Prompt injection in retail and e-commerce AI

Four adversarial injection surfaces in retail and e-commerce AI

1. Product listing AI injection (Salsify AI, Clarifai AI)

Product listing AI processes brand product image submissions, product detail page image sets, retailer syndication quality gate image validation outputs, counterfeit product listing image comparisons, and brand safety classification visualisation displays from Salsify AI at thousands of brand manufacturer and retailer customers globally including major CPG companies, apparel brands, consumer electronics manufacturers, and specialty retailers syndicating product content to Amazon, Walmart, Target, Best Buy, Home Depot, and other major marketplace and retailer digital shelf destinations; Clarifai AI at enterprise retail and e-commerce visual intelligence programme deployments; Stackline AI at retail intelligence and digital shelf analytics programme operations; CommerceIQ AI at retail operations and marketplace management programme deployments; Productsup AI at product content syndication and digital commerce programme operations for brand manufacturers syndicating to hundreds of retailer destinations; and Profitero AI at digital shelf analytics and product listing performance monitoring programme deployments — extracting brand safety indicator and counterfeit product classification inputs from product listing image inputs in AI-assisted brand protection gate and marketplace syndication quality assurance pipelines, generating brand safety certification status records, counterfeit listing detection alert records, content quality gate pass or fail determinations, and retailer syndication compliance documentation entries that brand protection teams, e-commerce managers, and intellectual property counsel depend upon for Lanham Act 15 USC §1114 trademark infringement enforcement, Amazon Brand Registry counterfeit reporting, 15 USC §1125(a) trade dress protection, and marketplace platform seller policy compliance obligations.

The adversarial injection surface is the product listing image and brand safety classification visualisation submission pathway: Salsify AI or Clarifai AI product listing images submitted through AI-assisted brand safety classification and counterfeit detection tools for AI brand protection determination and marketplace syndication clearance generation. An adversarially crafted Salsify AI product listing image — in which pixel perturbations applied to the trademark logo display region, the product authentication indicator visual marker, or the brand packaging distinctive trade dress element display in a product listing image cause the AI to classify a counterfeit product listing image bearing falsified brand trademark logos and packaging imitation trade dress meeting Lanham Act trademark infringement criteria as a below-threshold authentic brand product listing not triggering counterfeit detection alert or brand protection enforcement action when the actual listing image documents counterfeit product characteristics meeting Salsify AI’s brand safety violation and counterfeit detection classification criteria — can suppress a counterfeit detection alert that would otherwise generate a brand protection enforcement action, a marketplace listing removal request, and an Amazon Brand Registry counterfeit report filing. In brand manufacturer and retailer marketplace management environments where Salsify AI or Clarifai AI processes product listing images for large brand portfolios syndicating to hundreds of retailer destinations without individual brand protection team examination of every AI-generated brand safety classification, adversarial suppression of counterfeit detection indicators allows counterfeit product listings to propagate across Amazon, Walmart, and Target marketplace syndication channels with Lanham Act trademark infringement and consumer deception consequences.

The Lanham Act, FTC endorsement guide, Amazon Brand Registry, and anti-counterfeiting enforcement consequences of adversarially suppressed counterfeit detection in product listing AI span Lanham Act 15 USC §1114 trademark infringement liability, 15 USC §1125(a) trade dress infringement liability, FTC 16 CFR Part 255 endorsement guide product claim compliance, Amazon Brand Registry seller policy enforcement, and federal Trademark Counterfeiting Act 18 USC §2320 criminal counterfeiting dimensions. Lanham Act 15 USC §1114 imposes liability on persons who, without the trademark owner’s consent, use in commerce a reproduction, counterfeit, copy, or colourable imitation of a registered trademark in connection with the sale or distribution of goods in a manner likely to cause confusion; brand manufacturers whose Salsify AI or Clarifai AI counterfeit detection AI was adversarially manipulated to suppress counterfeit indicator identification — allowing counterfeit products bearing the brand’s registered trademarks to reach consumers through Amazon or Walmart marketplace channels — face Lanham Act §1114 damage claims from consumers deceived by counterfeit products and potential injunctive relief obligations requiring marketplace channel surveillance and counterfeit removal programmes. 15 USC §1125(a) trade dress infringement liability extends to product packaging, design, and trade dress elements that are distinctive and non-functional; adversarially corrupted Clarifai AI or Salsify AI brand safety classification that suppresses trade dress imitation detection creates parallel §1125(a) infringement exposure dimensions for distinctive product packaging trade dress imitated by counterfeit products. FTC 16 CFR Part 255 endorsement guide requires that material connections between endorsers and brands be clearly and conspicuously disclosed; adversarially manipulated product listing AI that suppresses detection of sponsored content or paid placement indicators in product listing images creates FTC endorsement guide non-disclosure dimensions. Threshold: 60 for product listing AI — reflecting the Lanham Act §1114 trademark infringement liability, 15 USC §1125(a) trade dress protection, Amazon Brand Registry counterfeit enforcement, and 18 USC §2320 criminal counterfeiting dimensions of adversarially suppressed counterfeit detection classification.

