Prompt injection in satellite and remote sensing AI

Four adversarial injection surfaces in satellite and remote sensing AI

1. Satellite crop stress image injection (USDA NASS 7 USC §2204, FSA 7 USC §1961 farm loan eligibility)

Satellite crop stress classification AI processes multispectral satellite image tiles displaying false-colour composite renderings of crop canopy spectral reflectance conditions, shortwave infrared (SWIR) band composite display images of soil moisture and plant water stress indicators, red-edge band ratio display images of chlorophyll concentration and nitrogen sufficiency, visible-near-infrared (VNIR) crop canopy health index visualisation images, Leaf Area Index (LAI) spatial distribution display images, and crop type classification probability map display images from Planet Labs AI at 30,000 or more customers including USDA Farm Service Agency, federal crop insurance companies, and agricultural lenders processing multispectral satellite imagery across 140 million square kilometres of daily Earth surface imaging coverage through AI-assisted crop stress classification, production impairment detection, and FSA emergency loan eligibility documentation tools; Satellogic AI at government and agricultural client operations processing 1-metre multispectral satellite imagery covering 60 million or more hectares of enrolled farmland through AI-assisted crop monitoring, disease and pest stress detection, and crop condition survey analysis tools; and Indigo Ag AI at carbon programme and crop insurance client operations monitoring over 1 million enrolled acres through satellite NDVI-based crop health and production stress monitoring systems integrating multispectral satellite imagery from Planet Labs and Sentinel-2 sources — extracting crop stress severity classifications, production impairment determinations, and crop condition category assessments from multispectral satellite image tile inputs in AI-assisted USDA NASS crop condition survey contribution, USDA FSA emergency loan collateral valuation, Noninsured Crop Disaster Assistance Program (NAP) payment trigger verification, and federal crop insurance loss assessment pipelines at agricultural monitoring volumes that make individual human geospatial analyst inspection of every satellite image tile impracticable.

The adversarial injection surface is the multispectral satellite image tile submission pathway: Planet Labs AI or Satellogic AI multispectral image tile display images submitted through AI-assisted crop stress classification and FSA loan eligibility documentation tools for AI crop condition determination record generation and USDA programme filing input. An adversarially crafted multispectral satellite image tile — in which pixel perturbations applied to the spectral reflectance band display region of the false-colour crop canopy composite, the SWIR soil moisture and plant stress indicator visualisation, or the crop health index spatial display layer cause the AI to classify a crop under measurable drought stress, nitrogen deficiency, pest infestation, or disease presence as a healthy crop meeting USDA FSA Farm Ownership Loan collateral eligibility thresholds or USDA NASS crop condition survey “good” or “excellent” category criteria when the actual spectral reflectance signature of the satellite imagery evidences production-impairing stress conditions — can suppress a crop stress indicator that would otherwise generate an FSA loan haircut determination, a crop insurance indemnity trigger, a NASS downward crop condition revision signal, or a NAP programme payment eligibility determination. In agricultural lending, crop insurance, and USDA programme operations where Planet Labs AI or Satellogic AI processes thousands of multispectral satellite image tiles per day without individual human agronomist examination of every AI-processed image tile before the AI classification governs the FSA loan collateral valuation or NASS survey contribution, adversarial suppression of crop stress indicators creates USDA NASS 7 USC §2204 crop reporting integrity, FSA 7 USC §1961 emergency loan eligibility fraud, and federal crop programme support level manipulation dimensions.

The USDA NASS 7 USC §2204, FSA 7 USC §1961, 7 CFR Part 1437 NAP programme, and FSA Direct Farm Ownership Loan regulatory consequences of adversarially suppressed crop stress classification span USDA NASS 7 USC §2204 crop reporting and statistical survey authority establishing the legal framework for NASS crop condition survey data used for federal agricultural policy, commodity price discovery, and crop programme support level calibration — adversarial manipulation of satellite AI crop condition classifications that suppresses below-average crop condition indicators creates NASS survey data integrity and federal crop programme support level calibration failures with commodity market and federal budget dimensions; USDA FSA 7 USC §1961 emergency loan authority creating eligibility requirements for FSA Emergency Loan programme access dependent on documentation of qualifying crop production loss caused by natural disaster, adversarial weather event, or production calamity — adversarially suppressed crop stress satellite AI classifications that mask qualifying production loss conditions create FSA emergency loan eligibility documentation fraud dimensions when suppressed stress indicators enable producers who do not experience qualifying losses to obtain FSA Emergency Loan programme benefits; 7 CFR Part 1437 Noninsured Crop Disaster Assistance Program (NAP) payment trigger requirements establishing crop loss thresholds that must be documented and verified through production loss evidence including satellite and field inspection data — adversarial suppression of crop stress indicators in satellite AI that would otherwise satisfy NAP payment trigger documentation requirements creates NAP programme payment eligibility manipulation dimensions; and USDA FSA Direct Farm Ownership Loan eligibility requirements establishing collateral valuation standards for farm real estate and crop production assets used as FSA loan collateral — adversarially corrupted crop stress AI that inflates crop health classifications for collateral valuation purposes creates FSA loan fraud dimensions with direct federal programme financial exposure. Threshold: 60 for satellite crop stress classification AI — reflecting USDA NASS 7 USC §2204 crop reporting integrity, FSA 7 USC §1961 emergency loan fraud, 7 CFR Part 1437 NAP payment trigger manipulation, and FSA Direct Loan collateral fraud dimensions.

