Prompt injection in chemical plant process safety AI

Four adversarial injection surfaces in chemical plant process safety AI

1. Flare stack and fired heater infrared thermography AI (OPGAL ThermoVision, FLIR A-Series industrial, Worley drone thermography)

Flare stacks, fired heaters (furnaces), and boilers in petroleum refinery and chemical plant operations are subject to refractory degradation, tube hot-spot formation, and combustion zone failures that are detected by periodic infrared thermography inspection. OPGAL ThermoVision AI (deployed at Israeli and European refinery facilities), FLIR A-Series industrial cameras with AI analysis modules (deployed at petrochemical sites globally), and Worley’s drone thermography inspection service process rendered infrared thermography images — false-colour thermal maps where colour encodes surface temperature (blue = ambient; red = elevated; white = high-temperature anomaly) — to detect: fired heater tube hot spots (elevated surface temperature indicating tube overheating from coking, scale buildup, or loss of process flow — precursor to tube rupture and unconfined vapour cloud explosion in the furnace firebox); flare stack refractory brick failures (cold spots in the refractory lining indicating missing or collapsed refractory, which allows the steel shell to overheat and fail); heat exchanger bundle fouling anomalies (temperature distribution patterns in the exchanger shell indicating blocked tube bundle sections and overpressure risk); and steam boiler tube overheat patterns (elevated temperature in superheater tube sections, precursor to hydrogen damage or creep rupture failure).

An adversarial perturbation on a rendered infrared thermography image that suppresses a fired heater tube hot spot — cooling the false-colour signature of a 750°C tube surface anomaly (represented as a white-to-red cluster above the 680°C tube design maximum operating temperature) to the ambient furnace shell temperature (represented as the blue-green baseline) by modifying the thermal intensity pixel values in the hot spot cluster within the JPEG quantisation noise floor — causes the thermography AI to classify the furnace as “No thermal anomalies detected, equipment in normal operating condition” rather than “Hot spot on Run 3 tube bank, investigate immediately, consider steam injection for tube cooling.” A fired heater tube rupture in a crude distillation unit processing atmospheric residuum (boiling range 350–560°C, hydrogen content low, auto-ignition temperature 230–260°C) produces an immediate large vapour cloud of hydrocarbon vapour in the furnace firebox — igniting on the furnace burner flame to produce an unconfined vapour cloud explosion (UVCE) with a TNT-equivalent overpressure radius that exceeds the 1 psi overpressure contour (window breakage and human eardrum rupture threshold) at 150–300m from the firebox. The 2005 Texas City BP refinery explosion — 15 fatalities, initiating event was hydrocarbon liquid overflow from the raffinate splitter with ground-level pool ignition — occurred in this consequence envelope: a vapour cloud forming at grade in the process unit, igniting on an adjacent heat source.

2. Gas cloud detection camera AI (FLIR GF-Series OGI, Opgal EyeCGas, Sierra-Olympic Viento G)

Optical gas imaging (OGI) cameras — infrared cameras operating in the 3.2–3.4 μm or 8–12 μm thermal infrared band where hydrocarbon gases absorb IR radiation — detect fugitive emissions and gas cloud releases from process equipment by visualising the absorption contrast between the gas cloud and the background scene. FLIR GF320 and GF620 (Optical Gas Imaging cameras operating in the 3.2–3.4 μm HC absorption band), Opgal EyeCGas (fixed-mount autonomous OGI system), and Sierra-Olympic Viento G (640×512 pixel InGaAs sensor, 1.55–1.65 μm band for methane) generate rendered IR absorption video frames where hydrocarbon gas clouds appear as dark plumes against the background scene — the darker the cloud pixel intensity relative to the background, the higher the hydrocarbon column density. AI classification of OGI camera frames determines: whether a dark plume is a real hydrocarbon gas cloud (vs. steam plume, water vapour, or optical artefact — common false-alarm sources); the estimated gas cloud concentration (correlated with cloud pixel intensity and background contrast using Beer-Lambert absorption law); and whether the concentration exceeds the Lower Explosive Limit (LEL — for propane: 2.1% by volume; for ethylene: 2.7% by volume; for hydrogen: 4.0% by volume) at the cloud centroid, triggering an automated Emergency Isolation Valve (EIV) closure or plant emergency alarm. Opgal EyeCGas AI and FLIR’s AI analysis modules process OGI camera frames at 30 fps and generate real-time gas cloud concentration estimates that feed DCS (Distributed Control System) alarm management in OSHA PSM-regulated facilities.

