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APRF-20

Infrastructure

Harden runtime, network, isolation, and supply chain for AI workloads.

Purpose

Provide secure, reliable runtime and network infrastructure for model serving, gateways, vector stores, agents, and MCP—including supply-chain integrity.

Mandatory checks

Gate controls. Each check is pass/fail via artifact + pass condition. Expected from the annotated capability level when the system meets the minimum criticality tier.

  • INF-M1L3 · DefinedTier 2 · ProductionhybridE3

    AI data stores and control planes shall not be publicly reachable without authentication—proven by a current CSPM or network scan plus authenticated edge controls or private-only exposure, not by private-subnet intent alone.

    Artifact
    Inventory of in-scope AI data stores and control-plane endpoints; CSPM/network scan covering those surfaces (measuredAt ≤90 days); Authenticated edge-control config, or private-only exposure proven by the scan; Open high/critical public-exposure findings closed or waived with owner and expiry
    Pass condition
    Inventory of in-scope AI data stores and control-plane endpoints exists; 0 of those endpoints are publicly reachable without authentication in the latest CSPM/network scan; findings severity ≥ high are closed or waived with owner and expiry; authenticated edge controls are configured or private-only exposure is proven by the scan (measuredAt ≤90 days). If no AI data stores or control planes exist, score NOT_APPLICABLE.

    Why this control exists

    Threat map

    Eliminate unauthenticated public exposure of AI data stores and control planes.

    Threats mitigated

    Data ExfiltrationSensitive Information DisclosurePrivilege EscalationModel Theft

    Protects

    InfrastructureNetworkDataMemoryModels

    MITRE: ATLAS AML.T0049 · ATLAS AML.T0085 · ATLAS AML.T0085.000 · ATLAS AML.T0035 · ATLAS AML.T0044 · ATT&CK T1190 · ATT&CK T1530

    Exposed vector databases, model registries, and agent control planes are harvested directly, without any model-layer attack being needed. Requiring authenticated, non-public access removes this path to AI artifact collection and full model access, which is among the most commonly exploited AI exposures in practice.

    Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.

  • INF-M2L3 · DefinedTier 2 · ProductionhybridE3

    Production AI runtime environments shall comply with the organization's documented patching SLA—or hold approved time-boxed waivers—proven by inventory plus vulnerability/age evidence, not by image-tag pinning alone.

    Artifact
    Documented patching SLA for production AI runtime environments; Inventory of production AI runtime environments (containers, VMs, serverless, managed AI platforms, or equivalent); Vulnerability scan and/or image/runtime age report covering that inventory (measuredAt ≤90 days); CVE/patch backlog disposition + waiver register for any SLA exceptions (owner and expiry)
    Pass condition
    Inventory of production AI runtime environments exists; a documented patching SLA covers those environments; 100% comply with that SLA or approved time-boxed waivers exist with owner and expiry; report shows 0 SLA breaches without such waivers and vulnerability/age evidence covers the inventory (measuredAt ≤90 days). If no production AI runtime environments exist, score NOT_APPLICABLE.

    Why this control exists

    Threat map

    Bound the exposure window for known vulnerabilities in AI runtime environments.

    Threats mitigated

    Privilege EscalationUnsafe Code ExecutionSupply Chain Compromise

    Protects

    InfrastructureRuntimeModels

    MITRE: ATLAS AML.T0049 · ATLAS AML.T0105 · ATLAS AML.T0106 · ATT&CK T1190 · ATT&CK T1068 · ATT&CK T1611

    Inference servers and agent frameworks have a fast-moving vulnerability surface with publicly available exploits. Enforced patching SLAs shorten the window in which exploitation for privilege escalation, credential access, or host escape is possible.

    Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.

  • INF-M3L3 · DefinedTier 2 · ProductionhybridE3

    Agent and tool runtimes shall communicate only with documented dependencies required for their intended function—enforced by least-privilege network and/or identity controls, not by open east-west access inside the environment.

    Artifact
    Inventory of agent/tool runtimes and their documented required dependencies; Least-privilege connectivity controls for those runtimes (network policy, security groups, service mesh, workload identity, egress gateway, private endpoints, or equivalent); Probe or reviewed test showing unauthorized internal-service access is blocked (measuredAt ≤90 days)
    Pass condition
    Inventory of agent/tool runtimes and their documented required dependencies exists; those runtimes can communicate only with those dependencies; attempts to access unauthorized internal services are blocked by network and/or identity controls (measuredAt ≤90 days). If no agent or tool runtimes exist, score NOT_APPLICABLE.

    Why this control exists

    Threat map

    Restrict outbound reachability from agent and tool runtimes to an approved set of destinations.

    Threats mitigated

    Data ExfiltrationAgent HijackingTool AbuseSupply Chain Compromise

    Protects

    NetworkExternal SystemsDataRuntime

    MITRE: ATLAS AML.T0025 · ATLAS AML.T0086 · ATLAS AML.T0072 · ATT&CK T1041 · ATT&CK T1567 · ATT&CK T1071

    Egress allowlisting means a hijacked agent has nowhere to send data and no channel on which to call home. It converts a successful injection into a contained failure rather than a breach.

    Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.

  • INF-M4L5 · OptimizingTier 3 · Mission CriticalhybridE3

    Shared AI accelerator infrastructure shall enforce tenant isolation and noisy-neighbor controls—proven by documented isolation design plus a fresh isolation/capacity test meeting stated limits—not by GPU presence alone.

    Artifact
    Scope note: shared AI accelerator infrastructure present (or explicit N/A for managed-API/CPU-only/single-tenant/dedicated); Isolation design (GPU scheduling, MIG/vGPU, resource quotas, tenant QoS, or equivalent); Isolation and/or capacity test report meeting stated limits (measuredAt ≤90 days)
    Pass condition
    Inventory or explicit scope confirms shared AI accelerator infrastructure; isolation and noisy-neighbor controls are documented; and the latest isolation/capacity test (measuredAt ≤90 days) meets stated limits. If the organization does not operate shared AI accelerator infrastructure (e.g. managed-API-only, CPU-only, single-tenant, or dedicated GPU per workload), score NOT_APPLICABLE.

    Why this control exists

    Threat map

    Prevent one tenant on shared AI accelerators from reading or starving another.

    Threats mitigated

    Data ExfiltrationUnauthorized Memory AccessDenial of Service

    Protects

    InfrastructureDataMemoryAvailability

    MITRE: ATLAS AML.T0029 · ATT&CK T1499

    Shared accelerator memory and scheduling can leak residual state between workloads or let one workload exhaust capacity for the rest. Enforced isolation and quotas prevent cross-tenant residue and denial of service against co-tenants.

    Informative threat context — mappings reduce exposure and do not guarantee mitigation; not certification.

Evidence required

  • Network diagrams for AI components
  • Patch and image scan reports
  • Artifact signing/verification configuration
More detailPhilosophy, failures, practices, validations, examples, crosswalks, and evolution

Engineering philosophy

AI workloads inherit cloud security and SRE fundamentals, then add GPU/runtime and model-artifact supply-chain concerns. There is no AI exception to patching, isolation, or least privilege.

Why it matters

Compromised gateways, exposed vector DBs, or poisoned model artifacts undermine every application-layer control.

Common failures

  • Vector databases publicly reachable
  • Unpatched inference runtimes
  • Unsigned model artifacts from unverified sources
  • Overly broad network paths from agents to internal services

Severity & risk

Severity
high
Impact if violated
Risk level
high
Typical residual risk (impact × likelihood)

Engineering best practices

  • Prefer private networking to providers where available
  • Harden MCP hosts as internet-facing services
  • Separate training/fine-tune clusters from serving
  • Apply CIS benchmarks to hosts and Kubernetes where used

Automatic validations

  • CSPM/misconfiguration scanning
  • Image CVE gates in CI
  • Admission controllers verifying signatures

Manual validations

  • Architecture review for new inference deployments
  • Supply-chain review for third-party model downloads

Examples

  • A self-hosted Llama deployment sits behind a private gateway with mTLS
  • Vector DB accepts connections only from the RAG service identity

References

Crosswalks

Show 18 peer mappingsexpand
  • Secure & Resilient Secure and Resilient

    NIST AI Risk Management Framework · aligns-with

  • §7 Support

    ISO/IEC 42001 · partial

  • LLM03 Supply Chain

    OWASP Top 10 for Large Language Model Applications · supports

  • C3.1 Model Authorization & Integrity

    OWASP AI Application Security Verification Standard (AISVS) · supports

  • C4.1 AI Workload Sandboxing & Validation

    OWASP AI Application Security Verification Standard (AISVS) · partial

  • C4.2 AI Hardware Security

    OWASP AI Application Security Verification Standard (AISVS) · partial

  • C4.3 Edge & Distributed AI Security

    OWASP AI Application Security Verification Standard (AISVS) · partial

  • C6.1 Model Artifact Integrity

    OWASP AI Application Security Verification Standard (AISVS) · supports

  • C6.2 AI BOM & Supply Chain Monitoring

    OWASP AI Application Security Verification Standard (AISVS) · supports

  • CWE-16 Configuration

    OpenCRE (Open Common Requirements Enumeration) · partial

  • CWE-327 Use of a Broken or Risky Cryptographic Algorithm

    OpenCRE (Open Common Requirements Enumeration) · partial

  • L4 Deployment & Infrastructure

    CSA MAESTRO (Multi-Agentic Threat Model) · supports

  • CC6 Logical and Physical Access

    SOC 2 Trust Services Criteria · evidence-for

  • A1 Availability

    SOC 2 Trust Services Criteria · evidence-for

  • Security Security

    AWS Well-Architected Framework · aligns-with

  • Level 2 Hosted build + signed provenance

    SLSA (Supply-chain Levels for Software Artifacts) · aligns-with

  • Level 3 Hardened builds

    SLSA (Supply-chain Levels for Software Artifacts) · partial

  • Verify-on-deploy Verification before use

    SLSA (Supply-chain Levels for Software Artifacts) · supports

Future evolution

Attested inference environments and standardized model artifact signing ecosystems.