2. NDVI vegetation index injection (USDA ARC/PLC 7 USC §9016, RMA MPCI 7 USC §1508)

NDVI vegetation index AI processes Normalized Difference Vegetation Index (NDVI) composite layer display images generated from multispectral satellite imagery, Enhanced Vegetation Index (EVI) time-series growing season accumulation display images, Soil-Adjusted Vegetation Index (SAVI) bare soil correction composite display images, Red-Edge Chlorophyll Index (CIre) precision crop nutrition spatial distribution display images, growing season NDVI accumulation (GSA) index time-series comparison display images, crop yield estimation model output visualisation images derived from NDVI temporal trajectory analysis, and county-level NDVI deviation from historical mean benchmark display images from Maxar Technologies AI at US government agency and precision agriculture platform client operations processing 30-centimetre resolution imagery archive exceeding 1 billion square kilometres through AI-assisted NDVI layer analysis, crop yield estimation, and change detection classification tools serving NGA, NRO, and commercial agricultural customers; Satellogic AI at crop monitoring client operations processing 1-metre multispectral satellite imagery for NDVI-based crop health monitoring across 60 million or more enrolled hectares through AI-assisted NDVI vegetation index analysis and growing season productivity assessment tools; and ESRI ArcGIS Image AI at 350,000 or more global organisation clients processing remote sensing analysis and change detection workflows integrating Planet, Maxar, and Sentinel NDVI data sources through AI-assisted vegetation index composite analysis, multispectral classification, and agricultural productivity assessment tools — extracting ARC county revenue benchmark calibration inputs, PLC yield history reference values, and RMA MPCI APH verification determinations from NDVI vegetation index layer display image inputs in AI-assisted USDA commodity support programme administration and crop insurance loss determination pipelines.

The adversarial injection surface is the NDVI composite layer display image or EVI time-series display image submission pathway: Maxar Technologies AI or Satellogic AI NDVI vegetation index layer display images submitted through AI-assisted ARC county revenue benchmark and MPCI APH verification tools for AI crop programme calibration record generation and USDA programme filing input. An adversarially crafted NDVI composite layer display image — in which pixel perturbations applied to the vegetation index value colour gradient display region of the county-level NDVI composite map, the EVI growing season accumulation value time-series chart display, or the NDVI deviation from historical mean benchmark comparison display cause the AI to classify a below-average NDVI growing season productivity indicator as a normal or above-average vegetation index result meeting ARC county revenue benchmark calibration criteria or RMA MPCI APH historical yield reference baseline criteria when the actual NDVI satellite data evidences a below-average growing season productivity condition that would trigger ARC county revenue support payment obligations to enrolled commodity producers — can suppress a below-average NDVI indicator that would otherwise generate ARC county revenue support payment triggers, inflate historical NDVI-derived yield reference benchmarks used for PLC yield calculation, or suppress NDVI loss indicators that would trigger RMA MPCI indemnity payment calculations under crop insurance policies in force for the affected crop year. In USDA commodity programme administration and federal crop insurance indemnity operations where ESRI ArcGIS AI or Satellogic AI processes county-level NDVI composite layer and EVI time-series display images without individual human remote sensing analyst examination of every AI-processed vegetation index layer before the AI classification governs the ARC revenue trigger determination or MPCI indemnity calculation, adversarial suppression of below-average NDVI signals creates ARC/PLC 7 USC §9016 revenue trigger fraud and RMA MPCI 7 USC §1508 indemnity calculation manipulation dimensions.

