Prompt injection in EV battery and critical minerals supply chain AI

Four adversarial injection surfaces in EV battery and critical minerals supply chain AI

1. Battery passport and cell traceability image injection (EU Battery Regulation 2023/1542 Article 77, IEC 63330)

Battery passport and cell traceability AI processes battery cell QR code and 2D barcode scan images displaying cell serial number, cathode active material chemistry code, electrolyte specification, gigafactory line identifier, and manufacturing date batch fields with AI-readable traceability chain verification outputs, cathode active material certificate of analysis display images showing chemical composition specifications, lot number, supplier identifier, and EU Battery Regulation Annex XIII traceability attribute fields with AI-assisted material specification compliance overlays, electrolyte composition traceability document images showing electrolyte formulation specification, lot traceability identifier, and gigafactory receipt verification fields, gigafactory cell manufacturing specification display images showing cell formation programme code, capacity specification, energy density, and cycle life qualification test result reference fields with AI-generated battery passport Annex XIII data population outputs, and battery module and pack assembly traceability display images showing multi-cell assembly configuration and module identifier mapping from Circulor AI at BMW, Volvo, Volkswagen, and 60 or more tier-1 supplier clients processing battery cell QR code scan and traceability certificate display images through AI-assisted EU Digital Battery Passport data population, IEC 63330 battery data exchange, and supply chain traceability chain verification tools; BASF Battery Materials AI at cathode active material traceability for 10 or more gigafactory clients processing cathode material certificate of analysis and manufacturing specification display images through AI-assisted material traceability, battery passport Annex XIII attribute population, and EU Battery Regulation compliance tools; and Umicore Traceability AI at cobalt and recycled battery materials provenance tracking for 100 or more smelter clients processing material provenance and recycled content certification display images through AI-assisted EU Battery Regulation recycled content target verification and battery passport chain-of-custody population tools — extracting EU Digital Battery Passport Annex XIII traceability attribute determinations, IEC 63330 battery data exchange compliance assessments, EU CBAM carbon footprint reporting inputs, and EU Critical Raw Materials Act supply chain disclosure data from battery cell QR code scan and traceability certificate display image inputs in AI-assisted EV battery supply chain traceability compliance pipelines at gigafactory and OEM integration volumes that make individual human quality inspector review of every AI-processed traceability document impracticable.

The adversarial injection surface is the battery cell QR code scan image, cathode active material certificate of analysis display image, or gigafactory cell manufacturing specification display image submission pathway: Circulor AI, BASF Battery Materials AI, or Umicore Traceability AI battery traceability and passport data display images submitted through AI-assisted EU Battery Passport compliance and IEC traceability chain verification tools for AI passport attribute determination record generation and EU regulatory filing input. An adversarially crafted battery cell QR code scan image — in which pixel perturbations applied to the QR code module display pattern, the barcode bar spacing display, or the accompanying traceability label field display cause the AI to misclassify the cell’s cathode active material chemistry as lithium iron phosphate (LFP) rather than nickel manganese cobalt (NMC) oxide chemistry — or to associate the cell with a supplier’s certificate of analysis that specifies higher recycled cobalt content than the actual cell chemistry contains — creates a battery passport Annex XIII traceability record that misrepresents the cell’s chemistry, material provenance, or recycled content in ways that affect EU Battery Regulation 2023/1542 compliance for EV batteries placed on the EU market. Adversarial injection can also suppress a material specification non-conformance indicator that would otherwise generate a cathode active material quality hold, a battery passport data rejection, or an EU Battery Regulation Annex XIII compliance failure notification. In gigafactory battery traceability operations where Circulor AI or BASF Battery Materials AI processes thousands of battery cell traceability document images per production shift without individual human traceability officer examination of every AI-processed certificate display before the AI passport attribute determination governs the EU regulatory traceability record, adversarial corruption of battery traceability AI creates EU Battery Regulation 2023/1542 Article 77 digital passport accuracy, IEC 63330 data exchange standard, and EU Critical Raw Materials Act supply chain disclosure dimensions.

