Red Team, Standing By: Securing Automotive AI

Machine learning (ML) is driving remarkable innovation in the automotive sector, powering features that were once the stuff of science fiction. From advanced driver assistance to fully autonomous navigation, ML offers capabilities beyond the reach of traditional software engineering. Yet this leap forward brings with it a new class of risk. Unlike conventional software bugs, ML failures often emerge from the very nature of the technology itself. To preserve the safety and security we demand from vehicles, our approach to assurance must evolve. 

Why Does Automotive AI Fail?  

Failures in ML systems tend to fall into two broad categories: 

Intentional failures (the adversary) – malicious actors can exploit vulnerabilities in ways that seem almost trivial yet carrying significant consequences. Research has demonstrated that a few strategically placed stickers can render a stop sign invisible to a vehicle’s vision system, creating a real-world safety hazard. 

Unintentional failures (the blind spot) – here, the system falters not because of malice, but because it was never trained to recognise the situation. Unusual weather patterns, unexpected road layouts, or rare combinations of objects can all trigger unpredictable responses. 

Both forms of failure are inevitable in ML, but neither is acceptable in vehicles that must operate with a near-zero margin for error. 

The industry needs to go the extra mile: verifying that a system works and start actively searching for the scenarios where it won't.  

The solution is to adopt a philosophy of proactive red teaming: a continuous, adversarial process designed to break things on purpose, so they don’t break by accident. This requires a culture of proactive discovery with permanent red teams whose function is to find failures before the real world does. 

How to Expose Hidden Risks 

A resilient red teaming strategy rests on two complementary approaches: 

1. Disciplined adversarial testing - probing of the ML pipeline to hunt for exploits. This method treats the system as an opponent to be challenged, rather than a tool to be trusted blindly. 
2. SOTIF analysis (Safety of the Intended Functionality) - a creative process that seeks to anticipate the unimaginable, exploring operational boundaries and searching for flaws hidden in rare or unconsidered scenarios. 

The findings from these processes must feed into a scalable validation framework, such as the UNECE’s New Assessment/Test Method (NATM). By combining simulation, test-track validation, and public road trials, organisations can ensure that vulnerabilities discovered by red teams are systematically tested and resolved. 

A Cultural Shift in Assurance 

Standard testing protocols are no longer enough. Securing ML in automotive systems requires more than technical fixes! 

Safety and security can no longer be one-off milestones; they must become a philosophy of continuous assurance. The threat landscape is dynamic, with adversaries evolving as quickly as the technologies they target. Embedding permanent red teams into the lifecycle ensures that discovery, testing, and mitigation never stop. 

By embracing this mindset, the industry moves beyond reacting to failures after they occur. Instead, it builds verifiable trust: a trust not based on hope, but on evidence that systems have been systematically challenged, stressed, and strengthened. 

This is the foundation for the future of mobility: vehicles that are not only intelligent, but also resilient. 

With Critical Software’s strategic framework, the automotive industry can go further than simply responding to safety incidents. Our team anticipate them, hunt the unknowns, and build the deep, verifiable trust essential for the road ahead. 

Ready to go deeper? Download our white paper to explore how proactive red teaming and continuous assurance can secure automotive AI.