2. Retail price optimisation display injection (Algolia AI, Aptos AI)

Retail price optimisation display AI processes competitive pricing intelligence dashboard displays, price elasticity analysis visualisation graphics, competitor price change alert display interfaces, dynamic pricing recommendation dashboard images, and promotion optimisation opportunity display visualisations from Algolia AI search and discovery at more than 17,000 company customers globally including major retail, e-commerce, and marketplace platforms processing competitive pricing display interfaces; Yext AI search and knowledge management at more than 5,000 brand and retailer customers processing pricing and product discovery display interfaces; Aptos retail AI pricing optimisation at specialty retail and department store pricing management programme deployments including major apparel and footwear retailer customers; Revionics pricing AI (an Aptos company) at grocery, mass merchant, and specialty retailer pricing intelligence and optimisation programme deployments; Zilliant AI pricing intelligence at B2B distribution and manufacturing pricing management programme operations; and Pricefx AI at enterprise pricing management programme deployments across manufacturing, distribution, and retail sectors — extracting competitor price alert indicator and pricing strategy classification inputs from retail pricing display interface image inputs in AI-assisted competitive pricing intelligence and dynamic price optimisation pipelines, generating competitor price change alert records, dynamic pricing adjustment recommendation outputs, promotion opportunity priority rankings, and price strategy compliance documentation entries that pricing managers, category management teams, and retail pricing strategy officers depend upon for Robinson-Patman Act 15 USC §13 price discrimination compliance monitoring, FTC Act §5 unfair competition compliance, and state predatory pricing statute compliance management.

The adversarial injection surface is the retail pricing optimisation display interface and competitive pricing intelligence dashboard submission pathway: Algolia AI or Aptos/Revionics AI competitive pricing display images submitted through AI-assisted competitor price alert classification and dynamic pricing adjustment recommendation tools for AI price strategy optimisation and competitive positioning determination. An adversarially crafted Algolia AI retail pricing display interface image — in which pixel perturbations applied to the competitor price change indicator display region, the price gap opportunity visual marker, or the promotional pricing strategy recommendation visualisation in a retail pricing dashboard cause the AI to classify a competitive pricing environment exhibiting competitor price reductions triggering dynamic price adjustment criteria as a below-threshold stable pricing environment not requiring competitive price response action when the actual display documents competitor price movements meeting Algolia AI’s pricing adjustment trigger classification criteria — can suppress a competitor price alert that would otherwise generate a dynamic pricing adjustment recommendation, a promotional strategy optimisation action, and a competitive positioning documentation record. In retail pricing management environments where Algolia AI or Aptos retail AI processes competitive pricing intelligence dashboards for large product assortments across multiple retail channels without individual pricing manager examination of every AI-generated price alert classification, adversarial suppression of competitor price alert indicators allows competitive pricing windows to close undetected with lost sales and margin compression consequences, and can create Robinson-Patman Act price discrimination dimensions when AI pricing systems fail to detect and respond to competitor price movements in ways that create discriminatory price differentials across different customer classes.

