Prompt injection in restaurant and food delivery AI

Four adversarial injection surfaces in restaurant and food delivery AI

1. Restaurant food safety inspection image injection (FDA Food Code, OSHA)

Restaurant food safety inspection AI processes health department sanitarian inspection report display images, food safety inspection score and letter grade display photographs, critical and non-critical food safety violation photograph evidence images, food temperature log and time-temperature abuse documentation display images, food handler personal hygiene and handwashing compliance photograph images, pest and rodent evidence documentation photographs, food contact surface sanitation and chemical concentration display images, and food storage and labelling compliance photograph evidence from restaurant chain food safety compliance AI platforms processing health department inspection photograph evidence and violation documentation images through AI-assisted critical control point violation identification and food safety score classification tools at major restaurant chains including McDonald’s, Yum! Brands (KFC/Taco Bell/Pizza Hut), Restaurant Brands International (Burger King/Popeyes), and Darden Restaurants (Olive Garden/LongHorn Steakhouse) managing food safety compliance across tens of thousands of restaurant locations; health department AI platforms and restaurant inspection technology vendors including Registrar Corp AI, FoodLogiQ AI, and Alchemy Systems AI at restaurant food safety management system operations processing health department inspection result images through AI-assisted food safety compliance monitoring tools; and food delivery platform food safety compliance operations at Olo AI, DoorDash AI, and Uber Eats AI processing restaurant partner inspection score and compliance documentation images through AI-assisted food safety tier and delivery platform eligibility classification tools — extracting food safety critical violation classifications and restaurant food safety compliance tier determinations from health department inspection photograph and violation evidence image inputs in AI-assisted multi-location restaurant food safety compliance monitoring pipelines.

The adversarial injection surface is the health department inspection report photograph, food safety violation evidence image, or food safety score display image submission pathway: restaurant chain food safety AI or Olo AI restaurant partner inspection compliance images submitted through AI-assisted critical control point violation identification and restaurant food safety score classification tools for AI food safety compliance monitoring record generation and delivery platform eligibility determination input. An adversarially crafted health department inspection photograph or food safety violation evidence image — in which pixel perturbations applied to the food temperature non-conformance display region, the foodborne illness risk factor violation visual marker, or the rodent activity or pest evidence documentation display in a restaurant inspection photograph cause the AI to classify a restaurant inspection documenting one or more FDA Food Code Item 1–9 critical control point violations as a conforming inspection not meeting critical violation flagging criteria when the actual inspection photograph evidences a priority food safety violation with immediate closure or corrective action requirement — can suppress a critical food safety violation indicator that would otherwise generate a restaurant closure recommendation, a corrective action requirement notification, and a food safety compliance enforcement record. In restaurant chain food safety compliance operations where AI processes thousands of restaurant location inspection photographs per compliance cycle without individual human food safety manager pixel-level examination of every AI-processed inspection photograph before the AI classification governs the restaurant food safety compliance tier and delivery platform partnership eligibility, adversarial suppression of FDA Food Code critical violation indicators creates food safety enforcement and public health protection dimensions.