The EU Battery Regulation 2023/1542 Article 77, EU Critical Raw Materials Act, IEC 63330, and EU ETS CBAM regulatory consequences of adversarially corrupted battery passport and cell traceability classification span EU Battery Regulation 2023/1542 Article 77 digital battery passport requirements establishing that from 2027 all industrial and EV batteries placed on the EU market must carry a digital battery passport containing traceability data specified in Annex XIII — including cell chemistry, materials provenance, recycled content, and carbon footprint data — with market access denial and product recall authority as enforcement tools for non-compliant passport data; EU Critical Raw Materials Act battery supply chain disclosure requirements establishing monitoring and transparency obligations for critical raw materials in battery supply chains of companies operating in the EU — adversarially corrupted battery passport traceability AI that misrepresents cathode chemistry or material provenance creates CRMA disclosure accuracy failure dimensions; IEC 63330 battery data exchange standard applicable to battery passport interoperability and data format compliance — adversarially corrupted traceability AI creates IEC 63330 data exchange standard compliance failure dimensions affecting interoperability with EU battery data space infrastructure; EU ETS Carbon Border Adjustment Mechanism (CBAM) battery manufacturing carbon footprint reporting requiring verified carbon intensity data for batteries imported into the EU — adversarially corrupted battery passport AI that misrepresents material chemistry and gigafactory sourcing creates CBAM carbon footprint calculation integrity failure dimensions; and BMW, Volvo, and Volkswagen OEM quality management system requirements for battery supply chain traceability accuracy — adversarially corrupted Circulor AI traceability determinations create OEM contractual compliance dimensions. The EU Battery Regulation’s market access enforcement mechanism — denial of conformity for batteries with non-compliant digital passports — creates direct commercial consequence dimensions for adversarially corrupted battery passport AI. Threshold: 55 for battery passport and cell traceability image injection — reflecting EU Battery Regulation 2023/1542 Article 77 digital passport accuracy, EU CRMA supply chain disclosure, IEC 63330 data exchange, and EU ETS CBAM carbon footprint reporting dimensions.

2. Cobalt and critical mineral chain-of-custody document injection (SEC Regulation 13p-1 Form SD, OECD CAHRA)

Cobalt and critical mineral chain-of-custody document AI processes smelter audit certificate scan images from RMAP (Responsible Minerals Assurance Process) or equivalent recognised industry standard conformance programme (RCISP) audits displaying smelter name, audit date, conformance status, and geographic sourcing attestation fields with AI-readable conflict mineral due diligence compliance overlays, mine-of-origin provenance chain document images showing sourcing country, mine operator name, extraction site coordinates, and mine-to-smelter chain-of-custody documentation with AI-assisted CAHRA (Conflict-Affected and High-Risk Area) classification outputs, OECD CAHRA due diligence audit report display images showing five-step OECD Due Diligence Guidance compliance assessment fields and risk flag indicators, artisanal and small-scale mining (ASM) certification document scan images showing fair trade or responsible sourcing programme certification status, and OFAC DRC sanctions screening result display images showing smelter and mine operator name comparison against OFAC SDN List with match confidence indicators from Umicore Traceability AI at cobalt and recycled battery materials provenance tracking across 100 or more smelter clients processing RMAP audit certificate scan and mine-of-origin provenance chain document images through AI-assisted conflict mineral due diligence, SEC Form SD disclosure preparation, and EU Conflict Minerals Regulation compliance tools; Circulor AI at BMW, Volvo, Volkswagen, and 60 or more tier-1 supplier client traceability operations processing cobalt chain-of-custody document scan images through AI-assisted supply chain due diligence and conflict mineral disclosure tools; and Sourcemap AI at 200 or more Fortune 500 enterprise supply chain transparency deployments processing mineral provenance chain document scan images through AI-assisted supplier due diligence, conflict mineral disclosure, and supply chain transparency reporting tools — extracting SEC Form SD conflict mineral disclosure determinations, OECD CAHRA due diligence programme compliance assessments, EU Conflict Minerals Regulation importer due diligence verifications, and OFAC DRC sanctions screening results from cobalt smelter audit certificate and mine-of-origin provenance chain document scan image inputs in AI-assisted conflict mineral due diligence compliance pipelines.

