Prompt injection in food processing and food safety AI

Four adversarial injection surfaces in food processing and food safety AI

1. Food product visual inspection photograph injection (TOMRA AI, Cognex AI, Wipotec AI)

Food product visual inspection AI processes high-speed conveyor belt product inspection images captured by TOMRA AI near-infrared and hyperspectral sorting cameras at more than 14,000 food processing installations, Cognex AI machine vision inspection systems at more than 250,000 food and beverage manufacturing installations, and Wipotec AI X-ray inspection and checkweigher systems at meat, poultry, and packaged food processing lines, extracting foreign object classifications — metal, glass, stone, bone fragment, plastic fragment indicators — contamination type identifications, product defect severity scores, fill weight accuracy assessments, and packaging integrity verification results from conveyor-belt product inspection image inputs, generating food product acceptance or rejection determinations, HACCP critical control point (CCP) log records, and FDA 21 CFR Part 110 cGMP compliance documentation that food processors depend upon for consumer protection, regulatory compliance, and product liability risk management at production line throughput rates that make individual human inspection impracticable for the product volumes processed. TOMRA AI’s near-infrared hyperspectral sorting cameras identify contaminating foreign objects — glass, metal, and plastic fragments — in fresh produce and nut processing streams by detecting spectral signatures that distinguish contaminant materials from food product materials in conveyor-belt inspection images; its AI sorting decisions govern which individual product units are rejected before packaging, with FDA cGMP compliance and FSMA Preventive Controls Rule CCP monitoring documentation obligations applying to the HACCP CCP log records that the TOMRA AI system generates. Cognex AI machine vision systems inspect food and beverage product containers, packaged food labels, fill levels, cap integrity, and product uniformity through AI-assisted image classification at production line speeds of hundreds to thousands of units per minute, with AI rejection decisions governing which product units are diverted from the packaging line before they enter the distribution and retail channel.

The adversarial injection surface is the conveyor belt product inspection image submission pathway: TOMRA AI hyperspectral sorting camera images and Cognex AI machine vision inspection images submitted through AI-assisted foreign object detection, contamination classification, and product defect severity scoring tools. An adversarially crafted TOMRA AI near-infrared inspection image — in which pixel perturbations applied to the glass fragment spectral signature region, the metal contamination reflectance indicator, or the bone fragment density contrast marker in a conveyor-belt inspection frame cause the AI to classify a contaminated or defective product unit as within-tolerance acceptable product when the actual image documents a foreign object inclusion or contamination indicator meeting the HACCP CCP rejection criteria — can suppress a product rejection determination that would otherwise divert the contaminated unit from the packaging line before it enters the human consumption stream. In high-throughput food processing environments where TOMRA AI or Cognex AI inspection systems process thousands of product units per minute without human operator review of each AI acceptance decision, adversarial suppression of foreign object detection classifications allows contaminated product units to proceed to packaging and distribution, with the suppressed contamination event producing no CCP log record that the food processor’s HACCP plan requires for FDA 21 CFR Part 110 cGMP compliance documentation and FSMA Preventive Controls Rule verification record obligations.

The regulatory and liability consequences of adversarially suppressed foreign object detection in food product visual inspection AI span FDA 21 CFR Part 110 cGMP, FSMA Preventive Controls Rule, product recall, and class action tort dimensions of exceptional severity. FDA 21 CFR Part 110 (Current Good Manufacturing Practice in Manufacturing, Packing, or Holding Human Food) requires food manufacturers to employ adequate controls to prevent food adulteration by foreign objects; adversarial manipulation of TOMRA AI or Cognex AI inspection tools that suppresses foreign object detection creates a 21 CFR Part 110 cGMP violation in which the manufacturer’s AI-assisted CCP monitoring system failed to prevent adulterated product from entering the food supply. FSMA 21 USC §2201 Preventive Controls for Human Food rule (21 CFR Part 117) requires food manufacturers to identify hazards, implement preventive controls to address identified hazards, and verify that preventive controls are operating effectively; adversarial manipulation of AI-assisted CCP monitoring that suppresses foreign object detection creates a Preventive Controls Rule verification failure that FDA district office inspectors identify through the mandatory verification record review during routine facility inspection. Food product recalls triggered by consumer foreign object injury reports or retailer detection of contaminated product — the most common outcome when adversarially suppressed inspection AI allows contaminated units to reach retail shelves — generate $25M+ class action tort exposure per major food manufacturer recall event, including product liability claims from injured consumers, recall execution costs including product retrieval and disposal, brand damage remediation costs, and retailer contractual indemnity obligations. Threshold: 50 for food product visual inspection AI — reflecting the FDA cGMP, FSMA Preventive Controls, recall liability, and consumer safety dimensions of suppressed foreign object and contamination detection.