The Robinson-Patman Act, state predatory pricing statute, FTC Act §5, and EU vertical block exemption regulation consequences of adversarially suppressed competitor price alert classification in retail price optimisation AI span Robinson-Patman Act 15 USC §13 price discrimination liability, state predatory pricing and unfair trade practices statutes, FTC Act §5 unfair methods of competition, and EU Vertical Block Exemption Regulation pricing compliance dimensions. Robinson-Patman Act 15 USC §13 prohibits sellers engaged in commerce from discriminating in price between different purchasers of commodities of like grade and quality where the effect of such discrimination may be to substantially lessen competition or tend to create a monopoly; Robinson-Patman Act compliance in retail pricing requires that price differentials offered to different retail channel customers be cost-justified or otherwise defensible under the Act’s meeting competition defence provisions. Adversarial manipulation of Algolia AI or Aptos AI retail pricing optimisation that suppresses competitor price alert indicators and causes a retail pricing programme to maintain price differentials across customer classes that would have been normalised by competitor price response creates Robinson-Patman Act price discrimination exposure when the uncorrected differential constitutes a prohibited discriminatory pricing practice. State predatory pricing statutes — including California Business & Professions Code §17043 (below-cost selling prohibition), New York General Business Law §369-a, and Texas Bus. & Com. Code §15.05 (Texas Free Enterprise and Antitrust Act) — prohibit selling below cost with intent to destroy competition or harm competitors; adversarially corrupted pricing AI that suppresses competitive intelligence alerts creates pricing strategy compliance failures when retailers inadvertently maintain below-cost pricing or adopt pricing practices that trigger state unfair trade practices liability. FTC Act §5 prohibits unfair methods of competition in or affecting commerce; adversarially manipulated competitive pricing AI that enables systemic competitive pricing failures creates FTC Act §5 unfair methods of competition dimensions in markets where competitive pricing intelligence is material to competitive conditions. Threshold: 65 for retail price optimisation display AI — reflecting the Robinson-Patman Act price discrimination liability, state predatory pricing statute compliance, FTC Act §5 unfair competition, and EU vertical block exemption pricing regulation dimensions of adversarially suppressed competitor price alert classification.

3. Customer review AI injection (Bazaarvoice AI, PowerReviews AI)

Customer review AI processes consumer review submission images, review authenticity classification dashboard displays, fake review detection signal visualisation outputs, star rating distribution analysis display graphics, and FTC compliance verification documentation display images from Bazaarvoice AI at more than 7,000 brand and retailer customers managing more than 1.3 billion consumer reviews processed through AI-assisted fake review detection, review authenticity classification, and FTC endorsement guide compliance verification tools at major consumer brands and retailers including Best Buy, Dell, Target, and P&G; PowerReviews AI at brand and retailer customer review management programme deployments; Yotpo AI at e-commerce customer review and loyalty programme management operations; Trustpilot AI moderation at business review platform operations serving more than 1 million active businesses and processing hundreds of millions of business reviews; Stamped.io AI at Shopify and BigCommerce e-commerce customer review management programme deployments; REVIEWS.io AI at UK and US e-commerce review management programme operations; and Google Shopping review AI moderation at Google Shopping product review programme operations — extracting fake review indicator and review authenticity classification inputs from customer review queue display image inputs in AI-assisted review content moderation and FTC compliance verification pipelines, generating review authenticity certification records, fake review detection alert outputs, FTC endorsement guide material connection disclosure compliance determinations, review removal action recommendations, and review quality gate documentation entries that customer experience teams, brand trust operations, and FTC compliance officers depend upon for FTC 16 CFR Part 255 endorsement guide material connection disclosure compliance, FTC Act §5 unfair and deceptive trade practices avoidance, and marketplace review platform policy compliance.