The adversarial injection surface is the RMAP smelter audit certificate scan image, mine-of-origin provenance chain document image, or OECD CAHRA audit report display image submission pathway: Umicore Traceability AI, Circulor AI, or Sourcemap AI cobalt provenance and conflict mineral compliance document scan images submitted through AI-assisted SEC Form SD disclosure preparation and OECD due diligence compliance tools for AI conflict mineral determination record generation and SEC annual filing input. An adversarially crafted RMAP smelter audit certificate scan image — in which pixel perturbations applied to the conformance status indicator field display, the smelter geographic origin attestation display, the CAHRA risk flag indicator, or the OECD five-step due diligence compliance status summary display cause the AI to classify a smelter whose RMAP audit has identified sourcing from CAHRA conflict-affected areas with an active non-conformance finding as a fully conformant RMAP smelter with no conflict mineral sourcing risk when the actual audit certificate evidences non-conformance and CAHRA sourcing risk — can suppress a conflict mineral red flag that would otherwise generate a SEC Form SD “DRC conflict minerals” or “not DRC conflict-free” disclosure determination, an OECD due diligence programme risk escalation, an EU Conflict Minerals Regulation importer due diligence exception report, or an OFAC DRC sanctions screening escalation. In conflict mineral due diligence programmes where Umicore AI or Sourcemap AI processes hundreds of smelter audit certificate scan images annually without individual human supply chain compliance reviewer examination of every AI-processed certificate before the AI determination governs the SEC Form SD disclosure outcome, adversarial suppression of RMAP non-conformance and CAHRA risk indicators creates SEC Regulation 13p-1, OECD CAHRA, OFAC 31 CFR Part 546, and EU Conflict Minerals Regulation 2017/821 dimensions.

The SEC Regulation 13p-1 Form SD, OECD CAHRA Due Diligence Guidance, OFAC 31 CFR Part 546, and EU Conflict Minerals Regulation 2017/821 regulatory consequences of adversarially corrupted cobalt chain-of-custody classification span SEC Regulation 13p-1 Form SD conflict minerals disclosure requirements under Dodd-Frank §1502 establishing that SEC-registered manufacturers must determine whether conflict minerals originate from covered countries and disclose this determination annually on SEC Form SD — adversarially corrupted AI conflict mineral due diligence that misclassifies non-conformant RMAP smelters as conformant enables false “DRC conflict-free” Form SD disclosures creating SEC Exchange Act §13(p) enforcement dimensions and potential SEC fraud enforcement action; OECD Due Diligence Guidance for Responsible Supply Chains of Minerals from CAHRA establishing a five-step due diligence framework that includes assessing suppliers through supply chain audits and acting on identified risks — adversarially corrupted RMAP audit AI that suppresses CAHRA risk flags creates OECD due diligence framework compliance failure dimensions affecting company ESG reporting and investor due diligence reliance; OFAC DRC Sanctions 31 CFR Part 546 establishing restrictions on transactions with entities designated under the DRC sanctions programme — adversarially corrupted OFAC screening AI that suppresses DRC sanctions match indicators for smelter operators creates OFAC civil penalty exposure up to $1.3 million per transaction; EU Conflict Minerals Regulation 2017/821 requirements for EU importers of tin, tantalum, tungsten, and gold from CAHRA to conduct OECD-aligned due diligence and file annual responsible sourcing disclosure reports — adversarially corrupted AI due diligence creates EU Regulation 2017/821 compliance failure and supply chain import denial dimensions; and US Customs and Border Protection import restrictions on goods made with conflict minerals from sanctioned sources applicable to battery supply chain inputs. Threshold: 60 for cobalt and critical mineral chain-of-custody document injection — reflecting SEC Regulation 13p-1 Form SD Dodd-Frank §1502, OECD CAHRA due diligence guidance, OFAC DRC 31 CFR Part 546, and EU Conflict Minerals Regulation 2017/821 dimensions.

