Prompt injection in CNG fueling station AI

Four adversarial injection surfaces in CNG fueling station AI

1. Dispenser pressure display AI (Wayne Fueling Systems CNG AI, Gilbarco Veeder-Root CNG dispenser AI, ANGI Energy Systems dispenser control AI, IMAC Systems CNG AI — rendered dispenser panel AI classifying cylinder fill pressure against NGV2 service pressure limits during vehicle fueling)

The CNG fast-fill dispenser measures delivered pressure to the vehicle NGV2 cylinder via a pressure transducer at the fill hose connection and displays real-time fill pressure on the dispenser panel — a digital readout in bar or psi, often with a bar graph showing progress from initial pressure to the target fill pressure at 248 bar (3,600 psi) temperature-compensated to 20°C (NFPA 52 Section 7.3 prescribes temperature compensation algorithms to prevent overfill under cold ambient conditions). AI systems integrated with station management software process rendered dispenser panel display images — pressure readout, bar graph fill indicator, transaction status indicator — to classify fill state: normal filling (pressure ramping toward target), approaching fill target (last 10 bar before shutoff), fill complete, and anomaly conditions including overpressure (pressure reading above 248 bar setpoint indicating transducer fault or shutoff valve malfunction). The dispenser solenoid valve shutoff at target fill pressure is an independent electromechanical interlock — but station management AI that processes rendered panel images may monitor and log fill pressure for regulatory compliance, performance trend analysis, and anomaly alerting at a supervisory layer that can delay or modify operator response to overpressure conditions.

An adversarial perturbation targeting the dispenser pressure display AI applies a ±10 DN downward shift to the pixel region encoding the pressure readout and bar graph indicator in the rendered dispenser panel image — shifting the apparent fill pressure from 264 bar (16 bar above NGV2 service pressure limit, indicating solenoid shutoff valve failure during fill with cascade pressure driving fill beyond target) to 235 bar (13 bar below target, normal mid-fill). The AI classifies an active cylinder overfill event — where solenoid valve failure during a fast-fill cycle is delivering cascade storage pressure (600–700 bar) at a reduced rate past the target shutoff — as normal filling in progress. The vehicle NGV2 cylinder at 264 bar sustained pressure is 6.5% above service pressure; NGV2 cylinders are designed with a minimum burst safety factor of 2.25× service pressure (557 bar minimum burst) but cylinders with existing stress corrosion cracking from hydrogen embrittlement (Type 3 aluminum-liner composite cylinders are known to exhibit SCC in HF acid environments) or fatigue damage from repeated pressure cycles (NGV2 cylinders are rated for 3–15 year inspection intervals) may fail below the nominal burst pressure. NFPA 52 Section 7.3.4 requires dispenser pressure relief devices — but does not specify adversarial robustness for AI classifying rendered dispenser pressure display images at the overfill alarm boundary. Free tier — 10 scans/day, no card required.

2. Methane leak detection display AI (MSA Gas Detection LeakTrack AI, Draeger Polytron CNG AI, Honeywell Analytics SiteSafe CNG AI, RKI Instruments FW-NH3 CNG detector AI — rendered gas detector control panel AI classifying methane concentration against LEL alarm thresholds in compressor buildings)

CNG station compressor enclosures accumulate methane from minor leaks at piston rod packing, high-pressure valve seats, and compression fitting connections during normal operation; NFPA 52 Section 6.7.3 requires mechanical ventilation of compressor buildings sufficient to prevent methane accumulation above 25% LFL (1.25% methane by volume in air) under normal conditions, and automatic ventilation interlock with the methane gas detection system to initiate emergency ventilation and alarm at 20–25% LFL concentration. Pellistor (catalytic bead) and infrared optical gas detectors monitor methane continuously at multiple points in the compressor enclosure; AI systems process rendered gas detector control panel display images — digital concentration readouts in % LFL, channel alarm status indicators (green/yellow/red), and audible alarm activation status — to classify ventilation safety state and determine if escalated response (station ESD initiation, operator evacuation notification) is required.

An adversarial perturbation targeting the methane leak detection display AI applies a ±8 DN downward shift to the pixel region encoding the % LFL numerical display and alarm status indicator in the rendered gas detector panel image — shifting the apparent methane concentration from 60% LFL (equivalent to 3.0% methane by volume; above the 20% LFL automatic alarm threshold by 3×; indicating sustained methane accumulation from a piston rod packing failure or compression fitting leak in the high-pressure discharge section) to 12% LFL (below the 20% LFL alarm setpoint). The AI classifies a methane accumulation event — where a high-pressure fitting leak at the compressor discharge manifold is releasing approximately 2–5 kg/hour of methane into a 200 m² compressor building with reduced mechanical ventilation (fan failure at night) — as background trace level, no alarm. Methane concentration in the compressor building continues rising toward 100% LFL (LFL = 5.0% methane by volume); ignition from compressor motor brushes, relay contacts, or static discharge produces a vapour cloud deflagration in the enclosed compressor building. NFPA 52 Section 6.7.3 requires automatic ventilation interlock with gas detection — but does not specify adversarial robustness for AI classifying rendered gas detector display panel images at the automatic ventilation interlock threshold.