The adversarial injection surface is the customer review queue display image and review authenticity classification visualisation submission pathway: Bazaarvoice AI or PowerReviews AI customer review authenticity classification display images submitted through AI-assisted fake review indicator detection and FTC endorsement guide compliance verification tools for AI review quality determination and authentic/inauthentic classification generation. An adversarially crafted Bazaarvoice AI review queue display image — in which pixel perturbations applied to the fake review signal indicator display region, the suspicious review pattern visual marker, or the material connection disclosure compliance failure indicator in a customer review moderation queue display image cause the AI to classify a review submission set exhibiting fake review pattern indicators meeting the FTC endorsement guide material connection non-disclosure violation criteria as a below-threshold authentic organic review set not triggering fake review removal action when the actual review queue display documents suspicious review patterns meeting Bazaarvoice AI’s fake review detection classification criteria — can suppress a fake review detection alert that would otherwise generate a review removal action, an FTC compliance non-conformance record, and a marketplace review policy violation documentation entry. In brand and retailer customer experience management environments where Bazaarvoice AI or PowerReviews AI processes large volumes of customer review submissions without individual trust and safety team examination of every AI-generated review authenticity classification, adversarial suppression of fake review detection indicators allows inauthentic, incentivised, or solicited reviews without required FTC material connection disclosures to remain on brand product pages and retailer websites with FTC Act §5 unfair and deceptive trade practices and FTC endorsement guide non-compliance exposure.

The FTC 16 CFR Part 255, FTC Act §5, ROSCA, and FTC fake review enforcement action consequences of adversarially suppressed fake review detection in customer review AI span FTC 16 CFR Part 255 endorsement guide material connection disclosure obligations, FTC Act §5 unfair and deceptive trade practices prohibition, ROSCA 15 USC §8403 negative option marketing compliance, and FTC’s substantial civil penalty fake review enforcement authority dimensions. FTC 16 CFR Part 255 Guides Concerning the Use of Endorsements and Testimonials in Advertising requires that material connections between endorsers and brands be clearly and conspicuously disclosed to consumers; the FTC’s 2023 updated Guides include explicit provisions addressing fake reviews, paid reviews, and suppression of negative reviews — prohibiting brands from disseminating consumer reviews that were bought, solicited with incentives not disclosed, or created by insiders without clear disclosure of the material connection. Adversarial manipulation of Bazaarvoice AI or PowerReviews AI fake review detection that suppresses material connection indicator identification allows brands to unknowingly (or with plausible deniability) display incentivised or paid reviews without FTC-required disclosures with FTC Act §5 civil penalty exposure. The FTC has imposed substantial civil penalties for fake review violations: the FTC’s 2022 Warning Letter campaigns and the FTC’s 2023 Rule on the Use of Consumer Reviews and Testimonials (16 CFR Part 465) authorise civil penalties of up to $51,744 per violation of the fake review rule; the FTC has pursued enforcement actions with civil penalties exceeding $40 million against brands and marketplaces for systematic fake review promotion. ROSCA 15 USC §8403 prohibits misrepresentation or facilitation of charges for goods or services in internet commerce without clearly and conspicuously disclosing material terms; adversarially corrupted review AI that allows fake positive reviews to mislead consumers into purchasing subscriptions or negative option programmes creates ROSCA deceptive practices dimensions. Threshold: 60 for customer review AI — reflecting the FTC 16 CFR Part 255 endorsement guide material connection disclosure compliance, FTC Act §5 unfair and deceptive practices enforcement, ROSCA negative option compliance, and FTC civil penalty fake review enforcement dimensions of adversarially suppressed fake review detection classification.

4. Inventory demand forecast display injection (Manhattan Associates AI, Inventory Planner AI)

Inventory demand forecast display AI processes demand forecast trend dashboard visualisations, stockout risk probability indicator display graphics, inventory replenishment trigger threshold display images, seasonal demand pattern analysis visualisation outputs, and supply chain disruption impact forecast display images from Manhattan Associates retail AI at major retail chain, grocery, and distribution centre operations including Lululemon, Deckers (UGG, Teva, Hoka), and major grocery chain customers managing billions in annual inventory replenishment decisions; Inventory Planner AI at Shopify and BigCommerce e-commerce merchant operations managing inventory replenishment for thousands of online retailers; Blue Yonder AI at retail, grocery, and CPG supply chain demand management programme deployments at major global retailer and manufacturer operations; Relex Solutions AI at grocery retail and FMCG supply chain demand planning programme operations in European and US markets; o9 Solutions AI at large enterprise CPG and retail demand planning and supply chain optimisation programme deployments; and Logility AI demand planning at retail, wholesale distribution, and CPG supply chain planning programme operations — extracting stockout risk indicator and inventory replenishment trigger classifications from demand forecast dashboard display image inputs in AI-assisted inventory optimisation and supply chain management pipelines, generating stockout risk alert records, replenishment purchase order trigger recommendations, safety stock level adjustment outputs, promotional lift demand forecast inputs, and supplier order documentation entries that supply chain managers, replenishment planners, and retail operations directors depend upon for customer SLA contractual compliance, CPG co-manufacturing supply agreement inventory obligation fulfilment, retail vendor compliance programme chargeback avoidance, and lost sales minimisation.