The USDA ARC 7 USC §9016, PLC yield history verification, RMA MPCI 7 USC §1508, and 7 CFR Part 400 regulatory consequences of adversarially suppressed NDVI vegetation index classification span USDA ARC county revenue coverage payment trigger requirements under 7 USC §9016(b) establishing that ARC county payments are triggered when the actual county revenue for a covered commodity falls below the ARC county benchmark revenue — adversarial manipulation of NDVI vegetation index AI that suppresses below-average growing season productivity indicators creates ARC county benchmark revenue calibration manipulation dimensions that affect the revenue comparison used to determine whether ARC county payment obligations are triggered for enrolled commodity programme producers; USDA PLC yield history verification requirements under 7 USC §9016(c) establishing that PLC programme payments are based on the effective price for the covered commodity relative to the PLC reference price with yield history reference values used for payment calculation — adversarially inflated NDVI-derived yield reference benchmarks used in AI-assisted PLC yield history verification create PLC yield calculation inflation dimensions affecting federal commodity support payment obligations; RMA Multi-Peril Crop Insurance (MPCI) Actual Production History (APH) verification requirements under 7 USC §1508(a) and 7 CFR Part 400 establishing that MPCI crop insurance indemnity payments are calculated based on APH yield average and the extent of production loss below the insured yield guarantee — adversarially suppressed NDVI loss indicators in satellite AI that would otherwise support APH-based indemnity calculation triggers create MPCI indemnity fraud risk dimensions when adversarially corrupted NDVI AI classifications enable either suppression of legitimate indemnity triggers or inflation of loss indicators; and RMA Crop Insurance Handbook MPCI APH verification requirements establishing satellite-derived vegetation index data as a supporting evidence source for APH yield verification and loss adjustment — adversarially corrupted NDVI layer AI classifications create APH verification integrity failures with RMA Standard Reinsurance Agreement and crop insurance fraud dimensions. Threshold: 55 for NDVI vegetation index AI — reflecting ARC county revenue trigger manipulation under 7 USC §9016, PLC yield history inflation, RMA MPCI 7 USC §1508 APH indemnity fraud, 7 CFR Part 400 crop insurance regulation compliance, and RMA Crop Insurance Handbook APH verification integrity dimensions.

3. Wildfire damage assessment image injection (FEMA 44 CFR Part 206, SBA 7(b) disaster loan 15 USC §636)

Wildfire damage assessment AI processes post-fire satellite change detection composite display images, differenced Normalized Burn Ratio (dNBR) burned area severity classification grid display images — generated from pre-fire and post-fire NIR and SWIR band ratio differencing to quantify burn severity on a continuous scale from unburned through low, moderate, and high severity burn classifications — burned area perimeter delineation and acreage summary display images, structure damage and destruction assessment grid overlay display images derived from pre- and post-fire high-resolution multispectral change detection analysis, vegetation damage extent and mortality probability spatial distribution display images, debris flow and erosion hazard zone classification display images derived from SAR and multispectral post-fire landscape stability analysis, and county-level burn severity acreage tabulation summary display images from Planet Labs AI at federal agency, state emergency management, and insurance company client operations processing multispectral satellite change detection images for post-fire burn severity mapping, structure damage documentation, and FEMA PA grant calculation support through AI-assisted wildfire damage assessment and disaster documentation tools; Maxar Technologies AI at US government agency operations including FEMA, USDA Forest Service, and state emergency management agency operations processing 30-centimetre resolution post-fire change detection imagery through AI-assisted burn severity classification, structure damage assessment, and disaster documentation tools; and ESRI ArcGIS Image AI at county government, state emergency management, and federal agency operations processing post-fire satellite change detection workflows integrating Planet, Maxar, and Sentinel post-fire imagery through AI-assisted dNBR burn severity classification, structure damage assessment grid generation, and FEMA PA damage category documentation tools — extracting FEMA PA programme damage category determinations, FSA emergency disaster designation documentation inputs, and SBA physical disaster loan property damage verification assessments from dNBR burned area severity classification and structure damage assessment display image inputs in AI-assisted post-disaster federal programme documentation pipelines.