3. Battery cell manufacturing QC and formation image injection (UN 38.3, IEC 62133)

Battery cell manufacturing QC and formation AI processes cell formation curve display images showing cell voltage, capacity, coulombic efficiency, and internal resistance evolution traces across formation cycling protocols with AI-readable pass/fail quality gate annotations, electrode coating defect detection display images showing AI-generated defect density maps, coating thickness uniformity deviation indicators, particle size distribution anomaly alerts, and electrode calendering defect classification outputs, electrolyte fill level inspection display images showing AI-classified fill volume accuracy, seal integrity status, and electrolyte contamination indicator displays, thermal runaway risk indicator display images showing AI-generated thermal stability assessment scores derived from differential scanning calorimetry data visualisations and accelerating rate calorimetry output display images, and UN Manual of Tests and Criteria Section 38.3 transportation qualification test result summary display images showing T.1 altitude simulation, T.2 thermal test, T.3 vibration, T.4 shock, T.5 external short circuit, T.6 impact/crush, T.7 overcharge, and T.8 forced discharge test result pass/fail status from BASF Battery Materials AI at cathode active material traceability and quality management for 10 or more gigafactory clients processing cell formation curve, electrode defect detection, and manufacturing specification display images through AI-assisted manufacturing quality control, UN 38.3 transportation safety certification, and IEC 62133 cell safety standard compliance tools; Circulor AI at BMW, Volvo, Volkswagen, and 60 or more tier-1 supplier integration operations processing cell manufacturing QC display images through AI-assisted supply chain quality gate verification and battery safety certification documentation tools; and Everstream Analytics AI at 500 or more enterprise client supply chain risk intelligence operations processing manufacturing quality risk indicator display images through AI-assisted supply chain disruption risk and product quality compliance tools — extracting UN 38.3 transportation safety qualification pass/fail determinations, IEC 62133 cell safety standard compliance assessments, UL 9540 energy storage system certification inputs, and 49 CFR Part 173.185 hazardous materials transportation compliance verifications from cell manufacturing QC and formation curve display image inputs in AI-assisted EV battery manufacturing safety certification and transportation compliance pipelines.

The adversarial injection surface is the cell formation curve display image, electrode coating defect detection display image, or UN 38.3 test result summary display image submission pathway: BASF Battery Materials AI, Circulor AI, or Everstream Analytics AI cell manufacturing QC and safety certification display images submitted through AI-assisted UN 38.3 transportation qualification and IEC 62133 safety standard compliance tools for AI certification determination record generation and transportation safety compliance documentation filing. An adversarially crafted cell formation curve display image — in which pixel perturbations applied to the coulombic efficiency percentage display, the capacity fade curve trajectory, the internal resistance growth indicator, or the thermal runaway onset temperature annotation in the formation cycle analysis display cause the AI to classify a cell with substandard formation performance — including cells with initial coulombic efficiency below the production specification threshold, cells with thermal runaway onset temperatures below the UN 38.3 T.2 thermal test pass criteria, or cells with electrode defects meeting the facility’s critical defect rejection criteria — as a passing cell meeting all manufacturing quality gate criteria and UN 38.3 transportation safety qualification requirements when the actual formation and QC data evidences specification non-conformance or safety certification failure — can suppress a manufacturing quality rejection that would otherwise generate a cell hold, a production batch quarantine, a UN 38.3 transportation safety certification failure, or an IEC 62133 cell safety standard non-conformance notification. In gigafactory operations where BASF Battery Materials AI processes thousands of cell formation curve and QC display images per production shift without individual human quality engineer re-review of every AI-processed display before the AI quality gate determination governs production release and transportation safety certification, adversarial suppression of manufacturing quality and safety failure indicators creates UN 38.3, IEC 62133, UL 9540, and 49 CFR Part 173.185 dimensions.

The UN Manual of Tests and Criteria Section 38.3, IEC 62133, UL 9540, and 49 CFR Part 173.185 regulatory consequences of adversarially corrupted cell manufacturing QC classification span UN Manual of Tests and Criteria Section 38.3 lithium battery transportation testing requirements establishing mandatory abuse testing protocols for lithium cells and batteries shipped by air (IATA), sea (IMDG), and road (ADR) — each test requiring documented pass results before batteries may be classified as compliant for shipping — adversarial corruption of Section 38.3 test result display AI that creates false pass determinations for cells that failed T.2 thermal stability or T.5 external short circuit tests enables shipment of transportation-unsafe lithium batteries with IATA/IMDG/ADR dangerous goods regulation violation dimensions; IEC 62133 secondary lithium cell safety standard applicable to cell-level safety certification required by many OEM procurement specifications — adversarially corrupted IEC 62133 compliance display AI that creates false conformance determinations for cells with thermal runaway risk or electrode defects creates product safety certification fraud dimensions; UL 9540 standard for energy storage systems applicable to battery pack and system-level safety certification — adversarially corrupted AI safety certification creates UL 9540 product listing fraud dimensions; 49 CFR Part 173.185 DOT hazardous materials transportation requirements establishing that lithium batteries must meet Section 38.3 testing, packaging, and quantity limits — adversarially enabled shipment of non-38.3-compliant batteries creates DOT PHMSA hazardous materials transportation violation dimensions with civil penalty exposure up to $87,117 per violation per day; and National Highway Traffic Safety Administration (NHTSA) Federal Motor Vehicle Safety Standard FMVSS 305 electric vehicle battery system crash and post-crash integrity requirements applicable to EV battery cell quality certification. Threshold: 65 for battery cell manufacturing QC and formation image injection — reflecting UN Manual Section 38.3 transportation safety certification, IEC 62133 cell safety standard, UL 9540 energy storage system certification, and 49 CFR Part 173.185 DOT hazardous materials transportation compliance dimensions.

