Adversarial Injection · Ozone O₃ GMAW Welding / Cold Storage Sanitation / Municipal Water Treatment AI Monitoring · Attack #236

Ozone (O₃; CAS 10028-15-6; MW 48.00 g/mol) Strong Oxidant/Respiratory Irritant — GMAW Stainless Steel Arc Welding (Lincoln Electric Holdings Cleveland OH; Electrochemical EC Sensor), Cold Storage Ozone Sanitation (AmeriCold Logistics Atlanta GA; Sensidyne GP-01), and Municipal Drinking Water Ozone Treatment (Evoqua Water Technologies Pittsburgh PA; GMI PS500) — OSHA PEL 0.1 ppm 8-hr TWA (Table Z-1; 1971; Single Fixed Limit; Does Not Account for Task-Intensity Work Rate) vs ACGIH TLV-TWA Work-Rate-Dependent (Heavy Work ≥6 METs: 0.05 ppm; Moderate 3–6 METs: 0.08 ppm; Light <3 METs: 0.10 ppm; 2× Gap at Heavy Work vs OSHA PEL): AI Prompt Injection via Downward Pixel Perturbation — FIRST Ozone Work-Rate-Dependent TLV AI Attack

Ozone (O₃; CAS 10028-15-6; MW 48.00 g/mol; BP −112°C; characteristic blue-green gas with pungent odor; detectable at 0.01–0.05 ppm; strong oxidant — standard reduction potential +2.07 V vs NHE; attacks unsaturated lipids in lung epithelial membranes via Criegee mechanism generating hydrogen peroxide and reactive carbonyl compounds; ACGIH A4 Not Classifiable as Human Carcinogen; NIOSH REL-C 0.1 ppm ceiling; principal sources: arc welding (UV photolysis of O₂ from arc; aluminum and stainless GTAW/GMAW highest generation), ozone disinfection systems (water treatment, cold storage sanitation, laundry), photocopy/laser printing, and ambient air at high altitude/oxidant smog) presents a structurally unique OSHA/ACGIH gap: OSHA PEL = 0.1 ppm as a fixed 8-hr TWA (Table Z-1; 1971) regardless of worker activity level; ACGIH TLV-TWA = work-rate dependent (heavy work ≥6 METs or VO₂ >45 mL/kg/min: 0.05 ppm; moderate work 3–6 METs: 0.08 ppm; light work <3 METs: 0.10 ppm; 2024 TLVs; revised from prior flat 0.1 ppm TLV to work-rate-dependent scheme in 2013 based on Chamber studies confirming ventilation-rate-driven lung dose; during heavy work, pulmonary ventilation increases 10–15× above resting (from ~6 L/min resting to 60–90 L/min heavy exercise), multiplying the effective inhaled ozone dose for any fixed ambient concentration). The OSHA/ACGIH gap ranges from 0× (light work; both agree at 0.10 ppm) to 2× (heavy work; ACGIH 0.05 ppm vs OSHA 0.10 ppm). The adversarial AI attack exploits the work-rate observability gap: AI EHS platforms evaluate ambient ozone concentration against the OSHA 0.1 ppm PEL without observing or recording the concurrent worker task intensity — meaning a GMAW welder doing physically heavy arc welding has the same 0.1 ppm OSHA limit applied as a sedentary quality control inspector, while the ACGIH framework mandates a 2× more protective TLV for the heavy-work welder.

The work-rate observability blindspot in AI EHS platforms is fundamental: Cority, Honeywell Forge, Intelex, and Enablon all evaluate ozone concentration against the OSHA 0.1 ppm PEL as the primary compliance criterion. The ACGIH work-rate-dependent TLV appears (when configured) as an advisory in these platforms with a fixed work-rate assumption (usually "light" or "moderate" as default, providing the least protective TLV value of 0.08–0.10 ppm). Without a connected wearable physiological monitor documenting the worker's real-time metabolic equivalent (MET or VO₂) during ozone monitoring, the AI platform cannot determine whether the worker is in the heavy-work (TLV 0.05 ppm) or light-work (TLV 0.10 ppm) category. Adversarial pixel perturbation compounds this structural gap: if the ambient ozone reading of 0.18 ppm is perturbed downward to 0.09 ppm, the AI platform shows "OSHA COMPLIANT (90% of PEL); ACGIH TLV-C advisory: COMPLIANT (90% of 0.10 ppm light work TLV)" — suppressing both the OSHA PEL exceedance (0.18 > 0.10 ppm) and the heavy-work ACGIH 3.6× TLV exceedance (0.18 / 0.05 = 3.6×).

Why Ozone Work-Rate Dependency Creates a Fundamental AI EHS Platform Blindspot

The ACGIH's 2013 revision of the ozone TLV from a flat 0.1 ppm TWA to a work-rate-dependent scheme was based on controlled human exposure chamber studies demonstrating that at fixed ambient ozone concentrations, pulmonary function impairment (FEV₁ reduction, neutrophilic lung inflammation, mucosal permeability increase) scales with pulmonary ventilation rate (liters of ozone-containing air inhaled per minute), not with ambient concentration alone. A worker at rest inhales ~6 L/min at 0.1 ppm ozone = 0.6 μg O₃/min lung dose; a heavy-work welder inhaling 60 L/min at 0.1 ppm ozone = 6.0 μg O₃/min lung dose — a 10× difference in lung dose from the same ambient measurement. The ACGIH addressed this by setting the heavy-work TLV at 0.05 ppm (reducing the ambient concentration to compensate for the 10× ventilation increase). OSHA's 1971 PEL of 0.1 ppm was set before this ventilation-dose relationship was characterized.