The adversarial injection surface is the dNBR burned area severity classification display image or post-fire change detection composite display image submission pathway: Planet Labs AI or Maxar Technologies AI post-fire satellite change detection and dNBR burn severity classification display images submitted through AI-assisted FEMA PA grant damage documentation and SBA disaster loan property loss verification tools for AI damage category determination record generation and federal disaster programme filing input. An adversarially crafted dNBR burned area severity classification display image — in which pixel perturbations applied to the dNBR burn severity colour gradient display region of the post-fire change detection composite, the structure damage assessment grid overlay classification cells, or the burned area perimeter and high-severity burn zone spatial extent display cause the AI to classify a high-severity burn area with confirmed total structure loss and complete vegetation mortality as a low-severity burn area with partial vegetation damage and no confirmed structure loss when the actual pre- and post-fire multispectral change detection satellite data evidences high-severity burn conditions meeting FEMA Public Assistance Category C through G damage documentation thresholds — can suppress a burn severity and structure damage indicator that would otherwise generate FEMA PA grant obligation calculations, USDA FSA Emergency Loan disaster designation documentation triggers, SBA Physical Disaster Loan property loss eligibility determinations, and NRCS Emergency Watershed Protection programme activation documentation inputs. In FEMA PA programme administration, SBA disaster loan processing, and FSA emergency loan operations where post-fire satellite AI processes dNBR classification and structure damage display images without individual human remote sensing analyst examination of every AI-processed burn severity tile before the AI classification governs the FEMA damage category determination or SBA property loss eligibility assessment, adversarial suppression of high-severity burn and structure damage indicators creates FEMA 44 CFR Part 206 PA grant fraud, SBA 15 USC §636(b) disaster loan eligibility manipulation, and USDA FSA 7 CFR Part 1945 emergency programme designation fraud dimensions.

The FEMA 44 CFR Part 206, SBA 15 USC §636(b), USDA FSA 7 CFR Part 1945, and FEMA PA Programme Policy Version 4 regulatory consequences of adversarially suppressed wildfire damage assessment classification span FEMA 44 CFR Part 206 Public Assistance programme documentation requirements establishing disaster-related damage documentation standards for FEMA PA Subgrant categories including Category A debris removal, Category B emergency protective measures, and Category C through G permanent work on infrastructure and facilities — adversarial manipulation of post-fire satellite AI that suppresses high-severity burn area and structure damage extent indicators creates FEMA PA programme documentation fraud dimensions when adversarially corrupted AI damage classifications either suppress legitimate FEMA PA grant obligation calculations for eligible wildfire-impacted jurisdictions or inflate damage extent documentation beyond what satellite evidence supports; SBA Physical Disaster Loan programme requirements under 15 USC §636(b) establishing that SBA disaster loans require verified physical damage to real property, personal property, or business assets as a result of a declared disaster — adversarially suppressed satellite AI damage classifications that mask total structure loss in high-severity wildfire burn areas create SBA disaster loan eligibility determination fraud dimensions when adversarially corrupted dNBR AI classifications suppress qualifying property damage indicators for SBA Physical Disaster Loan applicants; USDA FSA Emergency Loan programme disaster designation requirements under 7 CFR Part 1945 establishing that FSA Emergency Loans require Secretary of Agriculture disaster designation or Presidentially declared disaster based on documented production loss or physical damage to farm real estate — adversarially suppressed post-fire satellite AI classifications that mask qualifying farm real estate damage extent create FSA Emergency Loan disaster designation fraud dimensions; and FEMA PA Programme Policy Version 4 damage category documentation standards establishing satellite-derived burn severity and structure damage mapping as an accepted damage documentation source for FEMA PA grant calculation and cost eligibility determination. The combination of FEMA PA grant obligations that can reach hundreds of millions of dollars for large wildfire disasters, SBA disaster loan approvals that individually can reach $2 million for businesses and $500,000 for homeowners, and FSA Emergency Loan limits of up to $500,000 per eligible producer creates aggregate federal disaster programme financial exposure dimensions for adversarially corrupted wildfire damage satellite AI that are significant at the scale of major wildfire disaster declarations. Threshold: 55 for wildfire damage assessment AI — reflecting FEMA 44 CFR Part 206 PA grant documentation fraud, SBA 15 USC §636(b) Physical Disaster Loan eligibility manipulation, USDA FSA 7 CFR Part 1945 Emergency Loan disaster designation fraud, and FEMA PA Programme Policy Version 4 damage category documentation integrity dimensions.