4. EV battery second-life and recycling documentation injection (EPA RCRA 40 CFR Part 261, EU Battery Regulation recycled content)

EV battery second-life and recycling documentation AI processes state-of-health (SOH) assessment display images showing AI-generated battery pack capacity retention percentage, internal resistance growth factor, thermal performance assessment, and second-life eligibility determination status with recommended application tier classification (stationary energy storage eligible, second-life EV eligible, direct recycling required), battery second-life eligibility determination display images showing AI-classified capacity threshold comparison, safety certification assessment, and remaining useful life estimation outputs with application tier recommendation, hazardous waste manifest scan images displaying generator name, EPA ID number, waste code, quantity, and disposal facility information with AI-assisted RCRA waste characterisation and manifest accuracy verification overlays, recycled material content certification document images showing verified recovered cobalt, lithium, nickel, and manganese mass fractions with EU Battery Regulation Annex XIII recycled content percentage attestation fields, and Basel Convention transboundary hazardous waste movement permit display images from Li-Cycle Battery Recycling AI at 45,000 or more tonnes of battery recycling capacity in North America and Europe processing battery SOH assessment, hazardous waste manifest scan, and recycled material content certification display images through AI-assisted RCRA waste classification, battery second-life eligibility, and EU Battery Regulation recycled content verification tools; Umicore AI at cobalt and recycled battery materials processing for 100 or more smelter operations processing recycled material content certification display images through AI-assisted EU Battery Regulation recycled content target compliance and materials provenance traceability tools; and Circulor AI at BMW, Volvo, and Volkswagen OEM integration operations processing battery end-of-life traceability and recycled content certification display images through AI-assisted EU Battery Passport recycled content attribute population and supply chain circularity documentation tools — extracting EPA RCRA hazardous waste classification determinations, EU Battery Regulation Annex XIII recycled content target compliance assessments, battery second-life application tier eligibility certifications, and Basel Convention transboundary movement permit verifications from battery SOH assessment and recycling documentation display image inputs in AI-assisted battery end-of-life regulatory compliance pipelines.

The adversarial injection surface is the battery SOH assessment display image, recycled material content certification document image, or hazardous waste manifest scan image submission pathway: Li-Cycle Battery Recycling AI, Umicore AI, or Circulor AI battery end-of-life documentation display images submitted through AI-assisted RCRA waste characterisation, EU Battery Regulation recycled content verification, and battery second-life eligibility determination tools for AI compliance determination record generation and regulatory filing input. An adversarially crafted recycled material content certification document image — in which pixel perturbations applied to the recovered cobalt mass fraction percentage display, the EU Battery Regulation Annex XIII recycled content target percentage comparison display, the material lot identifier field, or the certification programme conformance status indicator cause the AI to classify a battery recycler’s recovered materials as meeting the EU Battery Regulation 2023/1542 Annex XIII recycled cobalt content target of 16% (mandatory from 2030) when the actual recovered mass data evidences recovered cobalt content below the target threshold — can enable false EU Battery Regulation recycled content target compliance certification for OEM battery procurement, suppress an EPA RCRA hazardous waste characterisation finding that would otherwise require manifest documentation as listed hazardous waste rather than solid waste, misclassify a battery pack below the SOH threshold for safe second-life use as second-life eligible, or create a false Basel Convention permit compliance determination for transboundary battery waste shipment. In battery recycling operations where Li-Cycle AI or Umicore AI processes thousands of recycled content certification and waste manifest display images annually without individual human environmental compliance reviewer examination of every AI-processed document before the AI determination governs RCRA waste classification or EU recycled content certification, adversarial suppression of compliance failure indicators creates EPA RCRA 40 CFR Part 261, EU Battery Regulation Annex XIII, California SB 100, and Basel Convention dimensions.