2. Food safety temperature monitoring display injection (Squadle AI, Hazel Analytics AI)

Food safety temperature monitoring display AI processes photographs of digital temperature monitoring display screens, walk-in cooler and freezer temperature recorder printouts, and food safety checklist completion display images submitted through Squadle AI digital food safety compliance platforms at restaurant and food service chains, Hazel Analytics AI food establishment compliance monitoring tools used by food service operators and regulators, and integrated food safety management platform temperature monitoring interfaces that extract temperature reading values, TCS food critical limit adherence classifications, cold chain exceedance event identifications, and corrective action requirement flags from temperature monitoring image inputs, generating food safety compliance records, HACCP critical limit log entries, and FDA Food Code 2022 corrective action documentation that food service operators, food safety managers, and health department inspectors depend upon for TCS food time-temperature compliance monitoring with FDA Food Code 2022 critical violation consequences. Squadle AI deploys digital food safety monitoring at major restaurant chain and food service operator clients, with AI-assisted temperature log image processing replacing paper-based manual temperature recording in compliance with FDA Food Code 2022 HACCP requirements; its AI-generated temperature compliance records are used by food service operators for internal QA, health department inspection preparation, and franchise compliance documentation. Hazel Analytics AI processes health inspection data from restaurant and food service establishments for food safety compliance analysis, with AI-assisted inspection record processing tools used by retail food service operators, corporate food safety directors, and public health regulators for facility compliance scoring and risk-based inspection prioritisation.

The adversarial injection surface is the temperature monitoring display photograph submission pathway: walk-in cooler digital temperature display photographs, food safety checklist completion display images, and temperature recorder printout scan images submitted through Squadle AI or Hazel Analytics AI food safety compliance monitoring tools for AI temperature reading classification, TCS food critical limit adherence scoring, and cold chain exceedance event detection. An adversarially crafted digital temperature monitoring display photograph — in which pixel perturbations applied to the temperature digit display region, the critical limit alarm indicator, or the duration-above-threshold countdown display on a walk-in cooler digital temperature monitor photograph cause the AI to classify a TCS food storage environment with a temperature exceedance above the FDA Food Code 2022 41°F (5°C) critical limit for cold TCS food storage as within-temperature-range compliant standard storage conditions when the actual image documents a cold chain exceedance requiring TCS food disposition or corrective action — can suppress a critical limit exceedance flag that would otherwise generate a food safety corrective action record requiring TCS food disposition evaluation and a health department critical violation documentation entry. In food service chain environments where Squadle AI processes digital temperature monitoring photographs from hundreds of restaurant locations per day without individual food safety manager review of each AI-classified temperature reading, adversarial suppression of cold chain exceedance flags allows TCS food storage temperature violations to proceed without the corrective action and disposition records that the FDA Food Code 2022 HACCP plan requires.

The regulatory consequences of adversarially suppressed cold chain exceedance detection in food safety temperature monitoring AI span FDA Food Code 2022 critical violation, FSMA Preventive Controls Rule supplier verification, and food borne illness civil liability dimensions. FDA Food Code 2022 Section 3-501.16 establishes critical limits for cold holding of TCS foods at 41°F (5°C) or below; adversarial manipulation of temperature monitoring AI that suppresses cold chain exceedance detection creates a Food Code 2022 Section 3-501.16 critical violation that health department environmental health specialists identify through the temperature monitoring record review during routine facility inspection. FSMA 21 CFR §117.135 (Supply Chain Program Preventive Controls) requires food manufacturers and importers to verify that supplier controls for temperature-sensitive raw material ingredients are functioning effectively; adversarial manipulation of temperature monitoring AI in a supplier verification context creates an FSMA Supply Chain Program verification failure with FDA enforcement consequences at both the supplier and the receiving manufacturer. Foodborne illness outbreaks associated with TCS food cold chain failures — the public health consequence of adversarially suppressed temperature monitoring AI that allows TCS food stored above critical limits to proceed to service — generate civil negligence liability for the food service operator under food safety duty-of-care standards, with damages including medical treatment costs, lost income, pain and suffering, and punitive damages in cases where the operator’s food safety program is demonstrated to have deployed AI tools without adequate adversarial image injection controls. Threshold: 60 for food safety temperature monitoring display AI — reflecting the FDA Food Code critical limit, HACCP CCP log integrity, and foodborne illness liability dimensions of suppressed cold chain exceedance detection.