4. GPS prescription map injection (EPA FIFRA 7 USC §136, USDA Conservation Reserve Program)

GPS prescription map AI processes GPS field boundary satellite image overlay display images, variable-rate application (VRA) prescription map zone classification and application rate display images derived from satellite vegetation index and soil sampling analysis, site-specific crop input management zone boundary delineation display images derived from NDVI spatial variability and soil electrical conductivity remote sensing analysis, soil sampling grid georeferenced location and analytical result spatial display images, nutrient management plan compliance verification display images derived from satellite-assisted field zone and application rate analysis, pesticide application rate prescription zone display images derived from satellite-assisted pest pressure mapping and economic threshold calculation, and CRP conservation practice establishment and maintenance compliance monitoring display images derived from satellite multi-date vegetation classification analysis from Trimble Agriculture AI at 3 million or more connected acres processing FieldIQ prescription map delivery systems integrating Planet satellite imagery through AI-assisted variable-rate application management, prescription map generation, and EPA FIFRA application record documentation tools; and Indigo Ag AI at carbon programme monitoring and crop insurance client operations processing over 1 million enrolled acres through satellite NDVI-based crop health monitoring and GPS prescription compliance verification systems integrated with Indigo Carbon programme carbon credit certification and USDA CRP compliance monitoring tools — extracting EPA FIFRA pesticide application record accuracy determinations, USDA CRP conservation practice compliance verifications, and USDA EQIP conservation practice performance assessments from GPS prescription map satellite overlay and variable-rate application zone classification display image inputs in AI-assisted agrochemical application record, conservation programme contract compliance, and EQIP practice performance verification pipelines.

The adversarial injection surface is the GPS prescription map satellite overlay display image or variable-rate application zone classification display image submission pathway: Trimble Agriculture AI or Indigo Ag AI GPS prescription map satellite overlay and VRA zone classification display images submitted through AI-assisted EPA FIFRA application record compliance verification and USDA CRP conservation practice monitoring tools for AI compliance determination record generation and conservation programme documentation filing input. An adversarially crafted GPS prescription map display image — in which pixel perturbations applied to the field management zone boundary and application rate classification display region of the prescription map overlay, the variable-rate application rate layer visualisation colour gradient, or the site-specific management zone boundary delineation overlay display cause the AI to classify an out-of-tolerance pesticide overapplication prescription map — in which zone-specific application rates in grams per hectare or ounces per acre exceed the label-specified maximum application rate for the pesticide formulation per EPA FIFRA §2(ee) application in accordance with label requirements — as a compliant within-label-rate prescription map meeting EPA FIFRA pesticide application record accuracy requirements when the actual satellite-assisted prescription map data evidences application rates exceeding the label maximum in identifiable field management zones — can suppress a prescription rate exceedance indicator that would otherwise generate an EPA FIFRA pesticide misuse enforcement referral, a state department of agriculture pesticide application compliance investigation, a USDA CRP contract compliance violation determination, or a USDA EQIP conservation practice cost-share recapture demand. In commercial agricultural operations where Trimble Agriculture AI or Indigo Ag AI processes GPS prescription map satellite overlay display images for hundreds or thousands of field applications per crop season without individual human agronomist examination of every AI-processed prescription map before the AI compliance determination governs the EPA FIFRA application record documentation or USDA CRP contract compliance verification, adversarial suppression of prescription rate exceedance indicators creates EPA FIFRA 7 USC §136 civil penalty, USDA CRP 7 CFR Part 1410 contract violation, and USDA EQIP 7 CFR Part 1466 cost-share recapture dimensions.

The EPA FIFRA 7 USC §136, 7 CFR Part 1410 CRP, 7 CFR Part 1466 EQIP, and EPA 40 CFR Part 152 regulatory consequences of adversarially suppressed GPS prescription map compliance classification span EPA FIFRA 7 USC §136 pesticide application record requirements establishing that commercial pesticide applicators must maintain complete and accurate records of pesticide applications including application rates, field locations, and dates consistent with EPA FIFRA §2(ee) use-in-accordance-with-labelling requirements, with civil penalties of up to $20,000 per violation for certified commercial applicators who apply pesticides in a manner inconsistent with labelling requirements — adversarial manipulation of GPS prescription map satellite AI that suppresses label-rate exceedance indicators creates FIFRA §136 civil penalty dimensions of up to $20,000 per field application event for each prescription map compliance determination that permits out-of-tolerance application records to pass AI compliance review; EPA 40 CFR Part 152 pesticide registration and application record documentation requirements establishing the regulatory framework for pesticide label compliance requirements enforced through EPA FIFRA §12(a)(2)(G) unlawful pesticide application prohibitions; state department of agriculture pesticide application records inspection authority applicable to certified pesticide applicator licensing and compliance monitoring programmes in all 50 states; USDA CRP conservation programme contract compliance requirements under 7 CFR Part 1410 establishing that CRP contract participants must comply with conservation practice installation and maintenance standards including vegetation management and pesticide application restrictions for enrolled acres — adversarially corrupted GPS prescription map AI that suppresses CRP-restricted pesticide application rate exceedance indicators creates CRP contract violation determination dimensions with potential CRP contract liquidated damages and programme re-enrolment ineligibility consequences; and USDA EQIP conservation practice standard compliance requirements under 7 CFR Part 1466 establishing that EQIP payment recipients must implement conservation practices meeting NRCS practice standard performance criteria verified through programme compliance monitoring — adversarially suppressed prescription map compliance indicators create EQIP cost-share recapture demand dimensions requiring repayment of EQIP financial assistance received for practices determined not to meet NRCS performance standards. The cumulative EPA FIFRA civil penalty exposure at $20,000 per violation across a commercial agricultural operation processing hundreds of prescription map applications per season, combined with USDA CRP liquidated damages and EQIP cost-share recapture obligations, creates aggregate regulatory penalty exposure dimensions that make prescription map AI injection a high-consequence adversarial attack surface. Threshold: 65 for GPS prescription map AI — reflecting EPA FIFRA 7 USC §136 civil penalties up to $20,000 per violation, EPA 40 CFR Part 152 label compliance requirements, USDA CRP 7 CFR Part 1410 contract violation determination, and USDA EQIP 7 CFR Part 1466 cost-share recapture dimensions.