The EPA RCRA 40 CFR Part 261, EU Battery Regulation Annex XIII recycled content targets, California SB 100, and Basel Convention regulatory consequences of adversarially corrupted battery second-life and recycling compliance classification span EPA RCRA hazardous waste management requirements under 40 CFR Part 261 establishing waste characterisation criteria for spent lithium batteries — including listings under 40 CFR Part 261.31 (F-list) and 40 CFR Part 261.33 (P/U-list) — adversarially corrupted waste characterisation AI that misclassifies RCRA hazardous battery waste as non-hazardous solid waste creates 40 CFR Part 261 characterisation failure dimensions with RCRA Subtitle C generator compliance obligations including manifest documentation and licensed RCRA treatment, storage, and disposal facility (TSDF) disposal requirements and civil penalty exposure of up to $37,500 per day per violation; EU Battery Regulation 2023/1542 Annex XIII recycled content target requirements establishing minimum recycled material percentages in new EV batteries from 2030 — adversarially fabricated recycled content AI certifications that enable OEM battery procurement with false recycled content attestations create EU Battery Regulation market access and Annex XIII compliance failure dimensions with potential market access denial for non-compliant batteries; California SB 100 and California Department of Resources Recycling and Recovery (CalRecycle) battery recycling extended producer responsibility requirements applicable to EV battery end-of-life management in the California market; Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and Their Disposal establishing prior informed consent requirements for international hazardous waste movements — adversarially corrupted Basel permit status AI creates transboundary battery waste movement compliance failure dimensions; and battery second-life safety certification dimensions — adversarially fabricated battery SOH eligibility AI that certifies below-threshold-SOH battery packs as second-life eligible creates product liability and fire safety risk dimensions for stationary energy storage applications. Threshold: 50 for EV battery second-life and recycling documentation injection — reflecting EPA RCRA 40 CFR Part 261 hazardous waste characterisation, EU Battery Regulation Annex XIII recycled content targets, California SB 100 battery recycling requirements, Basel Convention transboundary hazardous waste, and battery second-life safety certification dimensions.

Integration: EV battery and critical minerals supply chain AI image ingestion with Glyphward pre-scan

EV battery and critical minerals supply chain AI image ingestion flows from Circulor AI, BASF Battery Materials AI, and Umicore Traceability AI battery cell QR code scan and traceability certificate display image processing channels, Umicore Traceability AI, Circulor AI, and Sourcemap AI cobalt smelter RMAP audit certificate scan and mine-of-origin provenance document image processing interfaces, BASF Battery Materials AI, Circulor AI, and Everstream Analytics AI cell formation curve and electrode defect detection display image processing pipelines, and Li-Cycle Battery Recycling AI, Umicore AI, and Circulor AI battery SOH assessment, recycled content certification, and hazardous waste manifest scan image processing platforms into EU Battery Passport traceability compliance AI, conflict mineral due diligence AI, cell manufacturing QC and safety certification AI, and battery end-of-life recycling regulatory compliance AI pipelines. Insert Glyphward’s pre-scan at the ingestion boundary before AI-generated output is committed to EU Digital Battery Passport Annex XIII traceability records, SEC Form SD conflict mineral disclosure inputs, UN 38.3 transportation safety certification records, or EPA RCRA hazardous waste characterisation and EU Battery Regulation recycled content certification 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"

# EV battery & critical minerals supply chain AI — adversarial pixel injection
# in battery cell QR code traceability images, cobalt RMAP audit certificates,
# cell manufacturing formation curve displays, and battery recycling documents
# with EU Battery Regulation 2023/1542 Article 77, SEC Regulation 13p-1 Form SD,
# UN 38.3 transport testing, and EPA RCRA 40 CFR Part 261 regulatory consequences.

