Sensor Spoofing Injection

Advanced attacks craft mutually consistent false data across heterogeneous sensor streams to defeat cross-validation checks. An attacker injects a phantom obstacle that appears simultaneously in virtual LiDAR point clouds, camera bounding boxes, and radar returns, making the hallucination indistinguishable from a real object. This exploits the sensor fusion pipeline's assumption that correlated signals across modalities indicate ground truth.

Static spoofed data is trivially detected by temporal filters. Sophisticated injections maintain kinematic plausibility over sequential frames, ensuring ghost objects obey realistic motion models with consistent velocity, acceleration, and trajectory profiles. The attacker must solve a constrained optimization problem to generate a sequence that passes Kalman filter and tracking algorithm validation without triggering anomaly thresholds.

Attackers exploit known failure modes in specific neural network architectures used for perception. This includes:

• Adversarial patches placed in camera streams that cause object detectors to ignore real obstacles
• Frustum-based LiDAR injection that places points precisely within the camera's projection frustum to create seamless visual-LiDAR alignment
• Edge case exploitation of non-max suppression algorithms to either hide or duplicate detected objects

The ultimate objective is not sensor deception but downstream behavioral manipulation. By controlling what the agent perceives, the attacker steers planning and control algorithms toward dangerous actions:

• Injecting a false obstacle to force an emergency braking event
• Masking a real hazard to cause a collision
• Creating a phantom clear path into a restricted or hazardous zone
• Inducing oscillatory control behavior by injecting moving obstacles that trigger repeated replanning cycles

Unlike physical sensor spoofing which requires expensive RF or optical equipment, virtual sensor injection is a pure software exploit. An attacker who compromises the simulation environment or the inter-process communication bus can inject data without hardware access. This makes the attack infinitely reproducible, scriptable, and scalable across parallel simulation instances used for fleet-level agent training.

Naive injection produces data that fails statistical tests for sensor noise characteristics. Advanced attacks learn and replicate the noise distribution of legitimate sensors, including:

• Gaussian noise parameters specific to each virtual sensor model
• Dropout patterns and outlier distributions
• Calibration error signatures
• Environmental attenuation effects This ensures injected data passes anomaly detection systems trained on nominal sensor distributions.