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Adaptive Learning Defense for AI Infrastructure

Attackers use AI to reason their way in. Phorvex determines the best defense.

Attackers use AI to reason their way in. Phorvex contains them in seconds across Kubernetes and AI infrastructure, learns from every move it makes, and never breaks what is running.

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No LLM in the decision path Measured on a real cluster Zero unsafe actions Scroll to watch an attack
Fleet

Your fleet, as Phorvex sees it.

It keeps a live picture of your whole fleet and adapts as it changes. Measured from the cluster, never assumed.

Zero-day

Something lands. Nothing fires.

No signature, no anomaly, no alert. The attacker settles into a training job and quietly starts working its way across the cluster.

Decision

Phorvex decides what to move.

It determines the best defense, and refuses any move that would destroy state. Every decision is on the record. How it decides stays sealed.

Eviction

The ground moves. The job doesn’t.

Everything the attacker mapped stops working, and the training job keeps running inside its quarantine.

Proof

Measured on a real cluster, not imagined.

Zero-day scenarios with no alert, head to head against a blind rotation timer and a reactive-only tool. Every cube is a pod from the fleet above.

phorvex / ml-batchlive
Attacker dwell00.0 s 
Alerts fired0
Workloads deleted0
LLM calls0
  1. watching 3 nodes · no move needed
  2. train-0 · behaviour unchanged, no alert
  3. checkout-7f · at risk
  4. db-0 · at risk
  5. decision · 3 moves, 1 quarantine
  6. rotate train-0 · refused, stateful
  7. quarantine train-0 · job keeps running
  8. checkout-7f → checkout-2d · moved
  9. attacker cut off · 5.5 s · nothing deleted
Fleet
Zero-day
Decision
Eviction
Proof
~5.5s eviction zero-day, real cluster 100% prevention reactive tools: 0% 20/20 stateful jobs kept baselines: 0/20 0 false positives not learnable 0 LLM calls no LLM in the path
Why now

Attackers use AI as an actor. Defenders still use it as a tool.

Attackers now reason their way in and move in minutes. Most defenses still wait for a signature, a clock or a person, and none of them can tell what a move will break.

Clocks are predictable

A schedule leaks its own rhythm.

An adaptive attacker learns the rotation interval and simply works in the gaps between moves.

PhorvexThere is no schedule to learn. Moves follow the live state of the fleet.
■ attacker, in the gaps   ■ rotation
Detection is blind to the new

A zero-day emits no alert.

Reactive tools only see what they already know. They sit silent while the attacker settles in.

PhorvexActs before the attacker does, with or without an alert. It never waits for a signature.
ALERTS FIRED 0
known signatures, found · the new one, never
Naive rotation destroys state

Deleting a pod deletes its work.

Rotating a database or a training job to protect it destroys the very thing you were protecting.

PhorvexRefuses any move that destroys state, and quarantines the workload instead. 20 of 20 jobs survived.
train-0 stateful
POD DELETED · PROGRESS LOST
a timer rotating a training job
Inside the platform

Four engines. One adaptive defender.

Three engines measure, anticipate and move, around a sealed decision core. The fourth is a learning attacker that makes the defender stronger, and it is walled off from every decision.

NO PATH DECISION CORE SEALED · DETERMINISTIC PhysicsMEASURES ContextANTICIPATES MTDMOVES RL Red-TeamATTACKS
Products

Both products from day one.

Kubernetes

Proactive defense that protects production instead of breaking it. Evicts footholds before any alert fires, and never at the cost of a stateful workload.

Explore Kubernetes

AI / Inference

The same adaptive defense, extended to AI inference. It evicts attackers from your serving stack without taking the model offline.

Explore AI / Inference
Next step

See the decision brain in action.

A technical briefing, the full benchmark method, or a design-partner conversation. Your call.

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