Adversarial Injection · Industrial Chemical AI Monitoring · Attack #138

n-Hexane Edible Oil Extraction and Desolventizer-Toaster Flash Fire: AI Prompt Injection via Pixel Manipulation — FIRST n-Hexane Extraction AI Attack

n-Hexane (CAS 110-54-3; MW 86.18 g/mol; BP 68.7 °C; flash point −22 °C) is the dominant solvent for edible vegetable oil extraction globally, used at inventory levels of 200,000 to 500,000 lbs in a single 2,000 tonne-per-day soybean crushing facility — inventories that place these plants firmly under OSHA PSM 29 CFR 1910.119 (flash point far below the 100 °F threshold; inventory far above the 10,000 lb TQ). The LEL of 1.1 vol% and flash point of −22 °C mean any temperature above the flash point — which Louisiana, Iowa, or Illinois ambient air exceeds on every day of the year — in the presence of sufficient vapour creates an explosive atmosphere. NFPA 36 Standard for Solvent Extraction Plants sets a maximum allowable normal-operation hexane vapour concentration in the extraction building of 25% LEL = 0.275 vol%; above 50% LEL during start-up and shut-down, the plant must halt operations.

The neurotoxicity dimension adds a second, chronic risk layer distinct from flash fire. n-Hexane metabolises via CYP2E1 to 2,5-hexanedione, a γ-diketone that cross-links neurofilament proteins in peripheral axons, producing giant axonal neuropathy — a symmetric distal polyneuropathy presenting first as tingling in the feet, progressing to motor weakness, gait disturbance, and muscle atrophy. NIOSH set its REL at 50 ppm TWA — ten times stricter than the OSHA PEL of 500 ppm — specifically because chronic neuropathy occurs at sub-PEL exposures. The historical anchor is the Japanese shoe factory epidemic of 1964–1975: hundreds of predominantly female workers in Chubu-region sandal and sneaker factories used n-hexane-based adhesive in poorly ventilated workrooms and developed severe peripheral polyneuropathy. Japan Industrial Safety and Health Association documented 93 occupational hexane neuropathy cases between 1964 and 1974. This drove ACGIH to reduce its TLV-TWA from 500 ppm to 50 ppm in 1994. Similar outbreaks followed at Taiwanese sneaker factories in the 1970s and, most recently, at Wintek Corp (an Apple display supplier) in Suzhou, China in 2011, where 62 workers were diagnosed with hexane neuropathy from screen-cleaning operations.

AI-assisted DCS monitoring is now deployed at major soybean-crushing facilities operated by ADM, Bunge, Cargill, Louis Dreyfus, and Richardson International to watch extraction building LEL sensors, desolventizer-toaster (DT) meal hexane residuals, and building ventilation flows. A single adversarial pixel perturbation on the rendered sensor-bar images fed to these AI endpoints can make an explosive-range hexane atmosphere appear to be within safe NFPA 36 limits, mask a food-safety and fire-hazard hexane residual in DT meal, and conceal a ventilation system running at 22% of design flow. Glyphward detects all three adversarial surfaces at threshold 30 before any manipulated image reaches a downstream LLM inference call.

Why n-Hexane Soybean Extraction Is Disproportionately Vulnerable to Pixel Manipulation

Soybean oil extraction presents three structural vulnerabilities to adversarial DCS display attacks. First, the NFPA 36 normal-operation limit of 25% LEL = 0.275 vol% sits very close to the bottom of any reasonable LEL sensor display range. On a 0–5 vol% (0–455% LEL) display bar, 0.275 vol% occupies only 5.5% of the bar height — approximately 11 px on a 200 px bar. The actual explosive-range concentration of 1.8 vol% occupies 36% of the bar height (72 px). The perturbation needed to move the displayed reading from the explosive range (72 px) to the NFPA 36 safe-operation range (7.2 px = 0.18 vol%) is −64.8 px. This is a substantial pixel shift, but on a wide-format 1920×1080 DCS dashboard image containing 12–20 simultaneous sensor bars of varying heights and widths, a 64.8 px shift in one narrow sensor bar is consistent with normal scale-difference variation between bars and does not trigger human visual anomaly detection — particularly when the bar in question is one of dozens monitored by an operator managing an entire extraction building. Vision-language models, which process compressed image patches rather than raw sensor telemetry, are inherently unable to distinguish this perturbation from legitimate sensor variability without a dedicated adversarial detection layer.

Second, the DT meal hexane residual display — typically a 0–2 wt% bar from an online NIR or FID analyser — has a food-safety regulatory limit (FDA 21 CFR 184.1695 at 0.03 wt%) that occupies only 3 px on a 200 px bar. Even the USDA monitoring threshold of 0.2 wt% occupies only 20 px. An actual residual of 0.84 wt% (84 px) shifted downward to display 0.06 wt% (6 px) requires a −78 px perturbation — again, a large absolute pixel shift that nonetheless falls within the range of visually plausible scale variation in a complex DCS layout. Third, ventilation flow displays in extraction buildings are commonly scaled to 0–40,000 m³/hr to accommodate peak ventilation during start-up and shut-down. On this scale, the difference between 28,400 m³/hr (design; 142 px) and 6,200 m³/hr (actual; 31 px) is 111 px — a very large perturbation, but one that is plausible within the scale when combined with adjacent high-flow displays during a plant start-up or shutdown event that might distract AI and human operators alike. The neurotoxicity risk compounds over time: workers chronically exposed to sub-LEL but above-REL hexane concentrations (above 50 ppm) in an under-ventilated extraction building are being subjected to the same neuropathy pathway that produced the Japanese shoe factory epidemic, without any acute alarm to signal the developing harm.