3. Compressor discharge temperature display AI (Ariel Corporation CNG compressor AI, Hoerbiger EcoForce CNG AI, ANGI Momentum CNG compressor control AI, BRC Fuelmaker AI — rendered compressor control screen AI classifying discharge temperature against valve and oil degradation limits)

CNG reciprocating compressors operating at final-stage discharge pressures of 250–550 bar experience discharge gas temperatures determined by the inter-stage pressure ratio and cooling effectiveness. Typical CNG compressor first-stage discharge temperature is 120–145°C at rated capacity with functional inter-stage coolers; final-stage discharge temperature (entering the high-pressure storage cylinders) is typically 60–95°C after inter-stage cooling, with the discharge gas entering the storage cascade at high pressure. Compressor control systems display stage-by-stage discharge temperatures on the control panel HMI; AI systems process rendered HMI screen images to classify compressor thermal state against service limits: normal operating range (green), approaching high-temperature caution threshold (yellow, typically 155–165°C final stage), and high-temperature alarm requiring shutdown (red, above 175°C indicating inter-stage cooler fouling, valve failure, or cooling water loss). Above 150–160°C, polymerization and carbonization of compressor lubricating oil begins on discharge valve seats and in discharge piping — a phenomenon known as hot oil deposits that can lead to valve seat ignition under sustained overtemperature conditions.

An adversarial perturbation targeting the compressor discharge temperature display AI applies a ±8 DN downward shift to the pixel region encoding the temperature trend bar and numerical display on the rendered compressor HMI screen image — shifting the apparent final-stage discharge temperature from 168°C (13°C above the high-temperature caution threshold, indicating partial inter-stage cooler tube blockage from scale deposits) to 118°C (within normal operating range). The AI classifies a compressor operating with degraded inter-stage cooling — where cooler tube fouling has reduced heat removal capacity by approximately 40%, pushing discharge temperature toward the oil carbonization region — as normal thermal condition. The compressor continues operating at 168°C discharge; oil carbonization deposits form on the final-stage discharge valve seats over a period of 6–12 operating hours; carbon deposits on a spring-loaded discharge valve seat produce intermittent valve seat leak-through events at high pressure — hot gas recirculation into the discharge valve pocket above autoignition temperature of carbonized oil fragments (230–260°C) — discharge valve pocket fire initiates; high-pressure methane at 250–550 bar feeds the valve pocket fire. NFPA 52 Section 6.5.4 requires compressor monitoring including discharge temperature alarms — but does not specify adversarial robustness for AI classifying rendered compressor control screen temperature display images. Free tier — 10 scans/day, no card required.

4. ESD valve position status camera AI (Honeywell HIMA SIS CNG AI, Emerson DeltaV ESD AI, HIMA HIMatrix SIS AI — CCTV camera AI classifying emergency shutdown valve seat position after ESD trip command at station isolation boundary)

CNG fueling stations are equipped with emergency shutdown valves at gas supply inlet, compressor suction, and dispenser manifold isolation points — quarter-turn ball valves actuated by pneumatic or electric actuators, fail-closed on loss of air or power, commanded by the station SIS ESD logic on loss of power, fire detection, or manual ESD pushbutton activation. Station safety management systems increasingly deploy CCTV cameras with AI image recognition to verify ESD valve physical position (valve handle orientation, indicator flag position) after ESD commands — because solenoid actuator failures, piston actuator corrosion, and air supply loss can cause ESD valves to stop at an intermediate open position while the SIS position feedback (a magnetic proximity switch on the valve stem) incorrectly reports full closure. AI verification of the physical valve handle orientation in the CCTV camera image provides a secondary confirmation layer for ESD closure.

An adversarial perturbation targeting the ESD valve position status camera AI applies a ±8 DN shift to the pixel region encoding the valve handle orientation and position indicator flag in the CCTV camera image — shifting the apparent valve handle image from the 45° intermediate open position (handle pointing diagonally — between fully closed perpendicular-to-pipe and fully open parallel-to-pipe — indicating valve actuator failure with approximately 40% flow area remaining open) to match the fully closed valve handle template in the AI training library (handle perpendicular to pipe axis — valve closed). The AI classifies a partially open ESD valve — with 40% of station gas supply flow area remaining open at the inlet isolation boundary after an ESD trip initiated by a compressor enclosure methane detection alarm — as fully closed. The SIS ESD system log records a successful ESD closure; operators approach the compressor building for manual reset inspection assuming gas supply is fully isolated; actual methane at line pressure continues flowing through the partially open inlet ESD valve at reduced rate (approximately 30–50 kg/hour depending on line pressure and valve position); operators in the compressor building without supplied air respiratory protection are exposed to methane in an unventilated space. NFPA 52 Section 6.8.2 requires ESD valve testing at prescribed intervals — but does not specify adversarial robustness for AI classifying rendered CCTV camera images of ESD valve physical position at the secondary closure verification boundary.