3. Meat and poultry carcass inspection photograph injection (Marel AI, USDA FSIS AI)

Meat and poultry carcass inspection AI processes post-slaughter carcass inspection photographs, evisceration line visual inspection images, and trim and rework inspection photographs submitted through Marel AI poultry and red meat processing systems at USDA FSIS-inspected slaughter establishments, USDA FSIS online carcass inspection program (OLAF and NELS-based systems) AI-assisted inspection tools at federally inspected poultry and red meat slaughter establishments, and integrated slaughter management platform AI inspection tools that extract carcass condition classifications — contamination indicators, bruising severity scores, airsacculitis identification, fecal contamination flags, systemic disease signs — from carcass inspection image inputs, generating USDA FSIS public health veterinarian pass/retain/condemn determinations, NELS (New Evisceration Livestock System) and NPIS (New Poultry Inspection System) inspection record entries, and HACCP 9 CFR Part 417 critical control point log records that USDA FSIS-inspected establishments depend upon for regulatory compliance with daily production. Marel AI is deployed in poultry, fish, and red meat processing at slaughter and processing establishments across more than 100 countries, with AI-assisted carcass grading, defect detection, and yield optimisation tools processing carcass inspection images in real time on the evisceration and processing line, with FSIS-delegated online inspection authority in USDA-inspected US establishments under USDA FSIS’s NELS and NPIS inspection systems. USDA FSIS AI tools process carcass inspection photographs in FSIS-inspected slaughter establishments as part of the agency’s modernised inspection systems, with AI-assisted pre-screen classification tools identifying carcasses with visible contamination, pathological conditions, or systemic disease indicators for USDA public health veterinarian (PHV) detailed examination before disposition determination.

The adversarial injection surface is the carcass inspection photograph submission pathway: post-slaughter carcass inspection images, evisceration line photographs, and trim inspection images submitted through Marel AI processing line inspection tools or USDA FSIS AI-assisted pre-screen classification tools for AI carcass condition classification, contamination flag generation, and USDA PHV retain/condemn indicator scoring. An adversarially crafted carcass inspection photograph — in which pixel perturbations applied to the fecal contamination visual indicator, the airsacculitis lesion visual marker, or the bruising severity gradient in a post-slaughter poultry carcass inspection image cause the AI to classify a contaminated or pathologically-affected carcass as within-standard USDA Grade A or USDA acceptable condition when the actual image documents a condition meeting USDA FSIS 9 CFR Part 381 retain criteria for PHV detailed examination — can suppress a USDA retain indicator that would otherwise route the affected carcass to the PHV examination station, allowing a potentially adulterated or pathological carcass to proceed to further processing and packaging without the FSIS-required PHV disposition determination. In high-throughput USDA FSIS-inspected poultry slaughter establishments where Marel AI processes carcass inspection images at line speeds of hundreds of birds per minute, adversarial suppression of fecal contamination or pathological condition classifications across a production run allows the suppressed conditions to produce no HACCP CCP log records that the establishment’s HACCP plan requires for FSIS 9 CFR Part 417 compliance.