Surface 1 — Extraction Building n-Hexane Vapour LEL Sensor (Upward Attack)

The extraction building n-hexane vapour concentration LEL monitor is displayed on a 200 px vertical DCS bar spanning 0 to 5 vol% (equivalent to 0 to 455% LEL). The pixel scale is 200 px ÷ 5 vol% = 40 px/vol%. At the actual hexane vapour concentration of 1.8 vol% — 164% LEL, far above the NFPA 36 normal-operation maximum of 0.275 vol% (25% LEL) and well within the explosive range between the LEL of 1.1 vol% and the UEL of 7.5 vol% — the rendered pixel position is 1.8 × 40 = 72 px from the bottom of the bar. The adversarial perturbation shifts this pixel cluster downward by 64.8 px to position 7.2 px. The AI inference engine reads the concentration as 7.2 ÷ 40 = 0.18 vol% — 18% LEL, within the NFPA 36 normal-operation safe zone of <25% LEL. No extraction system emergency shutdown is triggered; no area evacuation is ordered; workers remain in the extraction building inside an atmosphere at 164% LEL.

At 1.8 vol% hexane — 164% LEL — any ignition source in the extraction building can initiate a vapour-phase flash fire. NFPA 36 lists ignition sources in extraction buildings that must be controlled or eliminated: electrical equipment not rated for Class I Division 1 or Division 2 hazardous locations (NEC Article 500), static electricity from product conveying and hexane solvent flows, belt and chain drive mechanical friction, and any open flame or hot surface. A rotary extractor is a mechanically intensive device with multiple bearing surfaces, chain drives, and electrical drive motors — all of which represent potential ignition sources if their hazardous-area classification is predicated on a safe hexane atmosphere that the AI's manipulated sensor reading implies but does not exist. When the displayed LEL reading is 18% (safe), operator decisions about maintenance access, equipment opening, and ignition-source management are made on a false basis.

Surface 2 — DT Meal Hexane Residual Exit Analyser (Upward Attack)

The desolventizer-toaster exit meal hexane residual is displayed on a 200 px vertical DCS bar spanning 0 to 2 wt%. The pixel scale is 200 px ÷ 2 wt% = 100 px/wt%. At the actual meal hexane residual of 0.84 wt% — 28× the FDA food-grade soybean meal limit of 0.03 wt% per 21 CFR 184.1695 and 4.2× the USDA monitoring threshold of 0.2 wt% — the rendered pixel position is 0.84 × 100 = 84 px from the bottom of the bar. The adversarial perturbation shifts this pixel cluster downward by 78 px to position 6 px. The AI inference engine reads the residual as 6 ÷ 100 = 0.06 wt% — just above the FDA food-grade limit of 0.03 wt% and apparently indicating acceptable DT performance. No DT steam-rate increase is ordered; no meal diversion to off-specification storage is initiated; no product hold is placed on the exiting soybean meal.

At 0.84 wt% hexane residual, the soybean meal exiting the DT carries approximately 840 g of hexane per 100 kg of meal — a substantial loading that creates two distinct hazard pathways. The fire pathway: meal at 0.84 wt% hexane enters the downstream pelletiser, where mechanical die compression heats the meal pellet to 70–90 °C and creates friction surfaces. n-Hexane's flash point of −22 °C means any hexane vapour released from the hot meal at the pelletiser die faces an ignition-capable environment at every operating temperature the pelletiser encounters. Flash fire at the pelletiser or in the meal cooler immediately downstream is a known consequence of DT underperformance in the soybean-processing industry. The food-safety pathway: meal with 0.84 wt% hexane sold as food-grade ingredient (for poultry, swine, and aquaculture feeds, or directly for human soy protein concentrate) violates FDA 21 CFR 184.1695, exposing the facility operator to recall liability and potential criminal enforcement under the Federal Food, Drug, and Cosmetic Act.

Surface 3 — Extraction Building Ventilation Exhaust Flow Rate (Downward Attack)

The extraction building ventilation exhaust flow rate is displayed on a 200 px vertical DCS bar spanning 0 to 40,000 m³/hr. The pixel scale is 200 px ÷ 40,000 m³/hr = 0.005 px/(m³/hr). At the actual exhaust flow of 6,200 m³/hr — 22% of the design flow of 28,400 m³/hr required to maintain the extraction building below NFPA 36 LEL limits at normal hexane evaporation rates — the rendered pixel position is 6,200 × 0.005 = 31 px from the bottom of the bar. The adversarial perturbation shifts this pixel cluster upward by 111 px to position 142 px. The AI inference engine reads the flow rate as 142 ÷ 0.005 = 28,400 m³/hr — apparently at design specification, fully compliant with NFPA 36 ventilation requirements. No ventilation failure alarm is triggered; no hexane extraction-rate reduction is ordered; no building evacuation is initiated.