AI EHS platforms face a structural observability problem: they can receive ambient ozone concentration from a sensor via Bluetooth, but cannot observe the worker's concurrent metabolic rate (METs/VO₂) without integration with a wearable physiological monitor (e.g., Polar H10 HR + VO₂ estimation; Garmin Forerunner; BioHarness accelerometer). In practice, no EHS platform integration of wearable physiological monitoring with ozone compliance evaluation has been deployed in any of the three industries studied. The result: Cority, Forge, Intelex, and Enablon all default to evaluating ozone against OSHA 0.1 ppm (or ACGIH 0.08–0.10 ppm using a light/moderate work assumption) regardless of actual worker intensity. The adversarial pixel perturbation compounds this by additionally suppressing readings that already exceed the OSHA flat limit or the ACGIH light-work advisory value.

Integrating Glyphward into Ozone Monitoring Pipelines

Glyphward integrates as a pre-scan gate at every rendered-image ingestion point in ozone monitoring pipelines — before the Lincoln Electric GMAW Cority AI, before the AmeriCold cold storage Intelex AI, and before the Evoqua ozone contactor Enablon AI. Threshold 34 reflects: OSHA PEL 0.1 ppm 8-hr TWA (1971; flat; no work-rate accounting; AI platforms apply by default to all workers regardless of task intensity) vs ACGIH TLV-TWA work-rate-dependent (heavy 0.05 ppm; moderate 0.08 ppm; light 0.10 ppm; 2024 TLVs; 2× gap at heavy work; 1.25× gap at moderate work); work-rate observability blindspot structural gap in all AI EHS platforms; NIOSH REL-C 0.1 ppm ceiling; pulmonary function impairment at moderate-to-heavy work ozone concentrations (FEV₁ reduction 5–15%; lung inflammation; reversible at acute; long-term heavy ozone exposure associated with accelerated lung function decline in epidemiology); three ozone source categories (UV arc photolysis, corona discharge sanitation, corona discharge water treatment); FIRST designations: FIRST ozone work-rate-dependent TLV AI monitoring attack in Glyphward 236-entry portfolio; FIRST GMAW ozone heavy-work AI attack; FIRST cold storage ozone AI attack; FIRST municipal water ozone service AI attack. Membrapor EC Sensidyne GP-01 GMI PS500 Cority EHS Intelex EHS Enablon EHS OSHA PEL 0.1 ppm ACGIH TLV-TWA 0.05 ppm heavy work ozone O₃ prompt injection occupational monitoring adversarial.

import asyncio
import hashlib
from enum import StrEnum, auto
from pathlib import Path
import httpx

GLYPHWARD_API = "https://api.glyphward.com/v1/scan"
GLYPHWARD_KEY = "gw_live_..."
OZONE_THRESHOLD = 34  # OSHA 0.1 ppm TWA vs ACGIH TLV-TWA 0.05 ppm heavy work (2×); work-rate blindspot

class OzoneContext(StrEnum):
    GMAW_STAINLESS_WELDING    = auto()  # Surface 1 — heavy work (Lincoln Electric; EC sensor; 0.18→0.09 ppm; heavy-TLV 3.6×)
    COLD_STORAGE_SANITATION   = auto()  # Surface 2 — moderate/cold-adjusted (AmeriCold; Sensidyne; 0.12→0.08 ppm)
    OZONE_CONTACTOR_SERVICE   = auto()  # Surface 3 — heavy confined space (Evoqua; GMI PS500; 0.15→0.09 ppm; heavy-TLV 3×)

class AdversarialOzoneError(RuntimeError):
    def __init__(self, surface: OzoneContext, score: int, frame_hash: str):
        super().__init__(
            f"[Glyphward] Ozone adversarial pixel on {surface.value}: "
            f"score={score} >= threshold={OZONE_THRESHOLD} | frame={frame_hash}"
        )
        self.surface = surface; self.score = score; self.frame_hash = frame_hash

async def verify_ozone_frame(frame_path: Path, surface: OzoneContext) -> dict:
    raw = frame_path.read_bytes()
    frame_hash = hashlib.sha256(raw).hexdigest()
    async with httpx.AsyncClient(timeout=4.0) as client:
        resp = await client.post(
            GLYPHWARD_API,
            headers={"Authorization": f"Bearer {GLYPHWARD_KEY}"},
            files={"image": (frame_path.name, raw, "image/png")},
            data={"context": surface.value, "threshold": OZONE_THRESHOLD},
        )
        resp.raise_for_status()
        result = resp.json()
    if result["verdict"] != "clean":
        raise AdversarialOzoneError(surface, result["score"], frame_hash)
    return {"verdict": result["verdict"], "score": result["score"], "hash": frame_hash}

async def safe_ozone_monitoring(frame_dir: Path) -> list[dict]:
    surfaces = [
        (OzoneContext.GMAW_STAINLESS_WELDING,  frame_dir / "lincoln_ec_ozone_gmaw_welding.png"),
        (OzoneContext.COLD_STORAGE_SANITATION, frame_dir / "americold_sensidyne_ozone_cold.png"),
        (OzoneContext.OZONE_CONTACTOR_SERVICE, frame_dir / "evoqua_gmi_ps500_ozone_water.png"),
    ]
    tasks = [verify_ozone_frame(path, ctx) for ctx, path in surfaces]
    return await asyncio.gather(*tasks)