# EU Battery Regulation 2023/1542 Article 77 digital battery passport Annex XIII;
# EU Critical Raw Materials Act supply chain disclosure; IEC 63330 data exchange;
# EU ETS CBAM battery carbon footprint reporting requirements.
THRESHOLD_BATTERY_PASSPORT_AI           = 55

# SEC Regulation 13p-1 Form SD Dodd-Frank §1502 conflict minerals;
# OECD CAHRA due diligence guidance; OFAC DRC sanctions 31 CFR Part 546;
# EU Conflict Minerals Regulation 2017/821 importer due diligence.
THRESHOLD_COBALT_CHAIN_OF_CUSTODY_AI    = 60

# UN Manual of Tests & Criteria Section 38.3 lithium battery transport testing
# (IATA/IMDG/ADR); IEC 62133 secondary lithium cell safety; UL 9540 ESS;
# 49 CFR Part 173.185 DOT hazardous materials transportation.
THRESHOLD_CELL_MANUFACTURING_QC_AI      = 65

# EPA RCRA 40 CFR Part 261 hazardous waste; EU Battery Regulation Annex XIII
# recycled content targets; California SB 100 battery recycling; Basel Convention.
THRESHOLD_BATTERY_RECYCLING_AI          = 50


class EVBatteryAIContext(str, Enum):
    BATTERY_PASSPORT_AI          = "battery_passport_ai"          # Circulor, BASF Battery Materials, Umicore
    COBALT_CHAIN_OF_CUSTODY_AI   = "cobalt_chain_of_custody_ai"   # Umicore, Circulor, Sourcemap
    CELL_MANUFACTURING_QC_AI     = "cell_manufacturing_qc_ai"     # BASF Battery Materials, Circulor, Everstream
    BATTERY_RECYCLING_AI         = "battery_recycling_ai"         # Li-Cycle, Umicore, Circulor


def threshold_for(context: EVBatteryAIContext) -> int:
    mapping = {
        EVBatteryAIContext.BATTERY_PASSPORT_AI:          THRESHOLD_BATTERY_PASSPORT_AI,
        EVBatteryAIContext.COBALT_CHAIN_OF_CUSTODY_AI:   THRESHOLD_COBALT_CHAIN_OF_CUSTODY_AI,
        EVBatteryAIContext.CELL_MANUFACTURING_QC_AI:     THRESHOLD_CELL_MANUFACTURING_QC_AI,
        EVBatteryAIContext.BATTERY_RECYCLING_AI:         THRESHOLD_BATTERY_RECYCLING_AI,
    }
    return mapping[context]


async def scan_ev_battery_ai_image(
    image_path: str | Path,
    context: EVBatteryAIContext,
    battery_entity_hash: str,   # SHA-256 of battery cell serial number or supply chain entity ID
    programme_ref: str,         # e.g. "CIRCULOR-PASS-2026-BMW-G09-04812", "LICYCLE-RCRA-2026-US-0041"
    supply_chain_session_id: str,  # traceability batch session, audit review session, or recycling batch ID
    client: httpx.AsyncClient,
) -> dict:
    """
    Scan an EV battery or critical minerals supply chain AI image for adversarial
    injection payloads before forwarding to battery passport traceability,
    cobalt conflict mineral due diligence, cell manufacturing QC safety
    certification, or battery recycling regulatory compliance AI.

    Raises AdversarialEVBatteryAIImageError if score meets threshold:
      - BATTERY_PASSPORT_AI:          threshold 55; EU Battery Reg 2023/1542 Art. 77
      - COBALT_CHAIN_OF_CUSTODY_AI:   threshold 60; SEC Reg 13p-1 Form SD §1502
      - CELL_MANUFACTURING_QC_AI:     threshold 65; UN 38.3; IEC 62133; 49 CFR §173.185
      - BATTERY_RECYCLING_AI:         threshold 50; EPA RCRA 40 CFR Part 261; Basel
    """
    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": {
                "ev_battery_context":        context.value,
                "battery_entity_hash":       battery_entity_hash,
                "programme_ref":             programme_ref,
                "supply_chain_session_id":   supply_chain_session_id,
                "client_scan_id":            client_scan_id,
                "image_sha256":              image_sha256,
            },
        },
        timeout=8.0,
    )
    resp.raise_for_status()
    result = resp.json()

    audit_record = {
        "battery_entity_hash":       battery_entity_hash,
        "programme_ref":             programme_ref,
        "supply_chain_session_id":   supply_chain_session_id,
        "ev_battery_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_ev_battery_audit_record(audit_record)

