Prompt injection in drone and UAV delivery and inspection AI

Four adversarial injection surfaces in drone and UAV delivery and inspection AI

1. Package delivery label and address verification image injection (18 USC §1341, FAA Part 107 §107.36)

Package delivery label and address verification AI processes delivery address label photograph images displaying printed shipping label fields including recipient name, delivery address, postal barcode, and package identifier, recipient identity verification photograph display images showing government-issued ID documents and facial comparison confirmation screens, drop-zone authorization display images showing geofenced approved delivery location confirmations, controlled substance shipment authorization display images presenting DEA registration and recipient licence verification status screens, and age-restricted delivery authorization display images showing recipient eligibility confirmation screens from Amazon Prime Air AI at 10 or more US cities under FAA BVLOS waiver authority processing delivery label and recipient verification images for AI-assisted autonomous last-mile delivery authorization decisions across hundreds of daily delivery operations per city; Wing Aviation AI at Alphabet across 300,000 or more cumulative deliveries in US, Australian, and Finnish markets processing delivery address label and drop-zone authorization images through AI-assisted delivery routing and recipient authorization systems; and Zipline AI at 700 or more hospital and clinic delivery locations across 8 countries processing medical supply delivery label, consignee authorization, and controlled substance chain-of-custody verification display images through AI-assisted drone delivery authorization and pharmaceutical supply chain documentation tools — extracting delivery authorization determinations, recipient identity confirmations, controlled substance delivery eligibility assessments, and drop-zone safety approvals from delivery label and recipient verification image inputs in AI-assisted last-mile drone delivery authorization pipelines at delivery volumes that make individual human reviewer inspection of every package label and recipient identity image impracticable for commercial drone delivery platform operations.

The adversarial injection surface is the delivery address label image and recipient identity verification display image submission pathway: Amazon Prime Air AI, Wing Aviation AI, or Zipline AI delivery label and drop-zone authorization display images submitted through AI-assisted autonomous delivery authorization and recipient verification tools for AI delivery completion authorization record generation. An adversarially crafted delivery address label image — in which pixel perturbations applied to the printed recipient name field, delivery address field, postal barcode region, or DEA registration number display region of the shipping label cause the AI to classify a package addressed to an unauthorized recipient or an address outside the approved BVLOS delivery corridor as a valid delivery authorization for the intended geofenced drop-zone location when the actual shipping label specifies a different recipient, address, or controlled substance delivery authorization status — can enable delivery to an unauthorized recipient, authorize delivery of Schedule II–V controlled substance shipments to unregistered consignees, suppress address mismatch indicators that would otherwise trigger human review, or falsify recipient identity verification that would otherwise generate a delivery hold for age-restricted or DEA-controlled shipments. In drone delivery operations where Amazon Prime Air AI or Wing Aviation AI processes hundreds of package label images per day without individual human package handler examination of every AI-processed label image before the AI authorization governs autonomous delivery execution, adversarial manipulation of delivery label images creates 18 USC §1341 mail and package fraud, USPS 39 USC §3005 mail fraud authority, and FDA 21 USC §829 controlled substance delivery prohibition dimensions.

The 18 USC §1341, USPS 39 USC §3005, FDA 21 USC §829, and FAA Part 107 §107.36 regulatory consequences of adversarially corrupted delivery label verification classification span 18 USC §1341 mail and package fraud prohibitions establishing criminal liability for knowing use of postal and commercial carrier delivery services to execute fraudulent schemes — adversarial manipulation of package delivery AI that authorizes delivery to fraudulent recipients or fabricates recipient authorization records for controlled substance shipments creates federal mail fraud criminal liability dimensions; USPS 39 USC §3005 mail fraud authority establishing USPS authority to issue cease-and-desist orders against fraudulent mail schemes affecting USPS-partnered drone delivery services; FDA 21 USC §829 controlled substance prescription requirements establishing that Schedule II–V controlled substances may only be dispensed to registered practitioners and licensed pharmacies — adversarially corrupted Zipline AI delivery label verification that authorizes pharmaceutical drone delivery to unregistered consignees creates DEA 21 CFR Part 1301 registration and FDA REMS programme delivery authorization fraud dimensions; FAA Part 107 §107.36 cargo delivery limitations establishing that FAA Part 107 BVLOS waiver operations must comply with approved operating procedures for package identification and delivery authorization — adversarially corrupted delivery label AI creates FAA BVLOS waiver compliance violation dimensions; and state consumer protection statutes applicable to fraudulent proof-of-delivery records generated by adversarially corrupted AI delivery label authorization. Threshold: 55 for package delivery label and address verification AI — reflecting 18 USC §1341 mail fraud, USPS 39 USC §3005 authority, FDA 21 USC §829 controlled substance delivery, FAA Part 107 §107.36 cargo operations, and DEA 21 CFR Part 1301 registration compliance dimensions.

