EMBRACE
THE CHAOS
If they can't see it, they can't attack it. Phoenix continuously rotates your infrastructure so attackers never get a stable target.
Static Defense Is a Sitting Duck
of attacks are reconnaissance and planning
MEET PHOENIX
Chaos engineering meets cybersecurity. Phoenix continuously rotates your workloads — turning infrastructure into a moving target attackers can't pin down.
Continuous Mutation
→IPs and identities change before attackers finish scans. Every probe hits a different target.
Self-Healing Workloads
→Automatic regeneration to known-good baselines. Specialized for NVIDIA NIMs.
Zero-Trust Runtime
→Compromised credentials are worthless — the environment is already gone.
Zero Dwell Time
→Eliminates lateral movement. Defeats persistence attacks entirely.
What happens when
the maze fights back
One Platform. Two Missions.
Secure your container infrastructure and your AI inference pipelines from a single control plane.
Move the Target
Automated Moving Target Defense for Kubernetes clusters. Continuous pod rotation, dynamic network obfuscation, and self-healing workloads — no agents, no code changes.
- —Automated Pod Rotation
- —Dynamic Network Obfuscation
- —Falco-Integrated Response
- —Helm / Terraform / Operator
For DevSecOps, Platform Leads, SREs
Learn More→Secure the Inference
Purpose-built defense for AI inference pipelines. Protect GPU resources, model endpoints, and training data from model theft, LLM jacking, and prompt injection.
- —GPU Pipeline Mutation
- —Context-Aware Defense
- —Inference Endpoint Ephemerality
- —NIM-Specific Optimization
For AI/ML Infrastructure, CTOs, ML Engineers
Learn More→Five Steps to Moving Target Defense
From signal to defense. No agents, no code changes, no disruption to your pipeline.
Collects Signals
Monitors Kubernetes workloads and AI runtimes for risk indicators across your entire cluster.
Analyzes Context
Controller decides if workloads need rotation or adaptation based on telemetry and threat signals.
Rotates Workloads
Containers and AI runtimes refreshed continuously, invalidating attacker footholds.
Adapts Security
Container settings auto-adjust based on risk signals. Every rotation is a hardening opportunity.
Runs Seamlessly
No agents, no code changes, no disruption. 1-2% overhead. Zero downtime.
Seven Vectors. Zero Persistence.
Each attack vector neutralized by making the target disappear.
Model Theft / Tampering
Continuous workload rotation prevents container hijacking
Credential Harvesting
Container mutation and short-lived runtimes invalidate stolen creds
Training Data Poisoning
Rotating pipelines disrupt long-running poisoning campaigns
Prompt Injection & LLM Jacking
Runtime variability defeats memory manipulation attacks
Ransomware
Self-healing + zero trust = no static targets to encrypt
Model Inference Manipulation
Ephemeral endpoints + context-aware defense
Session Hijacking
Static endpoints don't exist long enough to hijack
The Real Cost
Annual global cybercrime cost. Average breach takes 277 days to detect. 68% involve human error or misconfiguration. Static defense is a liability.
2025–2026 “Static Defense” Failures
CVE-2025-1097 / Ingress-NGINX Critical RCE
The Attack: A chain of unauthenticated Remote Code Execution vulnerabilities in the Ingress NGINX Controller. Attackers could bypass static network controls to gain full control of the cluster.
The AMTD Cure: Even if an attacker gains entry via an Ingress zero-day, Phoenix K8s invalidates their foothold. By the time they attempt lateral movement, the target pod and its network context have already mutated.
The Shai-Hulud 2.0 Worm
Supply ChainNPM Supply Chain Campaign (Jan 2026)
The Attack: A massive malware campaign affecting 25,000+ repositories. The worm automatically harvested secrets and published malicious versions of any package it could access — moving across cloud environments at 1,000 new repos every 30 minutes.
The AMTD Cure: Static secrets and static CI/CD pipelines are the "fuel" for this worm. Phoenix Metadata Obfuscation makes secrets and tokens unreadable to unauthorized automated scripts, stopping the worm’s self-replication in its tracks.
CVE-2025-33245: RCE via Malicious Data Injection
The Attack: A critical vulnerability in the NVIDIA NeMo Framework allowing full code execution through insecure deserialization. This specifically threatened model weights and inference results.
The AMTD Cure: Phoenix AI ensures that the NeMo inference environment is ephemeral. If an attacker injects a malicious payload to steal model weights, the underlying pod is rotated before the data exfiltration can complete, severing the attacker’s connection.
CISA KEV Addition: CVE-2026-20127
The Attack: Active exploitation (detected Feb 2026) of an authentication bypass in Cisco Catalyst SD-WAN. Attackers gained administrative access to rewire entire networks and create persistent backdoors.
The AMTD Cure: This attack relies on the persistence of the management interface. AMTD logic applied to management clusters ensures that administrative sessions are constantly re-validated and the underlying infrastructure is never static enough for a long-term backdoor to take root.
Real incidents. Real CVEs. Static infrastructure made each one possible — Automated Moving Target Defense would have stopped them.
Built for How You Actually Work
Phoenix fits into your existing pipeline. No new agents, no new dashboards, no vendor sprawl.
Phoenix in the Field
Real deployments. Real results. See how teams use Phoenix to eliminate their static attack surface.
Eliminating Dwell Time in Production Clusters
A financial services platform reduced their attack surface exposure from weeks to zero by deploying Phoenix's automated pod rotation.
Securing Inference Pipelines at Scale
An AI company running NVIDIA NIMs deployed Phoenix to protect GPU-intensive inference endpoints from model theft and LLM jacking.
From Reactive SOC to Autonomous Defense
A global enterprise replaced manual incident response with Phoenix's self-healing infrastructure, cutting mean time to recovery by 94%.
Ready to Move
the Target?
Deploy Phoenix OSS and turn your static clusters into moving targets.
Get Started→Secure Your
Inference
Purpose-built protection for AI inference — models, pipelines, and GPU resources.
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