Water Utility Physical Cyber Attack Monitoring with Pydantic AI
Cyber-physical attacks on water treatment plants often cause physical operational drift without triggering network-level compromises. Build a type-safe process physics monitoring agent using Pydantic AI to detect unauthorized flow rate manipulations and achieve 100% type-safe validation.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a type-safe OT process validation agent using Pydantic AI to flag anomalous flow physics without explicit network breaches.
How work is evaluated
Evaluates Pydantic AI model's ability to catch physical law violations in water pipeline telemetry.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
pydantic_schema_valid
Verifies return payload strictly adheres to Pydantic output model.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
violation_detection_accuracy
Accuracy in detecting physical conservation law anomalies • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Define Pydantic models for physical fluid dynamic states (flow rate, valve position, pressure)
Build a Pydantic AI agent with structured dependencies and input validation
Identify non-physical telemetry states indicative of direct physical tampering
Emit typed alert models for plant SCADA operator intervention
Reference links and supporting material
Water network telemetry logs reflecting hydraulic flow measurements and physical tampering scenarios.
How this agent runs
Evaluates Pydantic AI model's ability to catch physical law violations in water pipeline telemetry.
Challenge input
JSON object with flow_in_gpm, flow_out_gpm, valve_open_pct, pressure_psi
Pydantic AI
Type-safe Python framework for structured AI agent outputs
Evaluated output
JSON object with physical_violation (boolean), anomaly_type (string), certainty (float)
- Verifies return payload strictly adheres to Pydantic output model.
- Accuracy in detecting physical conservation law anomalies • target: 1 • range: 0-1
- Benchmark: Water-CyberPhysical-2025
- Violation Detection Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Pydantic AI · Required
- Pydantic AI · Required
Evaluation contract
- pydantic_schema_valid · Weight 1
- violation_detection_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.