The FDA Food Code, FSMA, OSHA, and state health department enforcement consequences of adversarially suppressed critical food safety violation classification in restaurant inspection AI span FDA Food Code Items 1 through 9 foodborne illness risk factor and intervention violation categories applicable to retail food establishment inspection and enforcement, FDA Food Safety Modernization Act (FSMA) 21 USC §350g Hazard Analysis and Risk-Based Preventive Controls (HARPC) requirements for food facilities with commercial kitchen operations, OSHA 29 CFR §1910.141 sanitation standards applicable to restaurant employee workplace safety, state and local health department restaurant inspection and food safety code enforcement authority including mandatory closure orders, permit suspension, and civil penalty authority, and FDA 21 USC §331 prohibited acts applicable to adulterated food products arising from food safety control failures. The FDA Food Code (most recent edition adopted by reference in state and local food codes) identifies nine categories of risk factors and interventions representing the most significant contributors to foodborne illness at retail food establishments: Items 1–9 include Employee Health, Good Hygienic Practices, Preventing Contamination by Hands, Approved Source, Protection from Contamination, Proper Cooking Temperatures, Proper Reheating, Proper Cooling, and Proper Cold Holding — adversarial manipulation of restaurant inspection AI that suppresses one or more Food Code Item 1–9 critical violation indicators creates restaurant food safety enforcement dimensions when adversarially corrupted AI classifications govern restaurant compliance tier assessments and health department enforcement referrals. FSMA 21 USC §350g requires food facilities to conduct hazard analysis, implement preventive controls, monitor preventive control effectiveness, and take corrective actions; adversarially suppressed Food Code critical violation indicators in restaurant inspection AI create HARPC corrective action failure dimensions when adversarially corrupted AI compliance assessments prevent implementation of required preventive control corrections. Threshold: 60 for restaurant food safety inspection AI — reflecting FDA Food Code Items 1–9 critical violation enforcement, FSMA §350g HARPC corrective action obligations, OSHA §1910.141 sanitation standards, state health department enforcement authority, and FDA 21 USC §331 adulterated food product dimensions.

2. Food delivery order allergen and quality image injection (DoorDash AI, Uber Eats AI, Olo AI)

Food delivery order allergen and quality AI processes DoorDash Dasher delivery bag photograph images, Uber Eats order fulfilment and food presentation quality photographs, Olo order management and delivery confirmation images, Grubhub order fulfilment and food quality photographs, Toast kitchen display system and order preparation photographs, food allergen cross-contact and cross-contamination evidence images, ingredient substitution and menu item modification documentation images, and food presentation quality and order completeness verification photograph images from DoorDash AI at 35 million+ customer and 700,000+ restaurant partner delivery operations processing delivery fulfilment and food quality photographs through AI-assisted order accuracy, food quality, and allergen safety verification tools; Uber Eats AI processing 900 million+ annual delivery orders through AI-assisted order fulfilment quality, food presentation verification, and restaurant food safety compliance monitoring tools; Olo AI at 600+ restaurant brand and 85 million+ active diner commerce platform operations processing order management and delivery coordination image evidence through AI-assisted order quality and fulfilment accuracy classification tools; and food delivery platform quality assurance AI operations at Grubhub and Caviar processing restaurant order fulfilment images through AI-assisted order quality, food presentation, and allergen safety verification tools — extracting food allergen safety and order quality classifications from order fulfilment food presentation photograph and delivery bag image inputs in AI-assisted multi-platform food delivery order quality assurance and allergen safety verification pipelines.

The adversarial injection surface is the DoorDash delivery bag photograph, Uber Eats order fulfilment image, or Olo order management confirmation image submission pathway: DoorDash AI or Uber Eats AI order fulfilment photograph images submitted through AI-assisted food quality and allergen safety verification tools for AI delivery order quality record generation and restaurant food safety compliance monitoring input. An adversarially crafted food delivery order fulfilment photograph or food presentation image — in which pixel perturbations applied to the allergen-containing ingredient presence indicator display region, the peanut/tree nut/gluten/shellfish substitution visual marker, or the food allergen cross-contact evidence display in a food delivery order fulfilment image cause the AI to verify a delivered meal containing a major food allergen as allergen-free and meeting the customer’s disclosed dietary restriction requirements when the actual delivery photograph evidences the presence of a major food allergen ingredient that the customer’s order specifically excluded — can suppress an allergen presence indicator that would otherwise generate a customer allergy alert, a restaurant partner non-conformance notification, and an order allergen safety failure record. In food delivery platform operations where DoorDash AI or Uber Eats AI processes millions of order fulfilment photographs per day without individual human food safety reviewer pixel-level examination of every AI-processed delivery photograph before the AI allergen safety classification governs the order accuracy and allergen compliance determination, adversarial suppression of allergen presence indicators creates FALCPA food allergen injury civil liability and state product liability dimensions.

