AI Security & Red Teaming for Automotive
Automotive has formal cybersecurity obligations most industries lack: UNECE R155 requires a certified cybersecurity management system for type approval, R156 governs software updates, and connected-car platforms expose attack surface at fleet scale. Now AI systems are joining that surface: telematics models, driver-facing assistants, dealer chatbots, and internal copilots with access to engineering and warranty data. AI security red teaming for automotive attacks those systems the way an adversary would: prompt injection into vehicle-connected services, data extraction from assistants trained near proprietary designs, and manipulation of models that influence safety-adjacent decisions. We find the failures before they become a CSMS audit finding or a headline.
AI Security & Red Teaming, built for automotive
We scope the AI attack surface across vehicle-connected services, customer-facing assistants, and internal copilots with access to sensitive engineering data.
We run adversarial testing: prompt injection, jailbreaks, data exfiltration attempts, and manipulation of model-driven decisions, mapped to frameworks like OWASP LLM Top 10.
Findings are tied to your R155 threat analysis and risk assessment structure, so results strengthen your CSMS evidence rather than sitting outside it.
We retest after remediation and leave you with repeatable test suites, because AI attack surface changes with every model and prompt update.
Where it pays off in automotive
Connected-service AI testing
Adversarial assessment of AI features in companion apps and vehicle-connected services, where a compromise has R155 implications.
Internal copilot assessment
Testing whether engineering and warranty copilots leak proprietary designs, supplier terms, or pre-recall defect discussions.
Dealer chatbot hardening
Injection and abuse testing on customer-facing dealer AI before it commits you to prices, promises, or unsafe advice.
Model manipulation testing
Probing whether quality, telematics, or adjudication models can be gamed with crafted inputs at scale.
Automotive clients typically surface critical, exploitable AI weaknesses in the first engagement window, remediate before exposure, and fold the findings into R155 evidence, cutting the cost of both incidents and audits.
Automotive AI, answered
R155 expects continuous risk assessment and testing across vehicle systems and their backends, and AI features increasingly sit inside that scope. We structure findings to slot into your threat analysis and CSMS evidence, so the red team output does double duty as compliance material.
Overexposed internal assistants. Copilots connected to engineering documents, warranty data, or supplier contracts frequently leak information across permission boundaries under adversarial prompting. It is consistently more urgent than teams expect, and consistently fixable once demonstrated.
We test in controlled environments agreed with your security and engineering teams, typically staging replicas of connected services and assistants. Anything touching actual vehicle operation is scoped conservatively with your functional safety organization involved.
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