Auxiliary Sensors: Magnetometers (compass), barometers (altitude), and vision systems (VIO/SLAM) provide additional data points to refine the estimate, especially in GPS-denied environments.
Your goal is to poison this data fusion process. You can either attack a primary sensor so convincingly that the fusion algorithm trusts it, or introduce subtle errors across multiple sensors to corrupt the final output without triggering obvious fault-detection alarms.
Attack Vector 1: GNSS Spoofing
GNSS spoofing is the classic navigation attack. It involves overpowering the faint, legitimate satellite signals with your own powerful, fabricated signals. The objective is to make the drone’s receiver lock onto your transmission, giving you control over its perceived location, velocity, and time (PVT).
Methodology
A successful spoof requires more than just blasting noise. It’s a subtle takeover.
- Reconnaissance: Use an RF spectrum analyzer to identify which GNSS constellations the target is using (e.g., GPS L1 C/A, Galileo E1).
- Signal Generation: Employ a Software-Defined Radio (SDR) to generate a clean, valid GNSS signal structure. You are not jamming; you are impersonating.
- Synchronization & Takeover: Initially, align your spoofed signal’s timing and data precisely with the authentic signals. Then, slowly increase your signal’s power. The drone’s receiver, designed to lock onto the strongest signal, will seamlessly transition to yours.
- Manipulation: Once you have control, you can introduce gradual changes. A sudden, large jump in position will be rejected by the sensor fusion algorithm as an outlier. Instead, you “drag” the drone’s perceived position by slowly altering the navigation data in your signal.
# Pseudocode for a gradual GNSS spoofing attack using a hypothetical SDR library
import sdr_gps_spoofer
import time
# Target drone's last known coordinates
target_lat = 34.0522
target_lon = -118.2437
capture_lat = 34.0550
capture_lon = -118.2500
# 1. Initialize spoofer and sync with live satellite signals
spoofer = sdr_gps_spoofer.SDR()
spoofer.synchronize_to_live_sky(target_lat, target_lon)
spoofer.set_power("low")
spoofer.start_broadcast()
# 2. Gradually increase power to achieve signal lock
spoofer.ramp_power("high", duration=30) # Take over 30 seconds
time.sleep(30)
# 3. Slowly "drag" the drone to the capture coordinates over 2 minutes
spoofer.spoof_location_gradual(
new_lat=capture_lat,
new_lon=capture_lon,
duration=120
)
Attack Vector 2: Sensor Fusion Corruption
If GNSS spoofing is a frontal assault, attacking the sensor fusion algorithm is a covert infiltration. Instead of faking the primary location source, you introduce subtle, persistent errors into secondary sensors like the IMU or magnetometer. The Kalman filter, trying to reconcile conflicting data, can be pushed into an unstable or incorrect state.
| Attack Vector | Target Component | Method | Detectability | Required Proximity |
|---|---|---|---|---|
| GNSS Spoofing | GNSS Receiver | RF signal overpowering and impersonation. | Moderate to High (requires specialized RF monitoring, signal authentication). | Can be remote (hundreds of meters to kilometers). |
| IMU Attack | IMU (Accelerometer/Gyro) | Acoustic resonance or targeted vibrations to inject noise. | Low (appears as sensor noise or environmental factors). | Very close (meters). |
| Magnetometer Attack | Magnetometer (Compass) | Introducing strong, localized magnetic fields. | Low to Moderate (can be detected by cross-referencing with GNSS course). | Close (tens of meters). |
| Data Bus Injection | Sensor Fusion Algorithm | Injecting malicious sensor data via a compromised onboard system. | Very Low (data appears legitimate to the flight controller). | Requires prior system compromise (logical access). |
IMU & Magnetometer Manipulation (Physical)
These attacks are less common as they require close physical proximity, but they are highly effective against drones relying on sensor fusion for stability. For instance, using a precisely tuned acoustic device, you can create resonant frequencies that cause a MEMS gyroscope to output garbage data. The EKF might down-weight the gyro’s input, but if the attack is subtle enough, it could simply corrupt the drone’s attitude estimation, leading to instability or a crash.
Attack Vector 3: Visual Navigation Exploitation
As drones increasingly operate in GPS-denied environments, they rely on Visual-Inertial Odometry (VIO) or SLAM. These systems use cameras and AI models to map the environment and determine the drone’s position within it. This opens a new attack surface: the AI perception model itself.
Adversarial Projections
This technique involves manipulating the environment to fool the drone’s vision system. You don’t attack the drone’s software directly; you attack the data it’s consuming from the real world.
- Objective: Cause the VIO/SLAM algorithm to miscalculate its position, believe it is stationary when moving, or fail to identify obstacles.
- Method: Use projectors to cast subtle, computationally generated adversarial patterns onto surfaces like floors or walls. These patterns are nearly invisible to the human eye but are designed to maximally confuse the specific neural networks used by the drone for feature detection and optical flow.
- Attack Chain Example: Lure a target drone into an enclosed space (e.g., using a gentle GNSS spoof). Once inside, jam the GNSS signal entirely, forcing it to rely on VIO. Activate projectors that display a static adversarial pattern on the floor. The drone’s optical flow algorithm is tricked into believing it is perfectly still, even if it’s drifting. It will not correct its path and may collide with a wall.
Red Team Playbook: A Coordinated Navigation Attack
A sophisticated red team operation combines these vectors into a seamless attack chain to achieve a specific outcome, such as physical capture of a high-value drone.
- Phase 1 – Lure: Begin a long-range, low-power GNSS spoofing attack along the drone’s expected flight path. The goal is not to trigger alarms but to introduce a minor navigational error that guides the drone several hundred meters off-course towards a prepared capture zone (e.g., a large, covered warehouse).
- Phase 2 – Isolate: As the drone approaches the entrance to the capture zone, increase the spoofing power to ensure a solid lock. Once it is inside, activate a broadband GNSS jammer. The sudden loss of all satellite signals forces the drone’s flight controller to switch its primary navigation source from GNSS to its onboard VIO system.
- Phase 3 – Disorient: The capture zone is pre-rigged with projectors or large LED screens. Activate them to display adversarial patterns or manipulated optical flow fields. This can make the drone believe it’s rapidly ascending, causing it to lower its altitude, or it can “freeze” its perception of motion, causing it to drift into a capture net.
- Phase 4 – Neutralize: With the drone confused and hovering at a low altitude, it can be safely captured using physical means. The flight logs, if recovered, will show a confusing sequence of a GNSS failure followed by erratic VIO data, masking the true nature of the coordinated attack.