Integration: satellite and remote sensing AI image ingestion with Glyphward pre-scan

Satellite and remote sensing AI image ingestion flows from Planet Labs AI and Satellogic AI multispectral crop stress image tile processing channels, Maxar Technologies AI and ESRI ArcGIS Image AI NDVI vegetation index composite layer and post-fire change detection display image processing interfaces, Planet Labs AI and Maxar Technologies AI wildfire dNBR burn severity classification display image processing pipelines, and Trimble Agriculture AI and Indigo Ag AI GPS prescription map satellite overlay and variable-rate application zone classification display image processing platforms into satellite crop stress classification AI, NDVI vegetation index layer and ARC/PLC revenue benchmark calibration AI, wildfire damage assessment and FEMA PA documentation AI, and GPS prescription map EPA FIFRA compliance verification AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to USDA NASS crop condition survey records, FSA loan collateral valuation determinations, ARC/PLC revenue benchmark calibration records, RMA MPCI APH verification records, FEMA PA damage documentation records, SBA disaster loan property loss assessments, EPA FIFRA application record documentation, or USDA CRP and EQIP conservation practice compliance verification records:

import asyncio
import base64
import hashlib
import os
import uuid
from enum import Enum
from pathlib import Path

import httpx

GLYPHWARD_API_KEY = os.environ["GLYPHWARD_API_KEY"]
GLYPHWARD_SCAN_URL = "https://glyphward.com/v1/scan"

# Satellite & remote sensing AI — adversarial pixel injection in multispectral
# image tiles, NDVI composite layers, dNBR burn severity classifications, and
# GPS prescription map overlays with direct USDA, FEMA, SBA, and EPA regulatory
# consequences spanning federal crop programme fraud, crop insurance indemnity
# manipulation, disaster grant documentation fraud, and pesticide record falsification.

# USDA NASS 7 USC §2204 crop reporting; FSA 7 USC §1961 emergency loan eligibility;
# 7 CFR Part 1437 NAP payment triggers; FSA Direct Farm Ownership Loan collateral.
THRESHOLD_CROP_STRESS_CLASSIFICATION_AI  = 60

# ARC county revenue 7 USC §9016; PLC yield history; RMA MPCI 7 USC §1508 APH
# indemnity; 7 CFR Part 400 crop insurance; RMA Crop Insurance Handbook APH.
THRESHOLD_NDVI_VEGETATION_INDEX_AI       = 55

# FEMA 44 CFR Part 206 PA grant documentation; SBA 15 USC §636(b) Physical
# Disaster Loan; USDA FSA 7 CFR Part 1945 Emergency Loan; FEMA PA Policy v4.
THRESHOLD_WILDFIRE_DAMAGE_ASSESSMENT_AI  = 55