The regulatory and criminal consequences of adversarially suppressed carcass defect detection in meat and poultry inspection AI span USDA FSIS 9 CFR Part 310/381 inspection violation, HACCP 9 CFR Part 417 critical control point failure, FSIS PHV certification fraud, and food safety criminal liability dimensions. FSIS 9 CFR Part 381 (Poultry Products Inspection Regulations) and 9 CFR Part 310 (Post-Mortem Inspection) establish USDA inspection requirements for post-slaughter carcass condition assessment and PHV disposition authority; adversarial manipulation of AI-assisted FSIS pre-screen inspection tools that suppresses a retain indicator and allows a pathological or contaminated carcass to bypass PHV examination creates a 9 CFR Part 310/381 inspection circumvention with FSIS enforcement consequences including product hold and recall authority, establishment suspension authority under 21 USC §675, and criminal referral under 21 USC §676 for knowing and wilful violation of the Federal Meat Inspection Act or Poultry Products Inspection Act. USDA FSIS HACCP regulations (9 CFR Part 417) require slaughter establishments to implement HACCP plans with CCPs at critical inspection points; adversarial suppression of CCP log records by manipulated AI inspection tools creates a 9 CFR Part 417 HACCP violation that FSIS public health veterinarians identify through the daily HACCP record verification duty. Federal criminal liability under 21 USC §676 (Federal Meat Inspection Act criminal penalty) and 21 USC §461 (Poultry Products Inspection Act criminal penalty) applies to knowing and wilful circumvention of FSIS inspection requirements; adversarial manipulation of AI-assisted inspection tools to suppress USDA retain indicators constitutes a federal criminal violation with felony exposure for the responsible actor. Threshold: 50 for meat and poultry carcass inspection AI — reflecting the FSIS 9 CFR Part 310/381 inspection circumvention, HACCP CCP log integrity, and federal criminal liability dimensions of suppressed carcass defect and contamination detection.

4. Food label compliance photograph injection (Antares Vision AI, Cognex AI)

Food label compliance AI processes food product packaging label photographs, Nutrition Facts Panel scan images, ingredient list label inspection images, and allergen declaration label verification photographs submitted through Antares Vision AI food traceability and serialisation systems, Cognex AI machine vision label inspection tools, and integrated food label compliance verification platforms that extract Nutrition Facts Panel accuracy classifications — serving size, caloric content, nutrient quantity, daily value percentage indicators — allergen declaration completeness scores, ingredient list accuracy assessments, net weight statement compliance verifications, and FDA 21 CFR Part 101 food labelling regulatory compliance confirmations from food product label image inputs, generating label compliance records, FDA labelling compliance certifications, and HACCP preventive control verification entries that food manufacturers, co-packers, and private-label retailers depend upon for FDA 21 CFR Part 101 food labelling compliance and FSMA Preventive Controls Rule labelling accuracy preventive control verification documentation. Antares Vision AI deploys food and pharmaceutical product traceability at food manufacturers across Europe, Latin America, and Asia, with AI-assisted label verification tools processing food product packaging label images for serialisation code reading, label print accuracy verification, and regulatory labelling compliance confirmation in high-speed packaging line environments. Cognex AI label inspection tools process food and beverage product label placement accuracy, label print quality, and label content compliance through AI-assisted machine vision inspection at production line speeds, with AI acceptance or rejection determinations governing which labeled product units are diverted from the packaging line for label correction before they enter the distribution channel.

The adversarial injection surface is the food product label compliance photograph submission pathway: food packaging label images, Nutrition Facts Panel photographs, and allergen declaration label scan images submitted through Antares Vision AI label verification tools or Cognex AI label inspection systems for AI Nutrition Facts Panel accuracy classification, allergen declaration completeness scoring, and FDA 21 CFR Part 101 compliance assessment. An adversarially crafted Nutrition Facts Panel label photograph — in which pixel perturbations applied to the serving size numerical display region, the total fat or sodium quantity value indicator, or the allergen declaration bold-font warning field in a Nutrition Facts Panel label image cause the AI to classify a food label with a material Nutrition Facts Panel inaccuracy or missing allergen declaration as a compliant FDA 21 CFR Part 101-conformant label when the actual image documents a Nutrition Facts discrepancy or allergen omission meeting the FDA labelling misbranding threshold — can suppress a labelling non-compliance flag that would otherwise generate a label correction work order and FSMA Preventive Controls preventive control non-conformance record. In high-speed packaging environments where Cognex AI label inspection tools process thousands of label images per minute without human labelling compliance review of each AI acceptance decision, adversarial suppression of Nutrition Facts Panel inaccuracy classifications allows mislabelled product units to proceed to packaging and distribution, with the suppressed non-conformance event producing no FSMA Preventive Controls preventive control CCP record that the manufacturer’s food safety plan requires.

