19.3.3 Hardware-specific exploits

Your red teaming objective here is to understand how the physical substrate of a neuromorphic chip can be turned against itself. These are not algorithmic tricks but direct, physical assaults on the hardware that underpins the network’s intelligence. Success means compromising the model not through its logic, but through its physics.

Exploiting Analog Component Imperfections

Unlike digital systems built on the certainty of 0s and 1s, many neuromorphic systems rely on analog components like capacitors and memristors to represent neural states and synaptic weights. These components are inherently “noisy” and susceptible to environmental conditions. An attacker doesn’t need to break encryption; they just need to turn up the heat.

The core attack vector is fault injection. By precisely manipulating the chip’s physical environment—using focused lasers for thermal stress, or electromagnetic (EM) pulses—you can induce predictable failures in these analog components. This is not about causing random chaos. It’s about targeted degradation. For example, heating a specific region of the chip could increase the leakage rate of capacitors acting as neuron membranes, effectively silencing a whole cluster of neurons responsible for detecting a specific feature.

Power and Clock Glitching Attacks

Classic hardware hacking techniques find a fertile new ground in neuromorphic computing. SNNs are critically dependent on the timing of spikes. A spike arriving a few nanoseconds too late might miss the integration window of its target neuron, completely altering the computational path. This temporal sensitivity is a vulnerability you can exploit.

By introducing carefully timed, short-lived “glitches” into the chip’s power supply, you can disrupt its operation in subtle but powerful ways:

• Instruction Skipping: A voltage drop at the right moment can cause a neuron’s update logic to fail, effectively freezing its membrane potential for a cycle.
• Spike Suppression/Generation: A glitch during spike communication can corrupt the event data packet, causing a spike to be dropped. Conversely, it can create a “ghost” spike, triggering downstream neurons incorrectly.

Imagine a security SNN designed to detect a specific audio signature. A precisely timed power glitch could suppress the key spikes that form this signature, rendering the system deaf to the threat at the exact moment it needs to listen.

// Pseudocode for a neuron's temporal integration
function integrate_spike(neuron, spike):
    if (spike.timestamp >= neuron.window_start && 
        spike.timestamp <= neuron.window_end):
        
        // Power glitch here could cause this block to be skipped
        neuron.potential += spike.weight
        if neuron.potential > neuron.threshold:
            fire_spike(neuron)
    else:
        // Spike arrived too late (due to induced delay) and is ignored
        log("Missed integration window for spike.")
            

Side-Channel Analysis on Spiking Activity

Event-driven computation is efficient, but it’s also leaky. A neuron firing consumes a tiny amount of power and emits a faint electromagnetic field. When thousands of neurons fire in correlated patterns, these emissions become a measurable signal. This is the foundation of side-channel attacks against neuromorphic hardware.

By placing a probe near the chip and monitoring its power consumption or EM emissions, an attacker can reconstruct the internal spiking activity of the network. This doesn’t require any logical access. You are essentially “listening” to the hardware think. This can reveal:

• Input Data: Different inputs (e.g., images of a “cat” vs. a “dog”) produce measurably different global spike patterns. With enough samples, you can build a classifier to deduce the input just from the power trace.
• Model Secrets: The power trace can reveal the “hotspots” of the network—the most active neurons—potentially leaking information about the model’s architecture or the features it considers most important.

Memristor and Crossbar Array Exploits

To achieve high density, many neuromorphic designs use memristor crossbar arrays to implement synapses. While brilliant for computation, this architecture is a minefield of potential hardware exploits. A memristor’s resistance (its synaptic weight) is an analog value programmed by applying specific voltages. This physical process can be subverted.