2. UAV infrastructure inspection finding image injection (PHMSA 49 CFR Part 195, NERC CIP-014)

UAV infrastructure inspection finding AI processes drone-captured pipeline exterior corrosion condition display images showing pipeline segment surface condition classification by corrosion severity grade, powerline conductor and tower structural integrity condition display images showing sag, arc flash risk, and conductor damage classification by inspection severity category, bridge deck and superstructure condition rating display images showing defect density and structural deterioration classification derived from drone inspection photography analysis, utility infrastructure damage assessment display images showing post-storm or post-event damage classification by repair urgency and safety risk category, substation physical security condition and intrusion detection display images processed through BRINC AI drone operations, and industrial facility equipment condition and anomaly detection display images from Skydio AI at US DoD and 500 or more enterprise client operations processing UAV inspection imagery for AI-assisted infrastructure condition classification, defect detection, and regulatory compliance inspection documentation; DJI Enterprise FlightHub 2 AI at 600,000 or more global enterprise users processing pipeline, powerline, wind turbine, cell tower, and bridge inspection drone imagery through FlightHub 2 cloud AI analysis and DJI Dock 2 autonomous inspection platform tools; Percepto AI at 100 or more enterprise industrial inspection deployment sites across oil and gas, utility, and transportation infrastructure operations processing autonomous continuous inspection drone imagery through AI-assisted anomaly detection, condition classification, and regulatory inspection interval compliance tools; and BRINC AI at 1,000 or more law enforcement agency operations processing public safety drone inspection imagery through AI-assisted situational assessment and infrastructure security monitoring tools — extracting infrastructure condition classification determinations, defect severity assessments, regulatory inspection interval compliance verifications, and safety risk category assignments from drone inspection finding display image inputs in AI-assisted PHMSA pipeline inspection, NERC grid infrastructure security, FERC dam safety, and OSHA industrial facility safety documentation pipelines.

The adversarial injection surface is the drone-captured infrastructure inspection finding display image submission pathway: Skydio AI, DJI Enterprise FlightHub 2 AI, or Percepto AI UAV inspection finding display images submitted through AI-assisted pipeline corrosion classification, powerline structural integrity assessment, and bridge condition rating tools for AI inspection compliance record generation and regulatory filing input. An adversarially crafted pipeline inspection display image — in which pixel perturbations applied to the corrosion severity grade colour classification display region of the pipeline exterior condition image, the structural defect density indicator overlay on the bridge superstructure photograph, or the anomaly detection alert indicator on the industrial facility equipment condition image cause the AI to classify a pipeline segment with active pitting corrosion meeting PHMSA 49 CFR Part 195.452 anomaly significance thresholds, a powerline conductor with NERC reliability standard violation-level structural damage, or a bridge deck with National Bridge Inspection Standards critical finding-level deterioration as a within-tolerance condition not requiring immediate remediation or regulatory notification when the actual drone inspection imagery evidences defect conditions meeting regulatory significance thresholds — can suppress a safety-significant inspection finding that would otherwise generate a PHMSA pipeline anomaly remediation timeline obligation, a NERC CIP-014 physical security event report, an FHWA National Bridge Inspection Standards critical finding notification, or an OSHA equipment safety lockout/tagout requirement. In pipeline, utility, and transportation infrastructure inspection programmes where Skydio AI or Percepto AI processes hundreds of drone inspection images per inspection cycle without individual human safety inspector examination of every AI-processed defect image before the AI classification governs the inspection interval compliance determination or regulatory defect notification, adversarial suppression of safety-significant inspection findings creates PHMSA 49 CFR Part 195, NERC CIP-014, and FERC 18 CFR Part 12 regulatory compliance dimensions.