The FALCPA, FDA FSMA, state product liability, and ADA consequences of adversarially suppressed allergen indicator classification in food delivery order AI span Food Allergen Labeling and Consumer Protection Act (FALCPA) 21 USC §343(w) requirements for major food allergen declaration applicable to food products commercially delivered through food delivery platforms, FDA Food Safety Modernization Act allergen control provisions requiring preventive controls for undeclared allergens at food facilities supplying restaurant delivery platforms, state product liability tort law applicable to food allergen personal injury claims including negligence per se under FALCPA, breach of implied warranty of merchantability for food unfit for human consumption, and strict products liability for defective food products causing allergen injury, and ADA 42 USC §12182 public accommodation allergen accommodation obligations applicable to restaurant food service operations and food delivery platforms serving customers with food allergies and dietary restriction medical conditions. FALCPA 21 USC §343(w) requires that food containing a major food allergen (milk, egg, fish, shellfish, tree nuts, wheat, peanuts, soybeans, and sesame) declare the major food allergen in the product label or labeling; food delivered through DoorDash, Uber Eats, or Olo platforms that contains a major food allergen ingredient not disclosed in the restaurant’s delivery platform menu listing creates FALCPA undeclared allergen food safety warning and voluntary recall exposure when major food allergen cross-contact creates undeclared allergen conditions. State product liability law imposes strict liability on sellers and distributors of food products that are unreasonably dangerous due to manufacturing defects, design defects, or warning defects; a food delivery platform that processes order fulfilment photographs through AI allergen safety verification tools and whose adversarially corrupted AI allergen classification fails to detect a major food allergen in a delivered meal causing an anaphylactic injury creates state product liability negligence and strict liability exposure for the food delivery platform and the restaurant partner. Threshold: 65 for food delivery order allergen and quality AI — reflecting FALCPA 21 USC §343(w) undeclared allergen food safety, FDA FSMA allergen preventive control obligations, state product liability allergen injury civil liability, and ADA §12182 food service allergen accommodation dimensions.

3. Alcohol delivery age verification document image injection (state ABC statutes, Dram Shop Act)

Alcohol delivery age verification AI processes state driver’s licence and state ID card document scan images, facial comparison and biometric identity match display images, IntelliCheck or Intellicheck DL Authenticate barcode and chip verification display images, DoorDash Alcohol Experience and Uber Eats alcohol delivery age gate document verification images, Vivid Seats and Drizly alcohol delivery age verification record images, state Real ID Act compliant identification document photograph images, and passport and federal identification document scan images for alcohol service age verification from IntelliCheck AI at 5,000+ customer implementations providing AI-assisted identification document authentication and age verification services across retail alcohol, restaurant, and food delivery platform operations processing patron identification document photographs through AI-assisted age determination and document authenticity classification tools; DoorDash alcohol delivery operations and Uber Eats alcohol delivery operations requiring Dasher delivery agent compliance with state ABC statutes through AI-assisted patron age verification systems processing state ID document scan and facial comparison images at alcohol delivery order fulfilment; and restaurant point-of-sale AI platforms including Toast AI, Square for Restaurants AI, and Lightspeed Restaurant AI processing patron identification document images through AI-assisted on-premise alcohol service age verification tools at restaurant and bar operations — extracting patron age compliance and identification document authenticity classifications from state ID document scan and facial comparison image inputs in AI-assisted alcohol service age verification compliance pipelines at delivery and on-premise alcohol service volumes that make individual human bartender and delivery agent pixel-level examination of every AI-processed identification document impracticable for high-volume alcohol service operations.

The adversarial injection surface is the state ID or driver’s licence document scan image or facial comparison verification display image submission pathway: IntelliCheck AI or DoorDash alcohol delivery AI age verification document scan images submitted through AI-assisted age determination and identification document authenticity classification tools for AI alcohol service age compliance record generation and alcohol delivery order fulfilment authorisation input. An adversarially crafted state ID document scan image — in which pixel perturbations applied to the date-of-birth indicator display region, the under-21 identification marker visual element, or the identification document security feature authenticity display in a patron identification document photograph cause the AI to classify a minor’s identification document as an adult identification document meeting minimum alcohol service age requirements when the actual ID document evidences a date-of-birth creating underage status under the applicable state ABC statute — can suppress a patron age underage indicator that would otherwise generate an alcohol service refusal determination, a delivery order rejection, and an ABC compliance record. In alcohol delivery and on-premise service operations where AI processes thousands of patron identification document images per day without individual human agent pixel-level examination of every AI-processed ID image before the AI age classification governs the alcohol service or delivery authorisation, adversarial suppression of underage indicators creates Dram Shop Act civil liability and state ABC criminal prosecution dimensions.

