Side Channels: Constant-Time, Cache Attacks, and Real Threat Models

Monthly research note. Theme: Cryptographic Infrastructure.

TL;DR

A focused memo on Side Channels: Constant-Time, Cache Attacks, and Real Threat Models: define the model, state the properties, then design the system so those properties remain true under failure and adversaries.

Key takeaways

• Audit logs are evidence: make them tamper-evident and queryable during incidents.
• Treat key IDs as capabilities; never pass raw private key material across boundaries.
• Rotation and rollback are core features—design them before you ship.
• Write assumptions down; treat them as interfaces.
• Define safety properties before performance goals.

Why this matters

• Key management failures are systemic: the breach is “a workflow,” not a bug.
• Policy drift silently turns strong crypto into weak practice.
• Most organizations don’t know where their keys live—until an incident.
• Side channels turn performance details into security boundaries.

Key questions

• What is the rollback plan when a new algorithm breaks production?
• What is the blast radius of compromise (tenant, service, region, environment)?
• How do keys rotate safely (overlap windows, dual-sign, staged rollout)?
• What is your disaster recovery story for KMS/HSM outages?
• How do you separate duties (operators vs developers vs security responders)?
• What is the root of trust (HSM, TPM, offline CA, threshold ceremony)?

Assumptions

• Key usage is high-volume; audit pipelines must scale without sampling away truth.
• Secrets leak through logs, metrics, crash dumps, and backups unless prevented.
• Attackers can observe timing and resource usage in shared environments.
• Some environments are hostile (CI, ephemeral runners, shared build agents).

Non-goals

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

Model & invariants

Key derivation is where protocols quietly succeed or fail. A sane default is domain-separated HKDF:

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

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

Security properties

• Replay resistance: duplicated inputs do not change outcomes.
• Evidence: critical actions emit verifiable audit events.
• Downgrade resistance: negotiation can’t silently weaken security posture.
• Integrity: invalid transitions are rejected (and detectable).

Failure modes

• Timeout ambiguity causing double-apply or partial state transitions.
• Observability gaps during incidents (missing evidence).
• Config drift that weakens security posture over time.
• Mixed-version behavior that violates assumptions silently.

Design sketch

flowchart LR
  policy["Policy (purpose + TTL)"] --> service["Signer Service"]
  service --> hsm["HSM/KMS"]
  service --> audit["Audit Stream"]
  audit --> siem["Detection/Response"]

Implementation notes

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

#[derive(Clone, Copy, Debug)]
pub enum Purpose { Tls, Jwt, Firmware, Ledger }

pub struct KeyHandle { id: String, purpose: Purpose }

// Enforce purpose and algorithm policy at the boundary, not in the caller.

Verification strategy

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

Operational notes

• Automate rotation with safety rails (canary, dual-sign, fast rollback).
• Separate duties and restrict production key access paths.
• Alert on policy drift: cipher suites, key sizes, algorithm toggles, TTL changes.
• Make audit streams append-only and queryable during incidents.
• Test backup/restore for crypto material with the same rigor as databases.

What to monitor

• Authz failures and policy denials (unexpected spikes).
• Invariant violation rate (should be ~0).
• Admission-control / rate-limit rejections (by reason).
• Retry/timeout rates by endpoint and client cohort.
• Rollback events and the conditions that triggered them.

Rollback plan

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

Evidence

• Designing Data-Intensive Applications (Kleppmann) (1) — The systems-engineering baseline for correctness, replication, and failure.
  • Evidence: Replication and consistency tradeoffs as engineering constraints; use as reference when naming guarantees.
• Site Reliability Engineering (Google) (2) — Error budgets, incident response, and reliability as an engineering discipline.
  • Evidence: Error budgets and incident response are correctness controls; tie monitoring and rollback triggers to SLO burn.

Open questions

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

Checklist

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

Further reading

• 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.
• NIST SP 800-57 Part 1 Rev. 5 — Key management guidance: lifecycle, strength, and policy.
• Designing Data-Intensive Applications (Kleppmann) — The systems-engineering baseline for correctness, replication, and failure.
• Site Reliability Engineering (Google) — Error budgets, incident response, and reliability as an engineering discipline.