The PHMSA 49 CFR Part 195, NERC CIP-014-3, FERC 18 CFR Part 12, and OSHA 29 CFR Part 1910.147 regulatory consequences of adversarially suppressed infrastructure inspection classification span PHMSA 49 CFR Part 195.452 integrity management programme requirements for hazardous liquid pipelines in high-consequence areas establishing that pipeline operators must assess, evaluate, and remediate pipeline anomalies meeting significance thresholds within specified regulatory timeframes including 60-day, 180-day, and 1-year remediation windows depending on anomaly severity — adversarial suppression of corrosion severity indicators in UAV inspection AI that prevents PHMSA-significant pipeline anomalies from reaching the regulatory remediation timeline triggers creates PHMSA civil penalty exposure of up to $266,015 per violation per day under 49 USC §60122 and potential criminal liability for knowing violations; NERC CIP-014-3 physical security of critical electric infrastructure requirements establishing that transmission owners and operators must implement physical security plans addressing identified vulnerabilities at transmission facilities — adversarially suppressed BRINC AI or DJI Enterprise AI physical security condition classification that conceals vulnerability indicators creates NERC CIP-014 compliance failure dimensions with NERC civil penalty authority of up to $1 million per violation per day; FERC 18 CFR Part 12 dam safety and independent consultants inspection requirements applicable to UAV-assisted dam face condition assessment AI; and OSHA 29 CFR Part 1910.147 control of hazardous energy (lockout/tagout) requirements applicable to industrial facility equipment condition AI inspection that suppresses energy isolation safety requirement indicators — creating OSHA willful violation civil penalty exposure of up to $156,259 per violation. Threshold: 65 for UAV infrastructure inspection finding AI — reflecting PHMSA 49 CFR Part 195 pipeline integrity management, NERC CIP-014 physical security, FERC 18 CFR Part 12 dam safety, and OSHA 29 CFR Part 1910.147 equipment safety regulatory compliance dimensions.

3. FAA Remote ID and obstacle detection display injection (FAA 14 CFR Part 89, 49 USC §44809)

FAA Remote ID and obstacle detection display AI processes FAA Remote ID broadcast status display images showing UAS serial number, position, altitude, velocity, and Emergency status field validation confirmation screens, airspace display and BVLOS corridor safety indicator display images showing UTM (Unmanned Traffic Management) USS-provider-validated corridor status, obstacle clearance confidence map display images showing AI-computed clearance probability grids for low-altitude drone flight corridor obstacle avoidance, LAANC (Low Altitude Authorization and Notification Capability) authorization grid display images showing airspace authorization status for commercial drone operations below 400 feet AGL, and ground risk classification display images showing population density and critical infrastructure proximity indicators from Amazon Prime Air AI at BVLOS waiver-authorized operations in 10 or more US cities processing FAA Remote ID compliance and airspace authorization display images through AI-assisted autonomous flight management and airspace deconfliction systems; DJI Enterprise FlightHub 2 AI at 600,000 or more global enterprise users processing airspace authorization, Remote ID compliance status, and obstacle detection display images through FlightHub 2 cloud-based flight management and DJI Dock 2 autonomous inspection launch systems; and Wing Aviation AI at Alphabet processing FAA UTM corridor validation, Remote ID broadcast verification, and low-altitude obstacle clearance display images through AI-assisted autonomous delivery flight management and BVLOS safety monitoring systems — extracting FAA regulatory compliance status determinations, airspace authorization confirmations, obstacle clearance safety assessments, and BVLOS corridor authorization validations from Remote ID status display and airspace compliance indicator image inputs in AI-assisted autonomous drone flight management pipelines at operational tempos where per-flight human airspace deconfliction review of every AI-processed display image is impracticable for large-scale BVLOS delivery and inspection operations.

