Prompt injection in offshore platform helideck operations AI

Four adversarial injection surfaces in offshore platform helideck operations AI

1. Helideck status lamp camera AI (Petair helideck lamp monitoring AI, Safe Landing Solutions helideck camera AI, Axis Communications helideck CCTV AI — camera-based AI classifying helideck status lamp state to determine deck clear or not-clear status for inbound helicopter approach)

The helideck status lamp — a traffic-light assembly typically mounted at the edge of the helideck or on an adjacent structure visible to an approaching helicopter from the primary approach sector — displays the helideck operational state: green (helideck clear, landing authorised), red (helideck not clear, do not land, or Helicopter Landing Officer (HLO) has not yet given clearance), or amber/flashing red (emergency landing only or deck preparation in progress). CAP 437 Section 3.6 specifies the status lamp requirements: the lamp must be clearly visible to the approaching pilot in all ambient lighting conditions; it must be capable of displaying at least the green (clear) and red (not clear) states; and status lamp changes must be made by or under the direct authority of the HLO. An AI system processing CCTV camera images of the helideck status lamp area can classify the lamp state as green (deck clear) or red (deck not clear) based on the colour rendering in the camera image — either as a ground-based decision-support tool at the Offshore Installation Manager (OIM) desk or as an automated deck clearance logging system.

An adversarial perturbation targeting the helideck status lamp camera AI applies a ±8 DN shift to the pixel region encoding the lamp colour in the rendered CCTV camera image — shifting the red status lamp pixel values toward the green channel, causing the AI to classify a red (not-clear) status lamp as green (deck clear). The AI records deck status as “cleared for landing” in the flight operations log at the OIM workstation. The inbound AS332 L2 Super Puma helicopter, on a crew change rotation with 17 passengers and 2 crew, receives deck-cleared confirmation from the radio operator who reads the AI-logged deck status without visual confirmation of the physical lamp. The helideck still has the previous inbound helicopter at the touchdown/positioning (T/P) marking — engines running, preparing to receive new passengers — when the inbound helicopter enters the final approach at 250 m range. The two-helicopter collision risk on an occupied helideck, combined with the fire and rotor wash hazard to persons on the deck, represents the primary consequence of a status lamp camera AI misclassification. CAP 437 Section 3.6.3 specifies that the HLO must confirm deck clear by visual check before issuing green status — but does not specify adversarial robustness for AI systems classifying rendered status lamp camera images used in deck clearance logging or decision support. Free tier — 10 scans/day, no card required.

2. Wind direction vane and anemometer display AI (Fugro METOCEAN helideck wind AI, Vaisala helideck WMT52 ultrasonic wind display AI, Gill Instruments WindSonic helideck AI — rendered digital wind direction and speed display AI classifying crosswind component and gust exceedance for helicopter approach authorisation)

Offshore helicopter operations are subject to meteorological landing limits specified in the helicopter operator’s Operations Manual and in CAP 437 — including maximum surface wind speed (typically 60–65 knots mean wind for most offshore helicopter types), maximum crosswind component (typically 35 knots for a 90° crosswind on the primary approach heading), and minimum required visibility and cloud ceiling. CAP 437 Section 2.10 requires that each helideck have a calibrated anemometer and wind direction vane with digital readouts visible to the HLO and accessible to the helicopter pilot via radio link. The crosswind component for any given wind direction and speed is calculated from the angle between the wind direction and the helicopter’s primary approach heading: at 90° (full crosswind), the component equals the full wind speed; at 0° (directly into wind or tail wind), the crosswind component is zero. AI systems process rendered images of the helideck wind display (a digital readout showing mean wind speed in knots, gust speed, wind direction in degrees, and often a computed crosswind component bar chart) to classify whether current wind conditions are within the operational envelope for the inbound helicopter type.

An adversarial perturbation targeting the wind direction vane display AI applies a ±10 DN shift to the pixel region encoding the wind direction value in the rendered digital display — shifting the apparent wind direction from 080° relative to the helideck primary approach heading (a near-full crosswind condition at 40 knots mean speed, producing a crosswind component of 39 knots, above the 35-knot limit for the Boeing 234 Chinook type in service on the installation) to 015° relative (a nearly head-on wind, producing a crosswind component of 10 knots, well within limits). The AI classifies the wind conditions as within the helicopter operational envelope for the primary approach heading. The HLO, relying on the AI-processed display readout rather than the raw anemometer instrument, authorises the approach on the primary heading. The inbound helicopter encounters the actual 40-knot crosswind during the final approach at 50 m range and 30 m altitude; the crosswind at this stage produces a lateral drift rate that exceeds the pilot’s ability to correct without aborting: the helicopter experiences a dynamic rollover event if the pilot attempts to continue or a near-miss wave strike if forced to abort into the sea surface. CAP 437 Section 2.10 specifies wind measurement equipment requirements — but does not address adversarial robustness for AI systems classifying rendered wind direction display images used in operational envelope assessment.