The state ABC statute, Dram Shop Act, 18 USC §1028, and restaurant licensing consequences of adversarially suppressed underage indicator classification in alcohol service age verification AI span state Alcoholic Beverage Control (ABC) statutes in all 50 US states prohibiting the sale or service of alcoholic beverages to persons under 21 years of age, with criminal misdemeanour and felony sanctions applicable to establishments and delivery agents that serve alcohol to minors, Dram Shop Act civil liability applicable in approximately 40 US states making alcohol service establishments civilly liable for injuries caused by persons to whom the establishment sold or served alcohol in violation of state ABC statutes including service to minors, 18 USC §1028 identification document fraud applicable to the use of fraudulent identification documents in state ID-required transactions including alcohol purchase, and state ABC licence revocation and civil penalty authority applicable to repeated or wilful ABC statute violations at licensed food service establishments and alcohol delivery services. State ABC statutes typically impose strict liability on establishments that serve alcohol to persons under 21 regardless of whether the establishment had actual knowledge of the patron’s age; adversarial manipulation of AI age verification that suppresses an underage indicator and authorises alcohol service or delivery to a minor creates state ABC strict liability dimensions irrespective of whether the restaurant operator or delivery agent had actual knowledge of the falsified AI verification. Dram Shop Act civil liability in states including California (Business & Professions Code §25602.1), Texas (Tex. Alc. Bev. Code §2.02), and New York (Dram Shop Act §11-101) imposes liability on establishments that provide alcohol to visibly intoxicated persons or minors who subsequently cause personal injury or property damage; adversarially corrupted AI age verification that authorises alcohol service or delivery to a minor creates Dram Shop Act third-party injury civil liability exposure for the restaurant, delivery platform, and delivery agent. Threshold: 55 for alcohol service age verification document AI — reflecting state ABC statute minor service prohibition, Dram Shop Act third-party injury civil liability, 18 USC §1028 identification document fraud, and state ABC licence revocation and criminal penalty dimensions.

4. Dark kitchen and ghost kitchen production image injection (FSMA §350g, FDA Food Code)

Dark kitchen and ghost kitchen production AI processes kitchen preparation station cross-contamination monitoring photograph images, food allergen zone separation and dedicated utensil compliance photograph evidence images, temperature danger zone time-temperature abuse monitoring display images, food storage and FIFO rotation compliance photograph evidence images, cleaning and sanitising chemical concentration and contact time compliance photographs, pest and rodent exclusion programme documentation images, food employee illness exclusion and personal hygiene compliance monitoring photographs, and multi-brand production line allergen segregation verification photographs from Olo AI, Toast AI, and restaurant technology platform AI at ghost kitchen and dark kitchen multi-brand production operations processing kitchen production photograph evidence through AI-assisted food safety compliance and allergen control verification tools; CloudKitchens, Kitchen United, and Ghost Kitchen Brands AI operations processing commercial kitchen production and food safety compliance images through AI-assisted multi-brand kitchen food safety monitoring tools; and food delivery platform ghost kitchen programme AI operations at DoorDash Kitchens, Uber Eats Virtual Restaurant programme, and Reef Technology AI at shared commercial kitchen facilities processing production photograph evidence and food safety compliance documentation images through AI-assisted kitchen quality and food safety classification tools — extracting food safety and allergen control compliance classifications from dark kitchen and ghost kitchen production photograph and compliance documentation image inputs in AI-assisted multi-brand commercial kitchen food safety monitoring pipelines.