# EPA FIFRA 7 USC §136 civil penalties $20k/violation; EPA 40 CFR Part 152;
# USDA CRP 7 CFR Part 1410 contract compliance; USDA EQIP 7 CFR Part 1466.
THRESHOLD_GPS_PRESCRIPTION_MAP_AI        = 65


class SatelliteRemoteSensingAIContext(str, Enum):
    CROP_STRESS_CLASSIFICATION_AI  = "crop_stress_classification_ai"   # Planet Labs, Satellogic, Indigo Ag
    NDVI_VEGETATION_INDEX_AI       = "ndvi_vegetation_index_ai"        # Maxar, Satellogic, ESRI ArcGIS
    WILDFIRE_DAMAGE_ASSESSMENT_AI  = "wildfire_damage_assessment_ai"   # Planet Labs, Maxar, ESRI ArcGIS
    GPS_PRESCRIPTION_MAP_AI        = "gps_prescription_map_ai"         # Trimble Agriculture, Indigo Ag


def threshold_for(context: SatelliteRemoteSensingAIContext) -> int:
    mapping = {
        SatelliteRemoteSensingAIContext.CROP_STRESS_CLASSIFICATION_AI:  THRESHOLD_CROP_STRESS_CLASSIFICATION_AI,
        SatelliteRemoteSensingAIContext.NDVI_VEGETATION_INDEX_AI:       THRESHOLD_NDVI_VEGETATION_INDEX_AI,
        SatelliteRemoteSensingAIContext.WILDFIRE_DAMAGE_ASSESSMENT_AI:  THRESHOLD_WILDFIRE_DAMAGE_ASSESSMENT_AI,
        SatelliteRemoteSensingAIContext.GPS_PRESCRIPTION_MAP_AI:        THRESHOLD_GPS_PRESCRIPTION_MAP_AI,
    }
    return mapping[context]


async def scan_satellite_remote_sensing_ai_image(
    image_path: str | Path,
    context: SatelliteRemoteSensingAIContext,
    field_entity_hash: str,     # SHA-256 of field ID, CLU parcel number, or disaster case number
    programme_ref: str,         # e.g. "NASS-SURVEY-2026-IA-44", "RMA-MPCI-2026-31-109", "FEMA-PA-DR-4821"
    analysis_session_id: str,   # satellite analysis batch ID, disaster assessment session, or application record session
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan a satellite or remote sensing AI image for adversarial injection payloads
    before forwarding to satellite crop stress classification, NDVI vegetation index
    ARC/PLC revenue benchmark, wildfire damage assessment FEMA PA documentation, or
    GPS prescription map EPA FIFRA compliance verification AI.

    Raises AdversarialSatelliteRemoteSensingAIImageError if score meets threshold:
      - CROP_STRESS_CLASSIFICATION_AI:  threshold 60; USDA NASS 7 USC §2204; FSA §1961
      - NDVI_VEGETATION_INDEX_AI:       threshold 55; ARC/PLC 7 USC §9016; RMA §1508
      - WILDFIRE_DAMAGE_ASSESSMENT_AI:  threshold 55; FEMA 44 CFR §206; SBA 15 USC §636
      - GPS_PRESCRIPTION_MAP_AI:        threshold 65; EPA FIFRA 7 USC §136 $20k/violation
    """
    image_bytes    = Path(image_path).read_bytes()
    image_b64      = base64.b64encode(image_bytes).decode()
    image_sha256   = hashlib.sha256(image_bytes).hexdigest()
    client_scan_id = str(uuid.uuid4())
    threshold      = threshold_for(context)

    resp = await client.post(
        GLYPHWARD_SCAN_URL,
        headers={"Authorization": f"Bearer {GLYPHWARD_API_KEY}"},
        json={
            "image": image_b64,
            "source": context.value,
            "metadata": {
                "satellite_rs_context":  context.value,
                "field_entity_hash":     field_entity_hash,
                "programme_ref":         programme_ref,
                "analysis_session_id":   analysis_session_id,
                "client_scan_id":        client_scan_id,
                "image_sha256":          image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "field_entity_hash":    field_entity_hash,
        "programme_ref":        programme_ref,
        "analysis_session_id":  analysis_session_id,
        "satellite_rs_context": context.value,
        "scan_id":              result["scan_id"],
        "client_scan_id":       client_scan_id,
        "image_sha256":         image_sha256,
        "score":                result["score"],
        "flagged_region":       result.get("flagged_region"),
        "threshold":            threshold,
        "action":               "blocked" if result["score"] >= threshold else "allowed",
    }
    await write_satellite_rs_audit_record(audit_record)

    if result["score"] >= threshold:
        raise AdversarialSatelliteRemoteSensingAIImageError(
            f"Satellite remote sensing AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"entity={field_entity_hash} ref={programme_ref}"
        )
    return result


async def write_satellite_rs_audit_record(record: dict) -> None:
    """Persist audit record to satellite and remote sensing AI regulatory documentation store (stub)."""
    import json, sys
    print(json.dumps(record), file=sys.stderr)


class AdversarialSatelliteRemoteSensingAIImageError(Exception):
    """Raised when a satellite or remote sensing AI image exceeds the adversarial injection threshold."""
    pass