3. Helideck thermal fire detection camera AI (Axis Communications AXIS Q19 helideck thermal AI, Bosch AVIOTEC helideck fire detection AI, Hanwha Q-series helideck thermal AI — thermal infrared camera AI classifying helideck surface and substructure fire or thermal anomaly during and after helicopter operations)

Helideck fire risk arises from: helicopter fuel spillage during refuelling operations on the deck; hydraulic fluid leaks from the helicopter landing gear or rotor head that ignite from engine exhaust or hot parts; aviation fuel ignition from a hard landing that ruptures the fuel cell; and platform structural fires propagating to the helideck level from below through penetrations in the helideck plating. CAP 437 Section 3.7 requires that offshore helidecks have a fixed fire detection system capable of detecting a fire on or below the helideck surface and initiating an alarm at the HLO station and OIM console. AI systems process rendered thermal infrared camera images of the helideck surface — which show temperature distribution as false-colour or greyscale images, with normal deck temperatures in the 5–30°C range and engine exhaust plumes or jet pipes at 200–400°C — to classify thermal anomalies as: normal (no hot zones), engine exhaust normal (within expected hot zone from helicopter type and position), fuel fire signature (distributed hot zone above 100°C extending beyond the engine exhaust cone), or structural fire (hot zone below deck plating detected through elevated deck surface temperature above 60°C).

An adversarial perturbation targeting the helideck thermal fire detection camera AI applies a ±8 DN downward shift to the pixel region encoding the temperature false-colour in the rendered thermal camera image — shifting the apparent deck surface temperature from 145°C (a localised aviation fuel fire from a fuel spillage during wet deck refuelling operations, visible as a red-white zone in the thermal image centred on the fuel drain point) to 35°C (classified as elevated but within the warm-deck-in-sunlight background range). The AI classifies an active small fuel fire on the helideck surface as elevated deck background temperature — not a fire condition. No fire alarm is initiated at the HLO station; the fire suppression system foam cannon is not activated; the HLO does not issue a deck not-clear status. An inbound helicopter continues its approach to the helideck; as the helicopter rotor wash reaches the helideck surface during the final hover at 5 m height, the rotor wash disperses the burning aviation fuel across a wider area of the helideck and draws additional air into the combustion zone, escalating the fire size. CAP 437 Section 3.7 requires helideck fire detection — but does not specify adversarial robustness for AI systems classifying rendered thermal camera images used in helideck fire detection. Free tier — 10 scans/day, no card required.

4. Helicopter weight-on-deck structural load display AI (Strainstall helicopter weighing AI, Pacific Marine helideck load display AI, RDS helicopter weight-on-deck AI — rendered strain gauge display AI classifying total helicopter landing weight against helideck structural limit)

Each offshore helideck is structurally rated for a maximum permitted landing weight (MPLW) in tonnes — the maximum all-up weight (AUW) of a helicopter that may land or operate on the helideck, determined by the structural capacity of the helideck plating, beams, and support framework under the dynamic landing load (2.0× static weight per CAP 437 Appendix A for dynamic impact factor). A helicopter landing on a helideck at its AUW applies a dynamic load approximately twice the static weight during touchdown; a heavy or hard landing can apply a load factor of 3–4× static weight. AI systems process rendered images of the helideck structural load monitoring display — a strain gauge readout or load cell display panel mounted at the HLO station showing total deck load in tonnes and percentage of MPLW — to classify whether a landed helicopter is within the structural limit and whether a planned landing by a particular aircraft type (with specified AUW) is within the helideck MPLW. In an emergency evacuation scenario, an overloaded helicopter attempting to land may exceed the helideck structural limit and cause a local deck failure.

An adversarial perturbation targeting the helicopter weight-on-deck display AI applies a ±8 DN downward shift to the pixel region encoding the load readout in the rendered display image — shifting the apparent current deck load from 13.4 tonnes (above the helideck MPLW of 12.8 tonnes for the installation, a fixed helideck on a mature North Sea production platform with the original 1970s-era structural specification) to 10.2 tonnes (within the MPLW). The AI classifies the deck as structurally within limits. The flight dispatcher authorises a second helicopter (AgustaWestland AW139 at 5.2 tonnes AUW) to land on the helideck while the first helicopter (Sikorsky S-92 at 12.1 tonnes AUW) is still on deck with engines running. The combined static load is 17.3 tonnes; the dynamic landing load of the AW139 touchdown is approximately 10.4 tonnes instantaneous (2.0× AUW dynamic factor). The helideck structure was not designed for simultaneous two-helicopter operations at this load combination. ICAO Annex 14 Volume II specifies helideck structural design requirements — but does not address adversarial robustness for AI systems classifying rendered structural load display images used in weight-on-deck authorisation decisions.