An adversarial perturbation on a rendered OGI camera frame that suppresses a gas cloud plume — filling in the dark absorption contrast pixels of the hydrocarbon cloud with the background scene IR reflectance texture, effectively removing the visible cloud signature from the image while preserving surrounding scene geometry — causes the gas cloud detection AI to classify the frame as “No gas cloud detected, background scene normal” rather than “Gas cloud detected, estimated concentration above LEL — initiate Emergency Isolation Valve closure.” A gas release above LEL in the exclusion zone of an alkylation unit (HF or H2SO4 alkylation processes in US refineries; total US alkylation capacity approximately 1.2 million barrels/day; HF alkylation at 50+ US facilities including major locations in Texas, California, and the Midwest) that does not trigger the automated EIV closure — because the OGI AI suppressed the gas cloud detection — allows the HF vapour cloud to grow beyond the plant battery limits. HF alkylate vapour cloud at LEL (1.0% by volume) within a 300m radius of the alkylation unit would, if ignited, produce a deflagration-to-detonation transition (DDT) in a congested process unit with overpressure consequences throughout the refinery footprint. The 1994 Texas City Amoco refinery fire and the 2012 Chevron Richmond refinery fire (15,000 persons sought medical attention from smoke inhalation) are both in the consequence envelope for OGI AI detection failures in major US refinery process units.

3. P&ID recognition and as-built documentation AI (Aspentech Inmation, SmartPlant Instrumentation AI, AVEVA P&ID AI)

P&ID (Piping and Instrumentation Diagram) documents are the primary engineering reference for process safety analysis — they define every pipe, valve, instrument loop, safety interlock, relief device, and instrumented protective function (IPF) in each process unit. OSHA PSM 29 CFR 1910.119(d) requires covered facilities to maintain accurate P&IDs as part of the Process Safety Information (PSI) package, and 1910.119(e) requires that HAZOP studies be conducted using PSI that accurately reflects the actual process design. AI systems for P&ID digitisation and as-built comparison — Aspentech Inmation AI, AVEVA P&ID AI (formerly Triconex), SmartPlant Instrumentation AI (Intergraph/Hexagon PPM), and Cognite Data Fusion P&ID AI — process scanned P&ID document images (TIFF rasters at 300–600 DPI from the plant engineering archive) and as-built field photographs to: identify symbol types (pump, compressor, control valve, safety relief valve, pressure transmitter) using convolutional symbol recognition; extract tag numbers (the instrument and equipment ID strings printed adjacent to each symbol) using OCR; compare as-built field photographs against the P&ID digital twin to detect deviations (a manual bypass valve installed in the field but not shown on the P&ID — a common HAZOP finding that represents an undocumented defeat mechanism for a safety interlock); and update the P&ID database with extracted topology (which instruments are connected to which process lines, which safety loops have which setpoints).

An adversarial perturbation on a rendered P&ID document scan image that removes the symbol of a Safety Relief Valve (SRV) — blanking the SRV symbol and its associated tag number from the image before the P&ID AI extracts the process topology — causes the HAZOP AI to generate a process topology model that does not include the SRV as a safeguard for the pressure vessel it protects. In a HAZOP node analysis of a reactor with a credible blocked outlet overpressure scenario (Guide Word: “More Pressure”), the AI-generated safeguard list that omits the SRV produces a Risk Assessment finding that the node has no pressure protection — triggering an Action Item to install an SRV (which already exists, undetected by the AI because it was suppressed from the P&ID scan). The consequence is not immediate but structural: the HAZOP Action Item log drives a capital project to install a “missing” SRV, consuming engineering budget; the existing SRV continues to operate without formal HAZOP validation of its sizing; and if the existing SRV is undersized for a runaway reaction overpressure scenario (a common finding in legacy OSHA PSM audits — API 520/521 basis not updated when process throughput increased), this structural omission prevents the HAZOP from identifying the SRV sizing deficiency and generating a corrective Action Item. The 2010 Tesoro Anacortes Refinery explosion (7 fatalities; OSHA PSM violation for failure to update HAZOP analysis when heat exchanger operating conditions changed) illustrates the consequence of PSM documentation failures that prevent HAZOP processes from identifying evolving process risks.

4. Drone and fixed-camera mechanical integrity visual inspection AI (Cognite, WorleyParsons InDAS, Baker Hughes Drishti)

Mechanical integrity inspection of process equipment in OSHA PSM facilities — required under 1910.119(j) — covers pressure vessels, heat exchangers, piping systems, relief devices, emergency shutdown systems, and controls. AI-assisted visual inspection uses drone camera platforms (DJI Matrice 300 RTK with Zenmuse H20T thermal+RGB, Flyability Elios 3 for confined spaces) and fixed-mount industrial cameras to process photographic and video inspection imagery through defect detection AI that identifies: corrosion pitting on vessel shells and nozzles (surface texture anomaly against intact paint/substrate); weld crack indications (linear surface discontinuities at heat-affected zones adjacent to weld beads); insulation system damage exposing bare metal to CUI (Corrosion Under Insulation) environments; and valve packing leak signatures (efflorescence or staining at packing glands indicating external leakage of toxic or flammable process fluid). Cognite Data Fusion AI (deployed at Equinor’s Kværner facility and Repsol refineries), Baker Hughes Drishti AI (visual inspection platform for downstream assets), and Worley’s InDAS (Intelligent Digital Asset System) inspection AI process inspection camera images from periodic survey campaigns and continuous fixed-camera monitoring into defect call reports that drive OSHA PSM 1910.119(j)(2) inspection interval scheduling.