The adversarial injection surface is the FAA Remote ID broadcast status display image, airspace authorization confirmation display image, or obstacle clearance confidence map display image submission pathway: Amazon Prime Air AI, DJI Enterprise FlightHub 2 AI, or Wing Aviation AI Remote ID and airspace compliance display images submitted through AI-assisted autonomous flight authorization and BVLOS corridor validation tools for AI airspace compliance determination record generation and FAA regulatory filing input. An adversarially crafted obstacle clearance confidence map display image — in which pixel perturbations applied to the obstacle clearance probability grid cell values in the low-altitude flight corridor visualisation, the obstacle proximity alert indicator display, or the UTM corridor safety status indicator cause the AI to classify a flight corridor with obstacle clearance confidence below the BVLOS waiver operating procedure minimum safety threshold as meeting BVLOS safety corridor clearance requirements when the actual sensor-derived obstacle proximity data evidences unacceptable clearance margins — can authorize an autonomous drone delivery or inspection flight through an obstacle-contested corridor that the BVLOS safety operating procedure would otherwise prohibit, suppress a Remote ID broadcast failure indicator that would otherwise ground the aircraft pending maintenance, or fabricate LAANC airspace authorization status for a flight operation in restricted airspace. In autonomous BVLOS delivery operations where Amazon Prime Air AI or Wing Aviation AI relies on AI-processed airspace display images for real-time flight authorization and safety corridor validation without continuous human pilot oversight of every AI airspace assessment before the assessment governs autonomous flight execution, adversarial manipulation of FAA Remote ID and obstacle detection display images creates FAA 14 CFR Part 89 Remote ID compliance violation, FAA Part 107 §107.51 operating limitations violation, and 49 USC §44809 unauthorized recreational drone operation dimensions with potential mid-air safety consequences.

The FAA 14 CFR Part 89, FAA Part 107 §107.51, FAA UTM USS provider requirements, and 49 USC §44809 regulatory consequences of adversarially corrupted Remote ID and obstacle detection classification span FAA 14 CFR Part 89 Remote ID broadcast requirements establishing that all UAS operating under FAA rules must transmit compliant Remote ID broadcasts containing serial number, position, altitude, velocity, and takeoff location with civil penalty authority up to $27,500 per violation per flight and potential aircraft registration suspension — adversarial suppression of Remote ID broadcast failure indicators in FAA compliance display AI creates per-flight Remote ID violation dimensions; FAA Part 107 §107.51 BVLOS operating limitations establishing that BVLOS waiver operations must comply with approved safety case operating procedures including minimum obstacle clearance margins, airspace deconfliction procedures, and real-time situational awareness requirements — adversarially corrupted obstacle clearance AI that authorizes flights below waiver-required clearance margins creates BVLOS waiver compliance violation and suspension dimensions; FAA UTM USS provider service requirements establishing that FAA-approved UTM USS providers must ensure airspace deconfliction and safety corridor validation data integrity — adversarially corrupted UTM corridor status display AI creates USS provider certification compliance dimensions; and 49 USC §44809 recreational UAS operating limitations applicable to non-commercial drone operations and the broad FAA civil aviation safety authority establishing criminal liability for knowing violations of FAA regulations that endanger aircraft safety — adversarially corrupted obstacle detection AI that enables collisions with manned aircraft or ground infrastructure creates 49 USC §46307 criminal penalty dimensions. The combination of BVLOS waiver operational authority that governs Amazon Prime Air, Wing, and DJI Enterprise autonomous flight operations and the life-safety consequences of obstacle clearance AI failure creates the highest adversarial injection risk profile among drone AI surfaces. Threshold: 70 for FAA Remote ID and obstacle detection display AI — reflecting FAA 14 CFR Part 89 Remote ID compliance, FAA Part 107 §107.51 BVLOS limitations, FAA UTM USS safety requirements, and 49 USC §44809 civil aviation safety dimensions.

4. Proof-of-delivery and recipient confirmation image injection (FTC Act 15 USC §45, Carmack Amendment 49 USC §14706)

Proof-of-delivery and recipient confirmation AI processes drop-zone landing confirmation photograph images displaying package placement in the AI-designated approved delivery location with package condition, placement accuracy, and drop-zone clearance indicator overlays, package delivery completion display images showing AI-generated delivery confirmation timestamps, GPS coordinate annotations, and package identifier barcodes superimposed on the drop-zone landing site photograph, recipient signature or biometric confirmation display images showing digital signature capture or facial recognition delivery confirmation screens for high-value or signature-required shipments, package condition at delivery documentation display images showing AI-classified package exterior damage condition assessments from drop-zone landing photographs, and contactless delivery authorization confirmation display images showing resident-specified delivery location approval status confirmation screens from Amazon Prime Air AI at BVLOS waiver delivery operations processing drop-zone landing confirmation and delivery completion photographs through AI-assisted proof-of-delivery record generation systems; Wing Aviation AI at 300,000 or more cumulative deliveries processing drop-zone confirmation and delivery completion documentation photographs through AI-assisted contactless delivery confirmation record and customer notification systems; and Zipline AI at 700 or more hospital delivery locations processing medical supply delivery confirmation and consignee receipt display images through AI-assisted pharmaceutical supply chain delivery documentation and cold-chain compliance verification systems — extracting delivery completion certification records, carrier liability clearance determinations, package condition at delivery assessments, and consumer delivery notification triggers from drop-zone confirmation photograph and delivery completion documentation display image inputs in AI-assisted autonomous drone delivery record and carrier liability documentation pipelines.