The adversarial injection surface is the inventory demand forecast dashboard display and stockout risk probability visualisation submission pathway: Manhattan Associates AI or Inventory Planner AI demand forecast dashboard display images submitted through AI-assisted stockout risk classification and inventory replenishment trigger identification tools for AI inventory optimisation determination and replenishment purchase order generation. An adversarially crafted Manhattan Associates AI demand forecast dashboard display image — in which pixel perturbations applied to the stockout risk probability indicator display region, the demand trend acceleration visual marker, or the inventory days-of-supply depletion trajectory visualisation in an inventory demand forecast dashboard cause the AI to classify an inventory position exhibiting accelerating depletion rate and stockout risk exceeding the replenishment trigger threshold as a below-threshold adequate inventory position not triggering replenishment purchase order when the actual dashboard documents inventory depletion metrics meeting Manhattan Associates AI’s stockout risk trigger classification criteria — can suppress a stockout risk alert that would otherwise generate a replenishment purchase order, a supplier order recommendation, and an inventory position compliance documentation record. In retail and e-commerce supply chain management environments where Manhattan Associates AI or Blue Yonder AI processes demand forecast dashboards for large product assortments across distribution centre networks without individual replenishment planner examination of every AI-generated stockout risk classification, adversarial suppression of stockout risk indicators allows inventory depletion to progress to stockout conditions with lost sales, customer SLA breach, retail vendor compliance chargeback, and co-manufacturing supply agreement penalty consequences.

The customer SLA, CPG co-manufacturing supply agreement, retail vendor compliance chargeback, and consequential lost sales damages consequences of adversarially suppressed stockout risk classification in inventory demand forecast AI span customer SLA contractual performance obligation breaches, CPG co-manufacturing and contract manufacturing supply agreement inventory delivery obligation failures, retail vendor compliance programme chargeback penalty exposure, and consequential lost sales and market share damages dimensions. Customer SLA contractual obligations in retail and e-commerce typically specify product fill rate commitments — requiring suppliers to fulfil a minimum percentage (commonly 95-98%) of ordered units within specified delivery windows — with liquidated damages, chargeback penalty, and purchase order cancellation remedies for fill rate failures; adversarially manipulated Manhattan Associates AI demand forecast that suppresses stockout risk indicators causing inventory depletion below the fill rate compliance threshold creates customer SLA breach with contractual chargeback and damages exposure. CPG co-manufacturing and contract manufacturing supply agreements governing private label and national brand manufactured-to-order product inventory create purchase commitment and delivery obligation dimensions; adversarially corrupted Inventory Planner AI or Blue Yonder AI that suppresses demand forecast signals causing under-ordering from contract manufacturers creates supply agreement minimum purchase commitment shortfall and co-manufacturer capacity utilisation penalty dimensions. Retail vendor compliance programmes — including Amazon Vendor Central fill rate compliance, Walmart supplier scorecard on-time in-full (OTIF) compliance, and Target supplier performance programme requirements — impose automated chargeback penalty assessments for suppliers who fail to meet fill rate, OTIF delivery, and purchase order compliance thresholds; adversarially corrupted inventory forecast AI that causes retailers to miss OTIF and fill rate compliance creates multi-percentage-point chargeback penalty exposure on affected purchase orders. Consequential lost sales damages from stockout conditions — including lost consumer purchase occasion value, permanent demand loss from consumer brand switching during stockout periods, and retail shelf space allocation reductions from category manager scorecard penalties — create substantial consequential damages exposure from adversarially manipulated inventory forecast AI at major retailer and e-commerce platform destinations. Threshold: 65 for inventory demand forecast display AI — reflecting the customer SLA fill rate contractual obligation, CPG co-manufacturing supply agreement delivery commitment, retail vendor compliance OTIF chargeback penalty, and consequential lost sales damages dimensions of adversarially suppressed stockout risk classification.