An adversarial perturbation on a rendered pressure vessel inspection photograph that suppresses a corrosion pit cluster on the vessel shell near a nozzle-to-shell weld — smoothing the rough pitted surface texture against the surrounding intact paint using a ±10 DN pixel shift within JPEG artefact noise — causes the mechanical integrity AI to classify the vessel shell as “Condition 1 — no active corrosion, next inspection at full regulatory interval (typically 5 years per API 510 interval criteria for vessels below 50% calculated corrosion rate).” For a vessel in chloride-ion service (cooling water heat exchanger with seawater or brackish cooling water on the shell side — a configuration used in refinery overhead condensers, column inter-coolers, and after-coolers throughout the Gulf Coast refinery complex), active pitting corrosion at the nozzle-to-shell weld progresses to corrosion-caused nozzle failure (the nozzle-to-shell weld area is the highest-stress location on a nozzle under internal pressure and piping load — ASME Section VIII Division 1 UG-27 governing thickness calculation applies here) within 2–5 years of active corrosion initiation at rates of 2–10 mm/year. A nozzle failure on a vessel containing a toxic substance (e.g., anhydrous ammonia at 14 bar, OSHA PSM threshold quantity 10,000 lbs) releases a two-phase flash vaporisation plume that, at 10,000 lb (4,536 kg) inventory, produces a toxic endpoint radius (IDLH = 25 ppm for ammonia) of 2–5 km under neutral atmospheric stability (ALOHA dispersion at F-stability conditions). The adversarial suppression of the corrosion pit detection at the inspection stage that would have triggered an API 510 inspection interval reduction (to 2.5 years vs. 5 years) and a potential repair decision removes the one intervention point in the mechanical integrity programme that prevents this outcome.

Integration: chemical plant process safety AI scanning with Glyphward pre-scan gate

The Glyphward scan gate for chemical plant process safety AI belongs at the rendered image ingestion boundary before each AI classification step — before flare stack thermography AI processes IR images, before OGI gas cloud AI processes camera frames, before P&ID recognition AI processes document scans, and before mechanical integrity AI processes inspection photographs. Threshold 40 for chemical plant process safety AI contexts reflects the toxic and explosive consequence severity under OSHA PSM and EPA RMP worst-case scenarios.

import asyncio, base64, hashlib, json
from datetime import datetime, timezone
from enum import Enum
from pathlib import Path

import httpx

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

# Chemical plant process safety AI contexts: threshold 40
# OSHA PSM 29 CFR 1910.119, EPA RMP 40 CFR Part 68, ATEX 2014/34/EU.
CHEM_SAFETY_AI_THRESHOLD = 40


class ChemicalPlantAIContext(Enum):
    FLARE_THERMOGRAPHY  = "flare_thermography"  # IR thermography AI (flares, heaters)
    GAS_CLOUD_DETECTION = "gas_cloud_detection" # OGI camera gas cloud detection AI
    PID_RECOGNITION     = "pid_recognition"     # P&ID document scan recognition AI
    MECH_INTEGRITY      = "mech_integrity"      # Mechanical integrity visual inspection AI


class AdversarialChemicalPlantImageError(Exception):
    """Raised when Glyphward detects adversarial pixel content in a
    chemical plant process safety AI rendered image above threshold 40.

    Consequence if not raised: fired heater tube hot spot / gas cloud /
    SRV omission / vessel corrosion suppressed from safety assessment →
    process hazard undetected → potential UVCE, toxic release, or vessel
    failure with off-site EPA RMP worst-case consequence.
    Fail-safe: suppress AI classification, notify Process Safety Engineer
    (PSM coordinator) per OSHA 1910.119(b) — do not issue MI inspection
    clearance or HAZOP safeguard approval without human engineer review.
    """

    def __init__(self, scan_id: str, score: int,
                 context: ChemicalPlantAIContext,
                 facility_id: str, asset_id: str | None,
                 flagged_region: dict | None = None) -> None:
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.facility_id = facility_id
        self.asset_id = asset_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial chemical plant image: "
            f"context={context.value} score={score} "
            f"facility={facility_id} asset={asset_id} scan_id={scan_id}"
        )


async def scan_chemical_plant_image(
    image_bytes: bytes,
    context: ChemicalPlantAIContext,
    facility_id: str,
    asset_id: str | None,
    is_osha_psm: bool,
    is_epa_rmp: bool,
    client: httpx.AsyncClient,
) -> dict:
    """Scan a chemical plant process safety AI image for adversarial content.