The adversarial injection surface is the drop-zone confirmation photograph image or delivery completion documentation display image submission pathway: Amazon Prime Air AI, Wing Aviation AI, or Zipline AI drop-zone landing and delivery completion display images submitted through AI-assisted proof-of-delivery record generation and carrier liability clearance tools for AI delivery confirmation record creation and customer notification dispatch. An adversarially crafted drop-zone confirmation photograph — in which pixel perturbations applied to the package placement indicator overlay, the GPS coordinate annotation display, the delivery completion timestamp, or the package condition classification indicator cause the AI to classify an incomplete, failed, or misdelivered delivery operation — where the package was not placed in the approved drop-zone, landed outside the geofenced delivery boundary, was damaged on landing, or was not delivered at all — as a successful completed delivery meeting carrier proof-of-delivery record requirements when the actual drop-zone photograph evidences delivery failure, package damage, or misdelivery — can fabricate a proof-of-delivery record that falsely certifies package delivery completion, suppress package damage at delivery indicators that would otherwise trigger carrier liability claims under Carmack Amendment 49 USC §14706, create fraudulent delivery timestamps for non-occurring deliveries, or prevent consumer non-delivery complaint triggers in cases of actual delivery failure. In drone delivery operations where Amazon Prime Air AI or Wing Aviation AI processes drop-zone confirmation photographs and generates proof-of-delivery records without individual human delivery confirmation review of every AI-processed photograph before the AI record governs carrier liability allocation and customer delivery notification, adversarial manipulation of delivery confirmation images creates FTC Act 15 USC §45 deceptive trade practices, Carmack Amendment 49 USC §14706 carrier liability fraud, and UCC Article 2 §2-503 delivery completion fraud dimensions.

The FTC Act 15 USC §45, UCC Article 2 §2-503, Carmack Amendment 49 USC §14706, and state consumer protection statute regulatory consequences of adversarially fabricated proof-of-delivery confirmation classification span FTC Act 15 USC §45 unfair and deceptive trade practices authority establishing FTC enforcement jurisdiction over materially false or misleading delivery confirmation records provided to consumers — adversarially fabricated AI proof-of-delivery records that falsely certify package delivery creates FTC deceptive trade practices enforcement dimensions with civil penalty authority of up to $51,744 per violation under 15 USC §45(m) for violations of FTC cease-and-desist orders; UCC Article 2 §2-503 tender of delivery requirements establishing that a seller’s delivery obligation requires putting and holding conforming goods at the buyer’s disposition in a manner that enables the buyer to take delivery — adversarially fabricated drone delivery proof-of-delivery records create UCC §2-503 tender failure dimensions affecting consumer contract rights and merchant return and refund obligations; Carmack Amendment 49 USC §14706 carrier liability for loss and damage establishing that common carriers are liable for actual loss or injury to property from the time of receipt to the time of delivery — adversarially fabricated AI proof-of-delivery records that falsely certify delivery completion of packages that were damaged, lost, or misdelivered create Carmack Amendment carrier liability allocation fraud dimensions when fraudulent delivery confirmation records are used to defeat consumer claims for damaged or non-delivered goods; and state consumer protection statutes in all 50 states prohibiting unfair, deceptive, or unconscionable trade practices in consumer transactions — adversarially fabricated drone delivery confirmation records create state consumer protection liability dimensions with statutory damages, attorney fee-shifting, and class action exposure in states with strong private right of action consumer protection statutes. Threshold: 45 for proof-of-delivery and recipient confirmation AI — reflecting FTC Act 15 USC §45 deceptive trade practices, UCC Article 2 §2-503 delivery completion fraud, Carmack Amendment 49 USC §14706 carrier liability manipulation, and state consumer protection statute dimensions.