Integration: retail and e-commerce AI image ingestion with Glyphward pre-scan

Retail and e-commerce AI image ingestion flows from Salsify AI and Clarifai AI product listing image brand safety and counterfeit detection channels, Algolia AI and Aptos/Revionics AI competitive pricing intelligence display interfaces, Bazaarvoice AI and PowerReviews AI customer review authenticity classification queue platforms, and Manhattan Associates AI and Inventory Planner AI demand forecast dashboard display systems into product listing brand safety gate and counterfeit detection AI, retail pricing competitive intelligence and dynamic price optimisation AI, customer review fake review detection and FTC compliance verification AI, and inventory demand forecast stockout risk identification and replenishment trigger AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to brand safety certifications, pricing adjustment recommendations, review authenticity determinations, or inventory replenishment triggers:

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"

# Retail & e-commerce AI — Lanham Act 15 USC §1114 trademark infringement;
# 15 USC §1125(a) trade dress; FTC 16 CFR Part 255 endorsement guide;
# FTC Act §5 unfair/deceptive practices; Robinson-Patman Act 15 USC §13;
# ROSCA 15 USC §8403; customer SLA fill rate; retail OTIF chargeback.
THRESHOLD_PRODUCT_LISTING_AI    = 60  # Salsify/Clarifai; Lanham Act; §1125(a); Amazon Brand Registry
THRESHOLD_PRICE_OPTIMIZATION_AI = 65  # Algolia/Aptos; Robinson-Patman; FTC §5; state predatory
THRESHOLD_CUSTOMER_REVIEW_AI    = 60  # Bazaarvoice/PowerReviews; FTC Part 255; §5; ROSCA
THRESHOLD_INVENTORY_FORECAST_AI = 65  # Manhattan Assoc/Inventory Planner; SLA; OTIF; lost sales


class RetailEcommerceAIContext(str, Enum):
    PRODUCT_LISTING_AI    = "product_listing_ai"    # Salsify, Clarifai, Stackline
    PRICE_OPTIMIZATION_AI = "price_optimization_ai" # Algolia, Aptos, Revionics, Zilliant
    CUSTOMER_REVIEW_AI    = "customer_review_ai"    # Bazaarvoice, PowerReviews, Yotpo
    INVENTORY_FORECAST_AI = "inventory_forecast_ai" # Manhattan Assoc, Inventory Planner, Blue Yonder


def threshold_for(context: RetailEcommerceAIContext) -> int:
    mapping = {
        RetailEcommerceAIContext.PRODUCT_LISTING_AI:    THRESHOLD_PRODUCT_LISTING_AI,
        RetailEcommerceAIContext.PRICE_OPTIMIZATION_AI: THRESHOLD_PRICE_OPTIMIZATION_AI,
        RetailEcommerceAIContext.CUSTOMER_REVIEW_AI:    THRESHOLD_CUSTOMER_REVIEW_AI,
        RetailEcommerceAIContext.INVENTORY_FORECAST_AI: THRESHOLD_INVENTORY_FORECAST_AI,
    }
    return mapping[context]


async def scan_retail_ecommerce_ai_image(
    image_path: str | Path,
    context: RetailEcommerceAIContext,
    brand_or_retailer_id_hash: str,  # SHA-256 of brand manufacturer or retailer identifier
    product_or_sku_ref: str,         # e.g. "SALSIFY-SKU-2026-44821", "BVOC-REV-88841"
    processing_session_id: str,      # listing sync run, pricing cycle, review batch, or forecast period
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan a retail or e-commerce AI image for adversarial injection payloads before
    forwarding to product listing brand safety and counterfeit detection, retail pricing
    competitive intelligence and dynamic optimisation, customer review fake review detection,
    or inventory demand forecast stockout risk identification AI systems.

