When Edge AI makes decisions in the cockpit, you need more than LLM-based guardrails
In-vehicle voice assistant, a multimodal agentic AI application is no longer UI layers. They reason,decide, and act. In-vehicle AI security cannot come at the cost of interaction quality. Yet most existing AI guardrails are cloud-based, creating critical challenges for edge AI deployments:
Latency
Cloud enforcement slows inference and degrades a smooth cockpit experience
Resource Overhead
Heavy guardrails consume memory and impact system performance
Integration Friction
Security not designed for edge AI delays SOP and struggles to keep up with emerging attack techniques
xPhinx: Secure Edge AI Interaction Without Delay or Overhead
Risk-based AI Security Protection for In-Vehicle Edge AI
xPhinx protects in-vehicle edge AI and AI agents from prompt injection, jailbreak, unsafe behaviors, and data leakage, without slowing down AI intelligence cockpit interaction. Powered by automotive threat intelligence, xPhinx keeps pace with evolving prompt attacks and jailbreak techniques, inspecting and sanitizing LLM inputs and outputs to stop manipulated or unsafe behavior where AI decisions are made.
Enforce AI Security With Minimal Performance Impact
Unlike LLM-based guardrails, xPhinx is purpose-built for in-vehicle edge AI models (LLM/VLM). Its lightweight architecture operates directly on the device, achieving:
- Up to 70%* faster execution
- Up to 90%* lower memory usage
All without retraining, modifying, or upgrading existing AI models.
*Compared with LLM-based guardrails.
Context-Aware, Tiered Protection for In-Vehicle AI
xPhinx uses a dual-layer, risk-aware design: a lightweight first layer runs continuously, while deeper intent analysis is activated only when higher-risk behavior is detected. This approach delivers strong AI security without impacting AI application performance across diverse smart-cockpit applications. All VicOne edge software aligns with the ASPICE CL2 product and project requirements.
Built for Vehicles
xPhinx vs. LLM-Based Guardrails
Cloud and LLM-based guardrails were designed for content and service safety, not for an edge AI-driven smart cockpit that directly influences vehicle behavior and seamless user interaction.
| LLM-Based Guardrails | xPhinx | |
|---|---|---|
| Designed for Edge AI smart cockpit | Limited; high cost & latency | Yes |
| Privacy and data residency | Data send to cloud guardrail | 100% local processing |
| Resource requirement | High (GPU/NPU), substantial RAM; not for Edge AI | Low; design for Edge AI |
| Availability | Need internet connection | 100% offline |
| User experience impact | Yes | User undetectable |
| Continuously automotive and AI attack techniques updated | Limited, no dedicated security threat intelligence | Supported by VicOne automotive threat intelligence |
FAQ for OEMs, IVI Platforms, and AI Model Providers
Does xPhinx require changes to our AI models?
No. xPhinx operates alongside existing models and requires no retraining or modification.
Does on-device protection impact AI response time?
xPhinx is designed for real-time edge execution with minimal latency and reduced memory footprint.
Can xPhinx be deployed selectively across AI frameworks or operating systems?
Yes. xPhinx supports multiple hooking methodologies to intercept LLM inputs and outputs.
How does xPhinx support automotive compliance?
xPhinx supports risk management thinking aligned with ISO/SAE 21434 and UN R155, and is developed under ASPICE CL2 processes.
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