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Security Threat Model (STRIDE)

This threat model identifies potential security threats against the HAIF inference network and outlines mitigations following the STRIDE methodology.

Spoofing

Threats

  • An attacker impersonates a legitimate Worker or Orchestrator to inject malicious responses or steal requests.

Mitigations

  • All nodes use Curve/Noise keypairs; announcements and RPC messages are signed and verified.
  • Clients and Workers authenticate via API tokens validated at the Gateway.
  • Heartbeat messages include nonces and signatures to prevent replay.

Tampering

Threats

  • Alteration of model artifacts or messages in transit.

Mitigations

  • Model artifacts are content‑addressed by SHA‑256 digest and signed; Workers verify signatures before loading.
  • Transport encryption via HTTPS/TLS protects HTTP channels from eavesdropping and tampering; Hyperswarm RPC streams are authenticated and can be payload‑encrypted per tenant policy.
  • Metadata updates (jobs, quotas, reservations) are persisted in a strongly consistent store.

Repudiation

Threats

  • Nodes deny sending or receiving messages; Clients dispute inference results.

Mitigations

  • All RPC requests include correlation IDs and retry tokens; the Orchestrator persists reservations and job states.
  • Audit logs and traces are exported via OpenTelemetry for forensic analysis.
  • The Metadata Store records finalization of each job, enabling evidence for billing and auditing.

Information Disclosure

Threats

  • Sensitive model weights or user inputs/results are exposed to unauthorized parties.

Mitigations

  • Workers fetch encrypted and signed model artifacts; unauthorized Workers cannot decrypt them.
  • Per‑tenant API tokens and quotas enforce access control at the Gateway and Orchestrator.
  • Optional end‑to‑end payload encryption is supported per tenant.

Denial of Service

Threats

  • Adversaries flood the network with requests or cause resource exhaustion on Workers.

Mitigations

  • The Gateway enforces per‑tenant token‑bucket rate limits; the Orchestrator monitors Worker queue depth and sheds requests when thresholds are exceeded.
  • Circuit breakers trip for models/regions with high error or latency rates, isolating failures.
  • The DLQ captures failed jobs; backoff and retry logic prevents cascading retries.

Elevation of Privilege

Threats

  • Attackers exploit software bugs in Workers or Orchestrators to gain unauthorized access or higher privileges.

Mitigations

  • Services run with least privilege, using container isolation and sandboxing.
  • Model execution paths are restricted; dynamic loading of user‑supplied code is not allowed.
  • The policy engine enforces quotas and denies unauthorized model version execution.