    Raises AdversarialRetailEcommerceAIImageError if score meets threshold:
      - PRODUCT_LISTING_AI:    threshold 60; Lanham Act §1114; §1125(a); Amazon Brand Registry
      - PRICE_OPTIMIZATION_AI: threshold 65; Robinson-Patman §13; FTC §5; state predatory pricing
      - CUSTOMER_REVIEW_AI:    threshold 60; FTC Part 255; FTC §5; ROSCA §8403
      - INVENTORY_FORECAST_AI: threshold 65; customer SLA; OTIF; lost sales; CPG supply agreement
    """
    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": {
                "retail_ecommerce_context":     context.value,
                "brand_or_retailer_id_hash":    brand_or_retailer_id_hash,
                "product_or_sku_ref":           product_or_sku_ref,
                "processing_session_id":        processing_session_id,
                "client_scan_id":               client_scan_id,
                "image_sha256":                 image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "brand_or_retailer_id_hash": brand_or_retailer_id_hash,
        "product_or_sku_ref":        product_or_sku_ref,
        "processing_session_id":     processing_session_id,
        "retail_ecommerce_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_retail_audit_record(audit_record)

    if result["score"] >= threshold:
        raise AdversarialRetailEcommerceAIImageError(
            f"Retail/e-commerce AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"brand={brand_or_retailer_id_hash} ref={product_or_sku_ref}"
        )
    return result


async def write_retail_audit_record(record: dict) -> None:
    """Persist audit record to brand protection and retail compliance documentation store (stub)."""
    import json, sys
    print(json.dumps(record), file=sys.stderr)


class AdversarialRetailEcommerceAIImageError(Exception):
    """Raised when a retail or e-commerce AI image exceeds the adversarial injection threshold."""
    pass

Call scan_retail_ecommerce_ai_image() with RetailEcommerceAIContext.PRODUCT_LISTING_AI before forwarding Salsify AI or Clarifai AI product listing images to brand safety classification and counterfeit detection AI — with product_or_sku_ref linking the Glyphward scan to the SKU record for Lanham Act 15 USC §1114 trademark infringement enforcement, Amazon Brand Registry counterfeit reporting, and 15 USC §1125(a) trade dress protection audit documentation. Call with RetailEcommerceAIContext.PRICE_OPTIMIZATION_AI for Algolia AI or Aptos/Revionics AI competitive pricing display interface images before AI competitor price alert classification and dynamic pricing adjustment recommendation generation, with brand_or_retailer_id_hash for Robinson-Patman Act 15 USC §13 price discrimination compliance monitoring and FTC Act §5 unfair competition compliance audit trail. Call with RetailEcommerceAIContext.CUSTOMER_REVIEW_AI for Bazaarvoice AI or PowerReviews AI customer review authenticity classification queue display images before AI fake review indicator detection and FTC endorsement guide compliance verification, with processing_session_id as the review moderation batch identifier for FTC 16 CFR Part 255 material connection disclosure compliance, FTC Act §5 enforcement, and ROSCA 15 USC §8403 negative option compliance audit documentation. Call with RetailEcommerceAIContext.INVENTORY_FORECAST_AI for Manhattan Associates AI or Inventory Planner AI demand forecast dashboard display images before AI stockout risk classification and replenishment purchase order trigger generation, with product_or_sku_ref for customer SLA fill rate contractual compliance, Walmart/Amazon OTIF chargeback penalty avoidance, and consequential lost sales damages mitigation audit trail. Get early access

Coverage matrix

Frequently asked questions

Further reading

• Indirect prompt injection via image — foundational attack pattern underlying all four retail and e-commerce AI injection surfaces; covers how adversarial pixel-level perturbations cause AI misclassification without detectable visual artefacts at human review resolution.
• Vision-language model security — technical architecture of adversarial image attacks against vision-language models including pixel perturbation classes applicable to product listing image AI, retail pricing display AI, customer review queue AI, and inventory demand forecast dashboard AI processing pipelines.
• Supply chain and logistics AI prompt injection — related attack surface analysis covering warehouse management AI, transportation logistics AI, freight broker AI, and 3PL operations AI adversarial injection with customer SLA, OTIF, and inventory compliance regulatory dimensions applicable to retail and e-commerce supply chain AI contexts.
• Free tier — 10 scans/day, no card required — start scanning retail and e-commerce AI images at development volumes before committing to a production plan.