Secrets vs Capabilities: Token Design in Microservices

Monthly research note. Theme: Cryptographic Infrastructure.

TL;DR

A focused memo on Secrets vs Capabilities: Token Design in Microservices: define the model, state the properties, then design the system so those properties remain true under failure and adversaries.

Key takeaways

• Rotation and rollback are core features—design them before you ship.
• Audit logs are evidence: make them tamper-evident and queryable during incidents.
• Side-channel constraints turn performance details into security boundaries.
• Bind security decisions to evidence (audit, invariants, telemetry).
• Automate guardrails; humans are for judgment, not for consistent enforcement.

Why this matters

• Auditability must not become a secret-leaking logging pipeline.
• Most organizations don’t know where their keys live—until an incident.
• Operational reality (rotation, audit, rollback) is where crypto systems fail.
• Side channels turn performance details into security boundaries.

Key questions

• How do keys rotate safely (overlap windows, dual-sign, staged rollout)?
• What is the rollback plan when a new algorithm breaks production?
• How do you separate duties (operators vs developers vs security responders)?
• What is the root of trust (HSM, TPM, offline CA, threshold ceremony)?
• What is your disaster recovery story for KMS/HSM outages?
• How do you handle key erasure and “right to be forgotten” constraints?

Assumptions

• Certificate chains and policies evolve; clients won’t all update together.
• Attackers can observe timing and resource usage in shared environments.
• Secrets leak through logs, metrics, crash dumps, and backups unless prevented.
• Key usage is high-volume; audit pipelines must scale without sampling away truth.

Non-goals

• Designing audit trails that expose sensitive plaintext or identifiers.
• Relying on manual rotation procedures for fleet-scale systems.

Model & invariants

A practical safety statement for key usage is least authority:

Assume compromise and design for recovery: rotation, revocation, and forensics.

Bind every derived key to context: protocol, role, version, and transcript.

Security properties

• Integrity: invalid transitions are rejected (and detectable).
• Downgrade resistance: negotiation can’t silently weaken security posture.
• Least authority: privileges are scoped by purpose and time.
• Replay resistance: duplicated inputs do not change outcomes.

Failure modes

• Recovery paths that only work when nothing is broken.
• Mixed-version behavior that violates assumptions silently.
• Timeout ambiguity causing double-apply or partial state transitions.
• Observability gaps during incidents (missing evidence).

Design sketch

flowchart TD
  gen["KeyGen (HSM/KMS)"] --> use["Use (TLS/VPN/Signing)"]
  use --> rot["Rotate (policy + automation)"]
  rot --> revoke["Revoke (incident)"]
  revoke --> audit["Audit/Forensics"]
  audit --> gen

Implementation notes

Crypto infra is a product: UX, policy, audit, and rollback must compose.

// Capability-style API: callers get a handle scoped to purpose + TTL.
type KeyPurpose string
type KeyHandle struct {
  ID string
  Purpose KeyPurpose
  ExpiresAtUnix int64
}

type Signer interface {
  Sign(h KeyHandle, msg []byte) (sig []byte, err error)
}

Verification strategy

• Chaos for KMS: inject throttling, partial outages, and latency spikes.
• Constant-time validation: microbenchmarks + side-channel tooling where feasible.
• Misuse resistance tests: wrong purpose, wrong context, wrong key type must fail.
• Rotation drills: staged rollout, dual-sign windows, and rollback.
• Forensics tests: can you reconstruct “who signed what” under load?

Operational notes

• Separate duties and restrict production key access paths.
• Automate rotation with safety rails (canary, dual-sign, fast rollback).
• Make audit streams append-only and queryable during incidents.
• Inventory keys and usage paths; treat unknown usage as an incident.
• Test backup/restore for crypto material with the same rigor as databases.

What to monitor

• Retry/timeout rates by endpoint and client cohort.
• Invariant violation rate (should be ~0).
• Error budget burn + tail latency under load.
• Authz failures and policy denials (unexpected spikes).
• Rollback events and the conditions that triggered them.

Rollback plan

• Define an explicit rollback trigger (metrics + thresholds).
• Keep dual-write / dual-verify windows where appropriate.
• Use canaries and staged rollout; stop early when signals degrade.
• Preserve evidence (configs, artifacts, audit logs) to reconstruct what changed.
• Prefer backward-compatible changes; avoid “flag day” upgrades.

Evidence

• Let's Encrypt Incident Reports (1) — Real-world PKI incidents and operational lessons.
  • Evidence: Rotation and revocation are operational protocols; extract failure patterns into drills and automated rollbacks.
• RFC 8446: TLS 1.3 (2) — Modern handshake design, key schedule, and downgrade resistance patterns.
  • Evidence: Handshake transcript binding and downgrade resistance patterns; monitor negotiation paths and failure reasons.

Open questions

• How do you guarantee that audit does not become a data exfiltration channel?
• What is your plan for emergency revocation at global scale?
• Which secrets must remain confidential for 10+ years and where are they stored today?
• What would a KMS compromise look like in your telemetry?

Checklist

• Failure modes enumerated with mitigations.
• Assumptions listed and reviewed.
• Rollback plan rehearsed and automated.
• Telemetry captures correctness signals.
• Safety properties stated as invariants.
• Costs bounded (CPU/memory/bandwidth) under adversarial inputs.

Further reading

• NIST SP 800-57 Part 1 Rev. 5 — Key management guidance: lifecycle, strength, and policy.
• RFC 8446: TLS 1.3 — Modern handshake design, key schedule, and downgrade resistance patterns.
• Let's Encrypt Incident Reports — Real-world PKI incidents and operational lessons.
• RFC 5869: HKDF — Domain separation and key derivation done sanely.
• Learn TLA+ — Practical entry point for specification and model checking.
• Designing Data-Intensive Applications (Kleppmann) — The systems-engineering baseline for correctness, replication, and failure.