The adversarial injection surface is the kitchen preparation station cross-contamination monitoring photograph or food allergen zone separation compliance image submission pathway: CloudKitchens AI or Ghost Kitchen Brands AI dark kitchen production photograph images submitted through AI-assisted food safety and allergen control compliance verification tools for AI dark kitchen food safety record generation and FSMA preventive control compliance monitoring input. An adversarially crafted dark kitchen production photograph — in which pixel perturbations applied to the food allergen cross-contact indicator display region, the dedicated allergen-free utensil and preparation surface compliance visual marker, or the multi-brand production line allergen contamination evidence display in a dark kitchen production photograph cause the AI to classify a kitchen production photograph documenting food allergen cross-contact or cross-contamination with a FSMA preventive control non-conformance as a compliant kitchen production record meeting allergen control and food safety preventive control requirements when the actual production photograph evidences a food allergen cross-contact or cross-contamination incident with FALCPA undeclared allergen and FSMA corrective action consequences — can suppress an allergen cross-contact or cross-contamination indicator that would otherwise generate an allergen preventive control corrective action requirement, a batch hold and allergen investigation initiation, and an FSMA HARPC corrective action record. In ghost kitchen and dark kitchen multi-brand production operations where AI processes hundreds of kitchen production photographs per operating day across multiple simultaneous brand production lines without individual human food safety manager allergen control pixel-level examination of every AI-processed kitchen photograph before the AI classification governs the FSMA preventive control compliance determination, adversarial suppression of allergen cross-contact indicators creates FSMA §350g HARPC corrective action failure and FALCPA undeclared allergen food safety dimensions.

The FSMA §350g, FALCPA, FDA 21 USC §331, and state health department consequences of adversarially suppressed allergen cross-contact and cross-contamination classification in dark kitchen production AI span FSMA 21 USC §350g Hazard Analysis and Risk-Based Preventive Controls requirements including allergen preventive controls for facilities with known allergen cross-contact hazards in multi-brand commercial kitchen environments, FALCPA 21 USC §343(w) undeclared major food allergen consequences when dark kitchen cross-contact creates allergen-containing delivered food products not declared in the delivery platform menu listing, FDA 21 USC §331 adulterated food prohibited acts applicable to food products adulterated through cross-contamination or sanitation failure in commercial kitchen operations, FDA voluntary recall and warning letter authority under the Federal Food, Drug, and Cosmetic Act applicable to FSMA-covered food facilities with allergen preventive control failures, and state health department enforcement authority including permit suspension, mandatory recall, and civil penalty applicable to food safety violations at ghost kitchen and dark kitchen commercial kitchen facilities. FSMA §350g(a) requires every owner, operator, or agent in charge of a food facility to evaluate the known or reasonably foreseeable hazards that may be associated with the food manufactured, processed, packed, or held at that facility; allergen cross-contact and cross-contamination in multi-brand dark kitchen operations producing allergen-containing and allergen-free menu items on shared production lines represent known FSMA §350g allergen hazards requiring preventive controls — adversarial manipulation of dark kitchen production AI that suppresses allergen cross-contact evidence creates FSMA §350g(b) corrective action failure and §350g(c) verification failure dimensions. FDA has issued Form 483 inspection observations and Warning Letters to food facilities for FSMA allergen preventive control failures including inadequate allergen cross-contact hazard identification, inadequate allergen preventive control monitoring procedures, and inadequate corrective action procedures — adversarially corrupted dark kitchen production AI classifications that suppress allergen cross-contact indicators create FSMA enforcement vulnerability equivalent to documented allergen preventive control procedure deficiencies. Threshold: 60 for dark kitchen production AI — reflecting FSMA 21 USC §350g HARPC allergen preventive control obligations, FALCPA undeclared allergen food safety, FDA 21 USC §331 adulterated food, FDA Warning Letter and voluntary recall authority, and state health department enforcement dimensions.