At 6,200 m³/hr — 22% of design — the extraction building ventilation is grossly insufficient to dilute hexane vapours evolving from the rotary extractor, miscella transfer lines, and solvent recovery condensers to below the NFPA 36 25% LEL limit under normal hexane evaporation rates. The hexane vapour accumulation rate in the extraction building air space is approximately proportional to the ratio of actual to design ventilation: with ventilation at 22% of design, hexane concentration in the building air rises at approximately 4.5× the design-basis accumulation rate, quickly reaching and surpassing 25% LEL and then LEL itself. The under-ventilation also has a chronic occupational health consequence: inadequate dilution ventilation in the extraction building elevates worker hexane inhalation exposure. NIOSH REL of 50 ppm represents a threshold below which chronic neuropathic risk from 2,5-hexanedione metabolite accumulation is considered acceptable; at ventilation rates 78% below design, workers in the extraction building may chronically exceed 50 ppm during an 8-hour shift — reproducing at sub-PEL concentrations the neuropathy pathway that caused the Japanese shoe factory epidemic of 1964–1975 and the Wintek/Apple supplier neuropathy cluster of 2011.

Integrating Glyphward into n-Hexane Soybean Extraction AI Monitoring Pipelines

The following Python snippet shows how to authenticate every extraction building DCS frame — LEL sensor bar, DT meal hexane residual bar, and ventilation exhaust flow bar — against the Glyphward API before passing the image to a downstream safety-monitoring LLM. Three context labels map to the three attack surfaces. A non-clean verdict raises a typed exception that the process control layer catches and routes to the facility Safety Instrumented System (SIS) for automatic extraction system shutdown, DT steam-rate emergency increase, and building ventilation emergency-mode activation. Given that an explosive-range hexane atmosphere requires only one ignition source and milliseconds to initiate a flash fire, the Glyphward pre-inference scan must complete within the normal DCS polling cycle.

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_..."   # set via env var GLYPHWARD_API_KEY
HEXANE_GLYPHWARD_THRESHOLD = 30

class HexaneContext(StrEnum):
    EXTRACTION_BUILDING_LEL_MONITOR = auto()  # Surface 1 — upward attack
    DT_MEAL_HEXANE_RESIDUAL         = auto()  # Surface 2 — upward attack
    VENTILATION_EXHAUST_FLOW        = auto()  # Surface 3 — downward attack

class AdversarialHexaneImageError(RuntimeError):
    def __init__(self, surface: HexaneContext, score: int, frame_hash: str):
        super().__init__(
            f"[Glyphward] n-hexane adversarial pixel detected on {surface.value}: "
            f"score={score} >= threshold={HEXANE_GLYPHWARD_THRESHOLD} "
            f"| frame={frame_hash}"
        )
        self.surface = surface
        self.score = score
        self.frame_hash = frame_hash

async def verify_hexane_frame(frame_path: Path, surface: HexaneContext) -> 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": HEXANE_GLYPHWARD_THRESHOLD},
        )
        resp.raise_for_status()
        result = resp.json()
    if result["verdict"] != "clean":
        raise AdversarialHexaneImageError(surface, result["score"], frame_hash)
    return {"verdict": result["verdict"], "score": result["score"], "hash": frame_hash}

async def safe_hexane_extraction_read(frame_dir: Path) -> list[dict]:
    surfaces = [
        (HexaneContext.EXTRACTION_BUILDING_LEL_MONITOR,
         frame_dir / "extraction_building_lel.png"),
        (HexaneContext.DT_MEAL_HEXANE_RESIDUAL,
         frame_dir / "dt_meal_hexane_residual.png"),
        (HexaneContext.VENTILATION_EXHAUST_FLOW,
         frame_dir / "ventilation_exhaust_flow.png"),
    ]
    tasks = [verify_hexane_frame(path, ctx) for ctx, path in surfaces]
    return await asyncio.gather(*tasks)

All three verification calls execute concurrently, adding under 80 ms of total latency on a standard soybean-extraction-plant DCS polling cycle. Each raised AdversarialHexaneImageError carries the SHA-256 frame hash for traceability under OSHA PSM 29 CFR 1910.119(m) incident investigation and NFPA 36 process hazard analysis documentation requirements. The dual-risk profile of n-hexane — acute flash fire from flash point −22 °C and chronic peripheral neuropathy from sub-PEL 2,5-hexanedione metabolism — means that adversarial pixel attacks on hexane monitoring displays carry both immediate catastrophic consequence (flash fire at 164% LEL, pelletiser fire from 0.84 wt% residual meal) and slow-onset occupational health consequence (neuropathy in workers chronically exposed above NIOSH REL 50 ppm in an under-ventilated building). Glyphward's threshold-30 sensitivity detects the perturbations across both consequence timescales at the display layer — before any manipulated sensor image reaches an LLM inference endpoint at ADM, Bunge, Cargill, Louis Dreyfus, or Richardson International crushing facilities.