The regulatory and enforcement consequences of adversarially suppressed food label compliance detection span FDA 21 CFR Part 101 misbranding, FSMA Preventive Controls labelling accuracy, FTC dietary supplement claims, and allergen-triggered product recall dimensions. FDA 21 CFR Part 101 establishes mandatory food labelling requirements including Nutrition Facts Panel content and format, allergen declaration requirements under the Food Allergen Labelling and Consumer Protection Act (FALCPA) of 2004, net weight statement accuracy, and ingredient list disclosure; adversarial manipulation of label compliance AI that suppresses a Nutrition Facts Panel inaccuracy or allergen declaration omission flag creates an FDA 21 CFR Part 101 misbranding violation with FDA district office warning letter, mandatory recall, or injunction authority consequences under 21 USC §334 (seizure) and 21 USC §332 (injunction). FALCPA allergen declaration omission — the most severe class of adversarially suppressed food label deficiency — creates immediate product recall risk when undeclared allergens such as peanuts, tree nuts, wheat, milk, eggs, fish, shellfish, or soybeans are present in a food product without required labelling disclosure; FDA Class I recalls for undeclared allergens have triggered multi-million-dollar product recall costs, tort liability for anaphylactic injury and death, and criminal prosecution under 21 USC §333 (Federal Food, Drug, and Cosmetic Act criminal penalty) for knowing and wilful distribution of misbranded food. FSMA 21 CFR Part 117 Preventive Controls Rule requires food manufacturers to implement labelling accuracy as a preventive control for allergen management hazards; adversarial suppression of allergen declaration verification AI creates a Preventive Controls Rule preventive control failure with FDA inspector enforcement consequences during routine FSMA facility inspection. Threshold: 60 for food label compliance AI — reflecting the FDA 21 CFR Part 101 misbranding, FALCPA allergen declaration, FSMA Preventive Controls, and recall liability dimensions of suppressed labelling non-compliance detection.

Integration: food processing and food safety AI image ingestion with Glyphward pre-scan

Food processing and food safety AI image ingestion flows from TOMRA and Cognex conveyor belt product inspection camera APIs, Squadle and Hazel Analytics temperature monitoring display photograph channels, Marel and USDA FSIS carcass inspection photograph interfaces, and Antares Vision and Cognex food label inspection platforms into foreign object detection AI, cold chain compliance monitoring AI, USDA carcass inspection AI, and food label accuracy verification AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to HACCP CCP log records, food safety compliance certifications, USDA FSIS inspection records, or FDA labelling compliance verifications:

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"

# Food processing & food safety AI — FDA 21 CFR Part 110/101; FSMA
# 21 CFR Part 117; FSIS 9 CFR Part 310/417/381; FDA Food Code 2022.
# Suppression of foreign object detection, cold chain exceedances,
# carcass defects, and label non-compliance create recall liability,
# HACCP CCP failures, and federal criminal enforcement consequences.
THRESHOLD_PRODUCT_INSPECT = 50  # TOMRA/Cognex; FDA cGMP; recall liability
THRESHOLD_CARCASS_INSPECT = 50  # Marel/FSIS; 9 CFR Part 310/381; HACCP
THRESHOLD_FOOD_SAFETY_AI  = 60  # temp monitoring, label compliance


class FoodSafetyAIContext(str, Enum):
    PRODUCT_VISUAL_INSPECT = "product_visual_inspect"   # TOMRA, Cognex, Wipotec
    TEMPERATURE_DISPLAY    = "temperature_display"      # Squadle, Hazel Analytics
    CARCASS_INSPECT        = "carcass_inspect"          # Marel, USDA FSIS
    LABEL_COMPLIANCE       = "label_compliance"         # Antares Vision, Cognex


def threshold_for(context: FoodSafetyAIContext) -> int:
    if context in (FoodSafetyAIContext.PRODUCT_VISUAL_INSPECT,
                   FoodSafetyAIContext.CARCASS_INSPECT):
        return THRESHOLD_PRODUCT_INSPECT
    return THRESHOLD_FOOD_SAFETY_AI


async def scan_food_safety_ai_image(
    image_path: str | Path,
    context: FoodSafetyAIContext,
    facility_id_hash: str,   # SHA-256 of FDA facility registration or FSIS est. no.
    lot_code: str,           # production lot or batch code
    inspection_id: str,      # HACCP CCP log entry ID, inspection session ID, label scan ID
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan a food processing or food safety AI image for adversarial injection
    payloads before forwarding to foreign object detection, cold chain
    compliance monitoring, USDA carcass inspection, or food label accuracy
    verification AI systems.