Integration: drone and UAV delivery and inspection AI image ingestion with Glyphward pre-scan

Drone and UAV AI image ingestion flows from Amazon Prime Air AI, Wing Aviation AI, and Zipline AI package delivery label and recipient verification image processing channels, Skydio AI, DJI Enterprise FlightHub 2 AI, and Percepto AI UAV infrastructure inspection finding display image processing interfaces, Amazon Prime Air AI, DJI Enterprise FlightHub 2 AI, and Wing Aviation AI FAA Remote ID and obstacle detection display image processing pipelines, and Amazon Prime Air AI, Wing Aviation AI, and Zipline AI drop-zone confirmation and proof-of-delivery photograph processing platforms into delivery label authorization AI, infrastructure inspection classification AI, airspace compliance AI, and delivery confirmation record AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to delivery authorization records, inspection compliance documentation, airspace safety determinations, or proof-of-delivery carrier 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"

# Drone & UAV delivery and inspection AI — adversarial pixel injection in
# package delivery label images, UAV infrastructure inspection findings, FAA
# Remote ID and obstacle detection displays, and proof-of-delivery photographs
# with 18 USC §1341 mail fraud, PHMSA 49 CFR Part 195, FAA 14 CFR Part 89,
# and FTC Act 15 USC §45 regulatory consequences.

# 18 USC §1341 mail and package fraud; USPS 39 USC §3005 mail fraud authority;
# FDA 21 USC §829 controlled substance delivery; FAA Part 107 §107.36 cargo.
THRESHOLD_DELIVERY_LABEL_VERIFICATION_AI  = 55

# PHMSA 49 CFR Part 195 pipeline integrity; NERC CIP-014 critical infrastructure;
# FERC 18 CFR Part 12 dam safety; OSHA 29 CFR Part 1910.147 equipment safety.
THRESHOLD_INFRASTRUCTURE_INSPECTION_AI   = 65

# FAA 14 CFR Part 89 Remote ID; FAA Part 107 §107.51 BVLOS limitations;
# FAA UTM USS provider requirements; 49 USC §44809 civil aviation safety.
THRESHOLD_FAA_REMOTE_ID_OBSTACLE_AI      = 70

# FTC Act 15 USC §45 deceptive trade practices; UCC Article 2 §2-503 delivery;
# Carmack Amendment 49 USC §14706 carrier liability; state consumer protection.
THRESHOLD_PROOF_OF_DELIVERY_AI           = 45


class DroneUAVAIContext(str, Enum):
    DELIVERY_LABEL_VERIFICATION_AI  = "delivery_label_verification_ai"   # Amazon Prime Air, Wing, Zipline
    INFRASTRUCTURE_INSPECTION_AI    = "infrastructure_inspection_ai"     # Skydio, DJI Enterprise, Percepto
    FAA_REMOTE_ID_OBSTACLE_AI       = "faa_remote_id_obstacle_ai"        # Amazon Prime Air, DJI FlightHub 2, Wing
    PROOF_OF_DELIVERY_AI            = "proof_of_delivery_ai"             # Amazon Prime Air, Wing, Zipline


def threshold_for(context: DroneUAVAIContext) -> int:
    mapping = {
        DroneUAVAIContext.DELIVERY_LABEL_VERIFICATION_AI:  THRESHOLD_DELIVERY_LABEL_VERIFICATION_AI,
        DroneUAVAIContext.INFRASTRUCTURE_INSPECTION_AI:    THRESHOLD_INFRASTRUCTURE_INSPECTION_AI,
        DroneUAVAIContext.FAA_REMOTE_ID_OBSTACLE_AI:       THRESHOLD_FAA_REMOTE_ID_OBSTACLE_AI,
        DroneUAVAIContext.PROOF_OF_DELIVERY_AI:            THRESHOLD_PROOF_OF_DELIVERY_AI,
    }
    return mapping[context]


async def scan_drone_uav_ai_image(
    image_path: str | Path,
    context: DroneUAVAIContext,
    uav_entity_hash: str,       # SHA-256 of UAS serial number or delivery order ID
    programme_ref: str,         # e.g. "PRIME-AIR-ORD-2026-04881", "SKYDIO-INSP-PLN-7712"
    flight_session_id: str,     # FAA Remote ID session or delivery flight batch ID
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan a drone or UAV AI image for adversarial injection payloads
    before forwarding to delivery label authorization, infrastructure
    inspection classification, FAA Remote ID airspace compliance, or
    proof-of-delivery confirmation record AI.