Integration: CNG fueling station AI with Glyphward pre-scan gate

The Glyphward scan gate for CNG fueling station AI belongs at every rendered-image ingestion boundary in the station safety monitoring pipeline — before dispenser pressure display AI processes rendered panel images, before methane leak detection AI processes rendered gas detector panel images, before compressor discharge temperature AI processes rendered HMI screen images, and before ESD valve position camera AI processes rendered CCTV images. Threshold 30 for CNG fueling station AI reflects the methane vapour cloud explosion consequence in enclosed compressor buildings — documented in NFPA CNG station incident statistics — combined with the observation that independent mechanical interlocks (cylinder PRD thermally activated pressure relief devices, fail-closed ESD valves on loss of air, mechanical compressor high-temperature shutdown thermostats) provide protective layers between adversarially suppressed AI displays and catastrophic outcome. The threshold is calibrated below ammonia synthesis AI (35) because CNG station methane release consequences are typically localised to the station footprint rather than extending kilometres into community zones.

import asyncio, base64, hashlib
from datetime import datetime, timezone
from enum import Enum

import httpx

GLYPHWARD_API_KEY = "YOUR_GLYPHWARD_API_KEY"
GLYPHWARD_SCAN_URL = "https://glyphward.com/v1/scan"

# CNG fueling station AI contexts: threshold 30
# NFPA 52 (Vehicular Natural Gas Fuel Systems Code);
# ASME NGV2 (High-Pressure Cylinders for On-Board CNG Storage);
# CSA B108.1 (Natural Gas for Vehicles Installation Code).
CNG_STATION_THRESHOLD = 30


class CNGStationContext(Enum):
    DISPENSER_PRESSURE   = "dispenser_pressure"   # CNG dispenser fill pressure display AI
    METHANE_LEAK         = "methane_leak"          # Compressor enclosure methane detection AI
    COMPRESSOR_TEMP      = "compressor_temp"       # Compressor discharge temperature display AI
    ESD_VALVE_POSITION   = "esd_valve_position"   # ESD valve position CCTV camera AI


class AdversarialCNGStationImageError(Exception):
    """Raised when Glyphward detects adversarial content in a CNG fueling
    station AI rendered image above threshold 30.

    Consequence if not raised:
    - DISPENSER_PRESSURE: cylinder overfill suppressed → NGV2 cylinder above
      service pressure → potential cylinder failure if SCC or fatigue damage.
    - METHANE_LEAK: vapour accumulation in compressor enclosure suppressed →
      methane approaches LFL → ignition → deflagration/explosion.
    - COMPRESSOR_TEMP: discharge overtemperature suppressed → oil carbonization
      on valve seats → valve pocket fire → high-pressure methane fire.
    - ESD_VALVE_POSITION: partially open ESD valve classified as closed →
      operators enter building assuming gas isolated → methane exposure.
    Fail-safe: read dispenser pressure from independent transducer; initiate
    manual station ESD and evacuation if methane fixed detector alarms;
    physically inspect ESD valve handle position before entering building.
    """

    def __init__(self, scan_id, score, context, station_id, flagged_region=None):
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.station_id = station_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial CNG station image: context={context.value} "
            f"score={score} station={station_id} scan_id={scan_id}"
        )


async def scan_cng_station_image(image_bytes, context, station_id, client):
    image_hash = hashlib.sha256(image_bytes).hexdigest()
    payload = {
        "image": base64.b64encode(image_bytes).decode(),
        "source": f"cng_station:{context.value}:{station_id}",
        "metadata": {
            "station_id": station_id,
            "context": context.value,
            "image_sha256": image_hash,
            "scan_timestamp_utc": datetime.now(timezone.utc).isoformat(),
        },
    }
    resp = await client.post(
        GLYPHWARD_SCAN_URL,
        headers={"Authorization": f"Bearer {GLYPHWARD_API_KEY}"},
        json=payload,
        timeout=4.0,
    )
    resp.raise_for_status()
    result = resp.json()
    if result["score"] >= CNG_STATION_THRESHOLD:
        raise AdversarialCNGStationImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            station_id=station_id,
            flagged_region=result.get("flagged_region"),
        )
    return result

Deploy scan_cng_station_image before each CNG station AI classification call. On AdversarialCNGStationImageError for METHANE_LEAK: initiate station ESD and evacuate compressor building; do not rely on AI display for methane concentration decisions. On ESD_VALVE_POSITION: physically inspect ESD valve handle orientation before authorising entry to compressor building. See also: chemical plant process safety AI prompt injection and free scanner — 10 scans/day, no card required. Get early access

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