Call scan_satellite_remote_sensing_ai_image() with SatelliteRemoteSensingAIContext.CROP_STRESS_CLASSIFICATION_AI before forwarding Planet Labs AI, Satellogic AI, or Indigo Ag AI multispectral satellite crop stress image tiles to crop condition classification and FSA loan collateral valuation AI — with programme_ref linking the Glyphward scan to the NASS survey submission, FSA loan case number, or NAP payment trigger determination for USDA NASS 7 USC §2204 crop reporting integrity, FSA 7 USC §1961 emergency loan eligibility fraud prevention, and 7 CFR Part 1437 NAP payment trigger documentation compliance. Call with SatelliteRemoteSensingAIContext.NDVI_VEGETATION_INDEX_AI for Maxar Technologies AI, Satellogic AI, or ESRI ArcGIS Image AI NDVI composite layer and EVI time-series display images before ARC/PLC revenue benchmark calibration and RMA MPCI APH verification AI, with field_entity_hash as the SHA-256 of the CLU parcel number and county FIPS code for 7 USC §9016 ARC revenue trigger manipulation prevention, RMA MPCI 7 USC §1508 indemnity fraud prevention, and 7 CFR Part 400 crop insurance programme audit trail documentation. Call with SatelliteRemoteSensingAIContext.WILDFIRE_DAMAGE_ASSESSMENT_AI for Planet Labs AI or Maxar Technologies AI post-fire dNBR burn severity classification display images before FEMA PA damage documentation and SBA disaster loan property loss verification AI, with programme_ref as the FEMA disaster declaration number for FEMA 44 CFR Part 206 PA grant documentation fraud prevention, SBA 15 USC §636(b) Physical Disaster Loan property loss eligibility audit trail, and USDA FSA 7 CFR Part 1945 Emergency Loan disaster designation fraud prevention. Call with SatelliteRemoteSensingAIContext.GPS_PRESCRIPTION_MAP_AI for Trimble Agriculture AI or Indigo Ag AI GPS prescription map satellite overlay and VRA zone classification display images before EPA FIFRA application record compliance and USDA CRP conservation practice monitoring AI — with analysis_session_id for EPA FIFRA 7 USC §136 civil penalty prevention at $20,000 per prescription rate exceedance violation, USDA CRP 7 CFR Part 1410 contract compliance fraud prevention, and USDA EQIP 7 CFR Part 1466 conservation practice cost-share recapture audit trail documentation. Get early access

Coverage matrix

Related questions

Further reading

• FigStep adversarial image injection detection — technical overview of the pixel-level adversarial perturbation attack methodology that underlies satellite multispectral image tile injection, NDVI composite layer perturbation, and dNBR burn severity classification manipulation at the spectral band display layer.
• Vision-language model security — architectural overview of multimodal AI adversarial injection vulnerability covering the VLM image encoder and cross-attention layers that satellite remote sensing AI platforms including ESRI ArcGIS Image AI use to process multispectral composite and change detection display images.
• Free tier — 10 scans/day, no card required — start scanning satellite and remote sensing AI image inputs at development volumes before committing to a production plan; test NDVI layer, dNBR composite, and GPS prescription map injection detection without a payment method on file.
• Prompt injection scanner for insurance AI — related adversarial attack surface covering insurance underwriting and claims AI, including crop insurance indemnity and property damage assessment AI with MPCI, RMA Standard Reinsurance Agreement, and FEMA PA programme dimensions that intersect directly with satellite wildfire damage assessment and NDVI-based crop loss AI.
• PDF prompt injection detection — related injection surface covering AI platforms that process satellite imagery analysis reports, wildfire damage assessment PDFs, FEMA PA project worksheet documents, and EPA FIFRA application record PDFs generated from satellite remote sensing AI outputs.
• Multimodal AI security checklist — comprehensive security checklist for AI systems processing image inputs, with specific coverage of satellite imagery pipeline injection hardening, NDVI composite layer integrity verification, and GPS prescription map display image adversarial input controls.
• EU AI Act Article 15 multimodal prompt injection — EU AI Act robustness and accuracy requirements under Article 15 applicable to high-risk AI systems processing satellite imagery and remote sensing data, including requirements for adversarial robustness verification in AI systems used for environmental monitoring, agricultural programme compliance, and disaster damage assessment.