AI Inference Is Static. Attackers Know It
Introducing Adaptive NIMs: a New Approach to Resilient Inference with Automated Moving Target Defense
AI models are becoming the new API. Whether it's summarization, vision, or agentic reasoning, inference endpoints are everywhere — and they’re exposed. Unfortunately, most of today’s inference infrastructure is built like it's 2015: static containers, predictable surfaces, and slow restart times.
That’s a problem. Attackers don’t need zero-days when they have hours and days of predictable uptime.
R6 Security built something different. It’s called Adaptive AIMs, and it combines NVIDIA’s containerized inference format (NIMs) with a concept borrowed from cybersecurity and chaos engineering: Automated Moving Target Defense (AMTD).
We just released a technical white paper — and here’s a preview of what’s inside 👇
The Problem: Inference APIs Are Predictable Targets
Inference infrastructure is typically:
Deployed once, and left up indefinitely
Easy to fingerprint and scan
Slow to recover )
Not designed with active defense in mind
Attackers thrive on that predictability. They scan, probe, wait, and eventually break in. Once they do, it’s often too late to rotate credentials or containers. The window of exposure is wide open.
The Solution: Adaptive AIMs
We rotate the environment, not the model.
Using LWS (Leader Worker Sets) and Kubernetes-native controllers, we orchestrate adaptive NIMs that:
Restart on schedule (e.g. every 60 mins ± jitter)
Use memory-cached warm standby pods to cut cold start to ~15s
Don’t modify the model code or NIM containers
Integrate with Prometheus metrics for dynamic, policy-based decisions
Support zero-downtime via smart load balancing and CRDs
All of this is container-native, infrastructure-first — and compatible with how NVIDIA NIMs are built today.
Inside the White Paper
Here’s what the full document covers:
Three reference architectures, from edge deployments to cloud-scale rollouts
Threat modeling with math — formal exposure decay functions and attacker success curves
SolarWinds-style breach walkthrough — and how AMTD disrupts it
Custom CRDs for inference lifecycle, rotation, and adaptive triggers
Performance tuning, restart profiles, and caching strategies
Zero source-code modification policy for full compatibility and auditability
This isn’t theory — it’s a blueprint for real-world deployments.
Download & Contribute
📄 Download the technical white paper here: Whitepaper
🔗 Visit our website: https://www.r6security.com
We’re releasing this as a reference for the community — and we’d love your feedback.
If you’re building inference-heavy apps, deploying models across clouds, or thinking about how to protect agentic AI, reach out. We’re building something that matters.