Integration: offshore platform helideck operations AI with Glyphward pre-scan gate

The Glyphward scan gate for offshore platform helideck operations AI belongs at every rendered-image ingestion boundary in the helideck operations monitoring pipeline — before helideck status lamp camera AI processes rendered CCTV images, before wind direction and speed display AI processes rendered anemometer display images, before helideck thermal fire detection camera AI processes rendered infrared camera images, and before helicopter weight-on-deck structural load display AI processes rendered strain gauge display images. Threshold 30 for offshore platform helideck operations AI reflects the fatal multi-casualty consequence of helideck operational failures — North Sea helicopter accidents have caused ~200 fatalities since 1970, with individual incidents killing 13–16 people (Bond Offshore AS332 2009: 16 killed; Cougar Helicopters Sikorsky S-92 2009: 17 killed) — combined with the mitigating observation that multiple independent protective layers are present in the helideck operational system: the HLO has direct visual access to the helideck, the pilot observes the actual helideck conditions during approach, and radio communication confirms deck state. These independent layers distinguish helideck AI (threshold 30) from arc flash PPE AI (threshold 35), where the adversarially incorrect AI classification propagates directly to the worker’s PPE selection with no independent visual confirmation loop.

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"

# Offshore platform helideck operations AI contexts: threshold 30
# CAP 437 8th Edition (UK CAA Standards for Offshore Helicopter Landing Areas);
# ICAO Annex 14 Volume II (Heliports — helideck design and operations);
# Norwegian CAA BSL G 10-1 (Norwegian helideck operational requirements).
HELIDECK_THRESHOLD = 30


class HelideckContext(Enum):
    STATUS_LAMP        = "status_lamp"        # Helideck status lamp camera AI
    WIND_DISPLAY       = "wind_display"       # Wind direction and speed display AI
    THERMAL_FIRE       = "thermal_fire"       # Helideck thermal fire detection camera AI
    WEIGHT_ON_DECK     = "weight_on_deck"     # Helicopter weight-on-deck load display AI


class AdversarialHelideckImageError(Exception):
    """Raised when Glyphward detects adversarial content in an offshore platform
    helideck operations AI rendered image above threshold 30.

    Consequence if not raised:
    - STATUS_LAMP: red not-clear lamp classified as green → inbound helicopter
      lands on occupied deck → rotor collision → multi-fatality crash.
    - WIND_DISPLAY: 40-knot crosswind direction suppressed to 015° → approach
      authorised in crosswind above limits → dynamic rollover or wave strike.
    - THERMAL_FIRE: active fuel fire on deck suppressed to background →
      no suppression activation → inbound helicopter rotor wash escalates fire;
      Piper Alpha 1988: 167 killed (helideck inaccessibility context).
    - WEIGHT_ON_DECK: structural overload suppressed → two-helicopter deck load
      exceeds MPLW → deck structural failure.
    Fail-safe: immediately issue red status lamp (not-clear) to all inbound
    helicopters; read wind instruments directly from the anemometer display
    (not the AI-processed readout); verify deck fire status by direct visual
    observation and activate foam suppression; halt all helideck operations
    until the adversarial input source is identified and the monitoring system
    is independently verified.
    """

    def __init__(self, scan_id, score, context, platform_id, flagged_region=None):
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.platform_id = platform_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial helideck image: context={context.value} "
            f"score={score} platform={platform_id} scan_id={scan_id}"
        )


async def scan_helideck_image(image_bytes, context, platform_id, client):
    image_hash = hashlib.sha256(image_bytes).hexdigest()
    payload = {
        "image": base64.b64encode(image_bytes).decode(),
        "source": f"helideck:{context.value}:{platform_id}",
        "metadata": {
            "platform_id": platform_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"] >= HELIDECK_THRESHOLD:
        raise AdversarialHelideckImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            platform_id=platform_id,
            flagged_region=result.get("flagged_region"),
        )
    return result

Deploy scan_helideck_image before each helideck AI classification call. On AdversarialHelideckImageError for STATUS_LAMP: immediately issue a red/not-clear status to all inbound helicopters by radio (do not rely on the status lamp AI classification); visually confirm helideck occupancy and condition directly; notify the OIM and halt all approach clearances until the adversarial input source has been identified and the helideck status lamp monitoring system independently verified. See also: offshore anchor handling vessel mooring AI prompt injection (related offshore safety AI adversarial surfaces) and free scanner — 10 scans/day, no card required. Get early access

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