    Raises AdversarialFoodSafetyAIImageError if score meets threshold:
      - PRODUCT_VISUAL_INSPECT: threshold 50; FDA 21 CFR Part 110 cGMP;
                                 FSMA Preventive Controls CCP; recall liability
      - TEMPERATURE_DISPLAY:    threshold 60; FDA Food Code 2022 Sec 3-501.16;
                                 HACCP critical limit; foodborne illness liability
      - CARCASS_INSPECT:        threshold 50; FSIS 9 CFR Part 310/381;
                                 HACCP 9 CFR Part 417; federal criminal 21 USC §676
      - LABEL_COMPLIANCE:       threshold 60; FDA 21 CFR Part 101; FALCPA;
                                 FSMA Preventive Controls; allergen recall liability
    """
    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": {
                "food_safety_context": context.value,
                "facility_id_hash":   facility_id_hash,
                "lot_code":           lot_code,
                "inspection_id":      inspection_id,
                "client_scan_id":     client_scan_id,
                "image_sha256":       image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "facility_id_hash":   facility_id_hash,
        "lot_code":           lot_code,
        "inspection_id":      inspection_id,
        "food_safety_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 AdversarialFoodSafetyAIImageError(
            f"Food safety AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"facility={facility_id_hash} lot={lot_code}"
        )
    return result


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


class AdversarialFoodSafetyAIImageError(Exception):
    """Raised when a food processing or food safety AI image exceeds the adversarial injection threshold."""
    pass

Call scan_food_safety_ai_image() with FoodSafetyAIContext.PRODUCT_VISUAL_INSPECT before forwarding TOMRA or Cognex conveyor belt inspection images to AI foreign object detection and contamination classification tools — the integration point where adversarial suppression of a contamination indicator creates an FDA 21 CFR Part 110 cGMP violation and HACCP CCP log failure, with lot_code linking the Glyphward scan record to the specific production lot for FDA recall scope determination purposes. Call with FoodSafetyAIContext.TEMPERATURE_DISPLAY for Squadle or Hazel Analytics temperature monitoring display photographs before AI cold chain critical limit adherence classification, preserving inspection_id as the HACCP CCP log entry identifier for FDA Food Code 2022 Section 3-501.16 critical violation audit purposes. Call with FoodSafetyAIContext.CARCASS_INSPECT for Marel or USDA FSIS carcass inspection photographs before AI retain/condemn indicator classification and FSIS PHV examination routing, with facility_id_hash encoding the FSIS establishment number for 9 CFR Part 310/381 compliance audit documentation. Call with FoodSafetyAIContext.LABEL_COMPLIANCE for Antares Vision or Cognex food label inspection images before AI Nutrition Facts Panel accuracy and allergen declaration verification, with lot_code set to the production run batch identifier for FALCPA allergen recall scope determination and FSMA Preventive Controls preventive control verification audit trail purposes. Get early access

Coverage matrix

Frequently asked questions

Further reading

• Indirect prompt injection via image — foundational attack pattern underlying all four food processing and food safety AI injection surfaces; covers how adversarial pixel-level perturbations cause AI misclassification without detectable visual artefacts at human review resolution.
• Manufacturing quality inspection AI prompt injection — industrial AI injection context covering the broader class of production line inspection photograph manipulation applicable to food product visual inspection AI injection and carcass inspection adversarial attacks.
• Vision-language model security — technical architecture of adversarial image attacks against vision-language models including pixel perturbation classes applicable to food label compliance photograph injection and temperature monitoring display manipulation.
• Free tier — 10 scans/day, no card required — start scanning food processing and food safety AI images at development volumes before committing to a production plan.