Integration: restaurant and food delivery AI image ingestion with Glyphward pre-scan

Restaurant and food delivery AI image ingestion flows from restaurant chain and health department food safety inspection photograph channels, DoorDash AI and Uber Eats AI order fulfilment and food allergen safety image interfaces, IntelliCheck AI and Olo AI alcohol service age verification document processing systems, and CloudKitchens AI and Ghost Kitchen Brands AI dark kitchen production cross-contamination monitoring platforms into food safety critical violation classification AI, order allergen and food quality verification AI, patron age compliance and ID authenticity classification AI, and kitchen production allergen control compliance AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to restaurant food safety compliance records, food delivery allergen safety determinations, alcohol service age verification authorisations, or dark kitchen FSMA preventive control compliance 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"

# Restaurant & food delivery AI — FDA Food Code Items 1-9 critical violations;
# FSMA 21 USC §350g HARPC preventive controls; FALCPA 21 USC §343(w) allergen labeling;
# Dram Shop Act civil liability; state ABC statutes; FDA 21 USC §331 adulterated food.
THRESHOLD_FOOD_SAFETY_INSPECTION_AI   = 60  # FDA Food Code Items 1-9; FSMA §350g
THRESHOLD_ORDER_ALLERGEN_QUALITY_AI   = 65  # FALCPA §343(w); state product liability
THRESHOLD_AGE_VERIFICATION_DOC_AI     = 55  # state ABC statutes; Dram Shop Act; 18 USC §1028
THRESHOLD_DARK_KITCHEN_PRODUCTION_AI  = 60  # FSMA §350g HARPC; FALCPA; FDA §331


class RestaurantFoodDeliveryAIContext(str, Enum):
    FOOD_SAFETY_INSPECTION_AI   = "food_safety_inspection_ai"    # restaurant/health dept inspection
    ORDER_ALLERGEN_QUALITY_AI   = "order_allergen_quality_ai"    # DoorDash, Uber Eats, Olo delivery
    AGE_VERIFICATION_DOC_AI     = "age_verification_doc_ai"      # IntelliCheck, ABC compliance
    DARK_KITCHEN_PRODUCTION_AI  = "dark_kitchen_production_ai"   # CloudKitchens, Ghost Kitchen Brands


def threshold_for(context: RestaurantFoodDeliveryAIContext) -> int:
    mapping = {
        RestaurantFoodDeliveryAIContext.FOOD_SAFETY_INSPECTION_AI:   THRESHOLD_FOOD_SAFETY_INSPECTION_AI,
        RestaurantFoodDeliveryAIContext.ORDER_ALLERGEN_QUALITY_AI:   THRESHOLD_ORDER_ALLERGEN_QUALITY_AI,
        RestaurantFoodDeliveryAIContext.AGE_VERIFICATION_DOC_AI:     THRESHOLD_AGE_VERIFICATION_DOC_AI,
        RestaurantFoodDeliveryAIContext.DARK_KITCHEN_PRODUCTION_AI:  THRESHOLD_DARK_KITCHEN_PRODUCTION_AI,
    }
    return mapping[context]


async def scan_restaurant_food_delivery_ai_image(
    image_path: str | Path,
    context: RestaurantFoodDeliveryAIContext,
    establishment_hash: str,    # SHA-256 of restaurant permit number, order ID, or facility ID
    compliance_ref: str,        # e.g. "INSP-2026-NYC-44821", "ORDER-DD-2026-88441", "LOC-TOS-2026-331"
    service_session_id: str,    # inspection session ID, delivery order batch ID, or shift ID
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan a restaurant or food delivery AI image for adversarial injection payloads
    before forwarding to food safety inspection violation classification, order
    allergen safety verification, alcohol service age verification, or dark kitchen
    production cross-contamination monitoring AI systems.

