Vector similarity can help verify firmware integrity in self-driving cars by comparing mathematical representations of firmware code to detect unauthorized changes. Firmware is a critical component in autonomous vehicles, controlling sensors, decision-making, and hardware interactions. By converting firmware into numerical vectors—such as embeddings derived from code structure, checksums, or behavior patterns—developers can measure similarity between a trusted baseline and the active firmware. If the vectors deviate beyond a predefined threshold, it signals potential tampering or corruption, triggering alerts or fail-safe mechanisms.
For example, a self-driving car’s firmware might be split into functional modules (e.g., lidar processing or brake control). Each module could be hashed and transformed into a vector using techniques like SHA-256 combined with feature extraction. During verification, the system computes cosine similarity between the current module vectors and those stored in a secure, read-only memory. If a module’s vector similarity drops below 0.95 (on a scale of -1 to 1), it could indicate unauthorized modifications. Machine learning models could also generate embeddings that capture semantic code patterns, making it harder for attackers to insert malicious code without altering the vector’s “shape.” For instance, a compromised neural network model in an autonomous system might show vector drift even if the file size or checksum appears unchanged, as the embedding reflects the code’s functional logic.
Practical implementation requires balancing accuracy and performance. Generating vectors for large firmware binaries might involve chunking code into smaller segments or using locality-sensitive hashing (LSH) to speed up comparisons. Secure storage of baseline vectors is critical—hardware security modules (HSMs) or trusted platform modules (TPMs) can protect these references. Real-time checks could run during boot-up or periodically during operation, with lightweight algorithms to minimize latency. For example, a vehicle’s onboard computer might use precomputed vectors for critical subsystems, cross-referencing them during sensor calibration phases. This approach ensures integrity without disrupting time-sensitive operations, providing a scalable way to safeguard against both accidental corruption and targeted attacks.