    Fail-safe contract: AdversarialChemicalPlantImageError or httpx error →
    suppress AI safety assessment output, notify PSM coordinator per OSHA
    1910.119 emergency response procedures. Do not clear fired heater hot
    spots, gas cloud alarms, or mechanical integrity inspection findings
    based on adversarially flagged images without human engineer review.

    Args:
        image_bytes: IR thermography image, OGI camera frame, P&ID scan,
            or mechanical integrity inspection photograph bytes.
        context: ChemicalPlantAIContext identifying the safety system.
        facility_id: EPA RMP Facility ID or OSHA establishment ID.
        asset_id: Equipment tag number (e.g., 'F-101' for furnace 101).
        is_osha_psm: True if facility is OSHA PSM 29 CFR 1910.119 covered.
        is_epa_rmp: True if facility has EPA RMP 40 CFR Part 68 programme.
        client: Shared httpx.AsyncClient for connection reuse.

    Returns:
        Glyphward scan result dict.

    Raises:
        AdversarialChemicalPlantImageError: if score exceeds threshold 40.
        httpx.HTTPStatusError: on Glyphward API error (fail-closed).
    """
    image_hash = hashlib.sha256(image_bytes).hexdigest()
    payload = {
        "image": base64.b64encode(image_bytes).decode(),
        "source": f"chem:{context.value}:{facility_id}:{asset_id}",
        "metadata": {
            "facility_id": facility_id,
            "asset_id": asset_id,
            "is_osha_psm": is_osha_psm,
            "is_epa_rmp": is_epa_rmp,
            "image_sha256": image_hash,
            "context": context.value,
        },
    }
    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()

    await _write_chem_scan_audit(
        image_hash=image_hash,
        scan_id=result["scan_id"],
        score=result["score"],
        context=context,
        facility_id=facility_id,
        asset_id=asset_id,
        is_osha_psm=is_osha_psm,
        flagged=result["score"] > CHEM_SAFETY_AI_THRESHOLD,
    )

    if result["score"] > CHEM_SAFETY_AI_THRESHOLD:
        raise AdversarialChemicalPlantImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            facility_id=facility_id,
            asset_id=asset_id,
            flagged_region=result.get("flagged_region"),
        )
    return result


async def _write_chem_scan_audit(
    *, image_hash: str, scan_id: str, score: int,
    context: ChemicalPlantAIContext, facility_id: str,
    asset_id: str | None, is_osha_psm: bool, flagged: bool,
) -> None:
    record = {
        "ts": datetime.now(timezone.utc).isoformat(),
        "scan_id": scan_id,
        "image_sha256": image_hash,
        "context": context.value,
        "score": score,
        "threshold": CHEM_SAFETY_AI_THRESHOLD,
        "flagged": flagged,
        "facility_id": facility_id,
        "asset_id": asset_id,
        "is_osha_psm": is_osha_psm,
        "regulatory_refs": [
            "OSHA PSM 29 CFR 1910.119 (Process Safety Management of Highly Hazardous Chemicals)",
            "EPA RMP 40 CFR Part 68 (Risk Management Programs for Chemical Accidental Release Prevention)",
            "ATEX Directive 2014/34/EU (Equipment for Use in Explosive Atmospheres)",
            "API 510 (Pressure Vessel Inspection Code, 11th ed. 2022)",
            "API 570 (Piping Inspection Code, 4th ed. 2016)",
            "API 653 (Tank Inspection, Repair, Alteration, and Reconstruction, 5th ed.)",
            "API 520 / 521 (Sizing, Selection, and Installation of Pressure-relieving Devices)",
            "ASME PCC-2 (Repair of Pressure Equipment and Piping)",
        ],
    }
    audit_path = Path("/var/log/glyphward/chem_safety_ai_scan_audit.jsonl")
    audit_path.parent.mkdir(parents=True, exist_ok=True)
    with audit_path.open("a") as fh:
        fh.write(json.dumps(record) + "\n")

Deploy scan_chemical_plant_image at each process safety AI rendered-image ingestion boundary: before flare stack thermography AI (threshold 40), before OGI gas cloud detection AI (threshold 40), before P&ID recognition AI (threshold 40), and before mechanical integrity inspection AI (threshold 40). On AdversarialChemicalPlantImageError: suppress AI safety assessment output, notify PSM coordinator per OSHA 1910.119 incident investigation procedures. For GAS_CLOUD_DETECTION context: do not suppress an active gas cloud alarm — initiate Emergency Isolation Valve closure independently of AI classification output when adversarial injection is suspected. Get early access

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