    Raises AdversarialDroneUAVAIImageError if score meets threshold:
      - DELIVERY_LABEL_VERIFICATION_AI:  threshold 55; 18 USC §1341; FDA 21 USC §829
      - INFRASTRUCTURE_INSPECTION_AI:    threshold 65; PHMSA 49 CFR Part 195; NERC CIP-014
      - FAA_REMOTE_ID_OBSTACLE_AI:       threshold 70; FAA 14 CFR Part 89; 49 USC §44809
      - PROOF_OF_DELIVERY_AI:            threshold 45; FTC 15 USC §45; 49 USC §14706
    """
    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": {
                "drone_uav_context":   context.value,
                "uav_entity_hash":     uav_entity_hash,
                "programme_ref":       programme_ref,
                "flight_session_id":   flight_session_id,
                "client_scan_id":      client_scan_id,
                "image_sha256":        image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "uav_entity_hash":    uav_entity_hash,
        "programme_ref":      programme_ref,
        "flight_session_id":  flight_session_id,
        "drone_uav_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_drone_uav_audit_record(audit_record)

    if result["score"] >= threshold:
        raise AdversarialDroneUAVAIImageError(
            f"Drone UAV AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"entity={uav_entity_hash} ref={programme_ref}"
        )
    return result


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


class AdversarialDroneUAVAIImageError(Exception):
    """Raised when a drone or UAV AI image exceeds the adversarial injection threshold."""
    pass

Call scan_drone_uav_ai_image() with DroneUAVAIContext.DELIVERY_LABEL_VERIFICATION_AI before forwarding Amazon Prime Air AI, Wing Aviation AI, or Zipline AI delivery address label and recipient verification images to delivery authorization AI — with programme_ref as the delivery order number and uav_entity_hash as the SHA-256 of the UAS serial number for 18 USC §1341 mail fraud prevention, USPS 39 USC §3005 authority compliance, and FDA 21 USC §829 controlled substance delivery authorization audit trail. Call with DroneUAVAIContext.INFRASTRUCTURE_INSPECTION_AI for Skydio AI, DJI Enterprise FlightHub 2 AI, or Percepto AI UAV inspection finding display images before pipeline corrosion, powerline structural integrity, and facility equipment safety classification AI — with programme_ref as the inspection programme identifier for PHMSA 49 CFR Part 195.452 pipeline integrity management, NERC CIP-014 critical infrastructure security, and OSHA 29 CFR Part 1910.147 equipment safety compliance. Call with DroneUAVAIContext.FAA_REMOTE_ID_OBSTACLE_AI for Amazon Prime Air AI, DJI Enterprise FlightHub 2 AI, or Wing Aviation AI Remote ID compliance status and obstacle clearance display images before airspace authorization AI — with flight_session_id as the FAA UTM session identifier for FAA 14 CFR Part 89 Remote ID compliance, FAA Part 107 §107.51 BVLOS limitations audit trail, and 49 USC §44809 civil aviation safety documentation. Call with DroneUAVAIContext.PROOF_OF_DELIVERY_AI for drop-zone confirmation and delivery completion images before carrier record AI — with programme_ref as the delivery order ID for FTC Act 15 USC §45 deceptive trade practices prevention and Carmack Amendment 49 USC §14706 carrier liability 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 underlying delivery label image injection, UAV inspection finding display suppression, and FAA Remote ID airspace display image corruption.
• Vision-language model security — architectural overview of multimodal AI adversarial injection covering the VLM image encoder and cross-attention layers that Amazon Prime Air AI, DJI Enterprise FlightHub 2 AI, and Skydio AI use to process drone capture and display images.
• Free tier — 10 scans/day, no card required — start scanning drone delivery and UAV inspection AI image inputs at development volumes before committing to a production plan; test delivery label, obstacle clearance, and inspection finding injection detection without a payment method on file.
• Prompt injection in autonomous vehicle fleet safety AI — related adversarial attack surface covering autonomous ground vehicle AI with overlapping BVLOS safety corridor, obstacle detection, and regulatory compliance injection dimensions.
• Prompt injection in CCTV and physical security AI — related physical security AI injection surface covering surveillance and intrusion detection AI with overlapping NERC CIP-014 and critical infrastructure security dimensions relevant to BRINC AI public safety drone operations.
• Prompt injection in aerospace and defence AI — related aerospace AI injection surface covering US DoD and defence contractor AI systems with overlapping Skydio AI DoD deployment and FAA airspace safety dimensions.
• Prompt injection scanner for computer use agents — related agentic autonomous systems injection surface covering AI agents that operate autonomous computer interfaces with structural parallels to autonomous drone flight management AI decision loops.