SECURE THE
INFERENCE
PIPELINE
Models are the new crown jewels. Phoenix wraps a mutating defense shell around your AI workloads — NVIDIA NIMs, LangChain agents, inference endpoints.
Models Are Crown Jewels
Whether it's an NVIDIA NIM or a custom LLM pipeline, AI endpoints are static and exposed. Attackers don't just want into your network — they want to steal your model weights, poison your training data, or hijack your expensive GPU compute for their own use.
Predictable deployments and static infrastructure configurations create vulnerabilities that adversaries exploit to compromise models and steal sensitive data. The inference layer is the new perimeter — and it's wide open.
LLM-Jacking
Attackers hijack GPU compute for unauthorized inference, running their own models on your infrastructure.
Model Theft
Exfiltrating proprietary model weights and training data through persistent access to static endpoints.
Prompt Injection
Manipulating model behavior through crafted inputs that exploit predictable serving environments.
Inference Manipulation
Tampering with model outputs in production by intercepting static API paths.
PHOENIX
A mutating defense shell wrapping your AI workloads. Phoenix adapts its defense posture based on what your models are doing, not just what attackers might do.
Inference Pipeline Mutation
Endpoints rotate, model access paths change, attack surface shifts continuously.
Context-Aware Defense
Defense adapts based on inference load, model type, and threat telemetry.
GPU Resource Protection
Prevents compute hijacking, resource theft, and unauthorized model inference.
Zero-Trust Model Access
Ephemeral credentials and rotating access patterns for model endpoints.
Inference Pipeline Mutation
Phoenix rotates the specific pods handling AI requests. By the time a prompt injection sequence is attempted across multiple requests, the session context is invalidated. Broken session persistence for AI attackers.
NVIDIA NIM Hardening
Deep integration with NVIDIA's inference microservices. Phoenix monitors GPU utilization and terminates shadow compute processes that signal unauthorized model-scraping. Prevention of model theft and LLM-Jacking.
Agentic Defense Logic
Phoenix uses a security agent to observe the I/O of your AI models. If it detects a malicious payload, it triggers an immediate panic mutation — moving the model to a clean, isolated node. Real-time isolation of compromised instances.
Every AI Stack. One Defense Layer.
- —GPU OptimizedDesigned for GPU-intensive workloads. Rotation respects CUDA contexts, model loading times, and inference latency requirements. 1-2% overhead even with large models.
- —Framework AgnosticWorks with LangChain, LlamaIndex, NVIDIA NIM, vLLM, and any inference framework. No framework-specific agents or SDKs.
- —Data Privacy FirstModels and data never leave your infrastructure. Phoenix operates at the infrastructure layer — zero visibility into model weights or training data.
- —NeMo Guardrails CompatibleLayered defense: NeMo filters malicious prompts at the application layer while Phoenix evicts persistent attackers at the infrastructure layer.
Secure your inference pipeline
Purpose-built protection for AI workloads — models, GPU resources, and inference endpoints.