    if result["score"] >= threshold:
        raise AdversarialEVBatteryAIImageError(
            f"EV battery supply chain AI image blocked [{context.value}]: "
            f"scan_id={result['scan_id']} score={result['score']} "
            f"entity={battery_entity_hash} ref={programme_ref}"
        )
    return result


async def write_ev_battery_audit_record(record: dict) -> None:
    """Persist audit record to EV battery supply chain AI regulatory documentation store (stub)."""
    import json, sys
    print(json.dumps(record), file=sys.stderr)


class AdversarialEVBatteryAIImageError(Exception):
    """Raised when an EV battery supply chain AI image exceeds the adversarial injection threshold."""
    pass

Call scan_ev_battery_ai_image() with EVBatteryAIContext.BATTERY_PASSPORT_AI before forwarding Circulor AI, BASF Battery Materials AI, or Umicore Traceability AI battery cell QR code scan and cathode active material certificate of analysis display images to EU Digital Battery Passport attribute population AI — with battery_entity_hash as the SHA-256 of the battery cell serial number for EU Battery Regulation 2023/1542 Article 77 digital passport Annex XIII accuracy, EU CRMA supply chain disclosure integrity, and IEC 63330 battery data exchange standard compliance. Call with EVBatteryAIContext.COBALT_CHAIN_OF_CUSTODY_AI for Umicore AI, Circulor AI, or Sourcemap AI RMAP smelter audit certificate scan and mine-of-origin provenance chain document images before conflict mineral due diligence AI — for SEC Regulation 13p-1 Form SD Dodd-Frank §1502 conflict mineral disclosure accuracy, OECD CAHRA five-step due diligence programme compliance, and OFAC DRC 31 CFR Part 546 sanctions screening audit trail. Call with EVBatteryAIContext.CELL_MANUFACTURING_QC_AI for BASF Battery Materials AI, Circulor AI, or Everstream Analytics AI cell formation curve, electrode defect detection, and UN 38.3 test result display images before manufacturing QC and transportation safety certification AI — for UN Manual Section 38.3 lithium battery transport qualification, IEC 62133 cell safety standard, and 49 CFR Part 173.185 DOT hazardous materials transportation compliance documentation. Call with EVBatteryAIContext.BATTERY_RECYCLING_AI for Li-Cycle AI, Umicore AI, or Circulor AI battery SOH assessment, recycled content certification, and hazardous waste manifest scan images before battery end-of-life compliance AI — for EPA RCRA 40 CFR Part 261 hazardous waste characterisation, EU Battery Regulation Annex XIII recycled content target compliance, and Basel Convention transboundary waste movement permit verification. Get early access

Coverage matrix

Related questions

Further reading

• FigStep adversarial image injection detection — technical overview of pixel-level adversarial perturbation underlying battery cell QR code traceability injection, RMAP smelter certificate suppression, and cell formation curve display corruption.
• Vision-language model security — architectural overview of multimodal AI adversarial injection covering the VLM image encoder layers that Circulor AI, BASF Battery Materials AI, Li-Cycle Battery Recycling AI, and Sourcemap AI use to process traceability certificate and compliance document images.
• Free tier — 10 scans/day, no card required — start scanning EV battery and critical minerals supply chain AI image inputs at development volumes; test battery passport QR code, RMAP certificate, formation curve, and recycling manifest injection detection without a payment method on file.
• Prompt injection in semiconductor fab AI — related manufacturing AI injection surface covering semiconductor fabrication quality control and yield management AI with overlapping IEC safety standard and manufacturing defect detection injection dimensions.
• Prompt injection in ESG and sustainability reporting AI — related ESG compliance AI injection surface covering carbon footprint and supply chain sustainability reporting AI with overlapping EU Battery Regulation CBAM, OECD CAHRA, and SEC conflict minerals disclosure dimensions.
• Prompt injection in supply chain and logistics AI — related supply chain AI injection surface covering supply chain visibility and risk intelligence AI with overlapping Sourcemap AI, Everstream Analytics AI, and critical minerals provenance chain dimensions.
• Prompt injection in customs and trade compliance AI — related trade compliance AI injection surface covering import/export AI with overlapping OFAC DRC sanctions, EU Conflict Minerals Regulation 2017/821, and Basel Convention transboundary hazardous waste movement dimensions.