    Raises AdversarialRestaurantFoodDeliveryAIImageError if score meets threshold:
      - FOOD_SAFETY_INSPECTION_AI:   threshold 60; FDA Food Code Items 1-9; FSMA §350g
      - ORDER_ALLERGEN_QUALITY_AI:   threshold 65; FALCPA §343(w); state product liability
      - AGE_VERIFICATION_DOC_AI:     threshold 55; state ABC statutes; Dram Shop Act
      - DARK_KITCHEN_PRODUCTION_AI:  threshold 60; FSMA §350g HARPC; FALCPA allergen
    """
    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": {
                "restaurant_food_delivery_context": context.value,
                "establishment_hash":               establishment_hash,
                "compliance_ref":                   compliance_ref,
                "service_session_id":               service_session_id,
                "client_scan_id":                   client_scan_id,
                "image_sha256":                     image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "establishment_hash":               establishment_hash,
        "compliance_ref":                   compliance_ref,
        "service_session_id":               service_session_id,
        "restaurant_food_delivery_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_food_safety_audit_record(audit_record)

    if result["score"] >= threshold:
        raise AdversarialRestaurantFoodDeliveryAIImageError(
            f"Restaurant food delivery AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"establishment={establishment_hash} ref={compliance_ref}"
        )
    return result


async def write_food_safety_audit_record(record: dict) -> None:
    """Persist audit record to restaurant food safety compliance documentation store (stub)."""
    import json, sys
    print(json.dumps(record), file=sys.stderr)


class AdversarialRestaurantFoodDeliveryAIImageError(Exception):
    """Raised when a restaurant or food delivery AI image exceeds the adversarial injection threshold."""
    pass

Call scan_restaurant_food_delivery_ai_image() with RestaurantFoodDeliveryAIContext.FOOD_SAFETY_INSPECTION_AI before forwarding health department inspection photographs and food safety violation evidence images to FDA Food Code critical violation identification and restaurant food safety compliance tier classification AI — with compliance_ref linking the Glyphward scan to the inspection record for FDA Food Code Items 1–9 critical violation enforcement, FSMA §350g HARPC corrective action obligation, and state health department enforcement referral compliance documentation. Call with RestaurantFoodDeliveryAIContext.ORDER_ALLERGEN_QUALITY_AI for DoorDash AI, Uber Eats AI, or Olo AI order fulfilment photograph and food presentation quality images before allergen safety and order quality verification AI, with establishment_hash as the SHA-256 of the restaurant partner permit number for FALCPA §343(w) undeclared allergen food safety, state product liability allergen injury, and ADA §12182 allergen accommodation compliance audit trail. Call with RestaurantFoodDeliveryAIContext.AGE_VERIFICATION_DOC_AI for IntelliCheck AI, Toast AI, or DoorDash alcohol delivery age verification ID document scan and facial comparison images before patron age compliance classification AI, with service_session_id as the shift or delivery session identifier for state ABC statute minor service compliance, Dram Shop Act third-party liability, and 18 USC §1028 identification document fraud audit trail documentation. Call with RestaurantFoodDeliveryAIContext.DARK_KITCHEN_PRODUCTION_AI for CloudKitchens AI, Ghost Kitchen Brands AI, or Olo dark kitchen production cross-contamination and allergen zone separation compliance photographs before FSMA preventive control verification AI, with compliance_ref for FSMA §350g HARPC allergen preventive control corrective action, FALCPA undeclared allergen food safety, and FDA Warning Letter audit trail. Get early access

Coverage matrix

Frequently asked questions

Further reading

• Food and beverage safety AI prompt injection — related adversarial attack surface covering food manufacturing and supply chain safety AI with FDA FSMA, HACCP, and food facility inspection dimensions applicable to restaurant and food delivery contexts where commercial kitchen supply chain and food production safety intersect.
• Food processing and food safety AI prompt injection — related regulatory framework covering food manufacturing AI with cGMP, FSMA preventive controls, and FDA enforcement dimensions applicable to dark kitchen and ghost kitchen production AI contexts where commercial scale food preparation creates HARPC compliance obligations.
• Retail pharmacy AI prompt injection — related adversarial attack surface covering pharmacy retail AI with allergen, FDA, DEA, and state board of pharmacy dimensions applicable to food delivery and restaurant allergen contexts where over-the-counter allergen medication and food allergen management intersect in retail pharmacy and grocery delivery operations.
• Free tier — 10 scans/day, no card required — start scanning restaurant and food delivery AI images at development volumes before committing to a production plan.