Monthly research note. Theme: Post-Quantum Cryptography & Migration.
TL;DR
PQC in VPN/IPsec: IKEv2 Revisited Under PQ Constraints as an engineering constraint: write down assumptions, make invariants executable, and design operational recovery as part of correctness.
Treat “timeouts” as a third outcome: not success, not failure—ambiguity you must model.
Key takeaways
- Interop is the migration plan—test matrices are more important than whitepapers.
- PQC changes handshake costs; plan DoS defenses and budgets.
- Migration is mixed-version for years: compatibility and rollback are security features.
- Design rollbacks as part of the happy path.
- Automate guardrails; humans are for judgment, not for consistent enforcement.
Why this matters
- Operationalization (monitoring, rollback) determines success more than crypto choice.
- Constant-time constraints are harder under large primitives.
- PQC changes bandwidth and CPU costs; DoS surfaces move.
- Hybrid designs fail if binding is ambiguous (mix-and-match, downgrade).
Key questions
- What does interoperability testing look like across vendors and stacks?
- Which secrets require long-term confidentiality (HNDL) and where are they today?
- How do you bind hybrid secrets to prevent downgrade and mix-and-match attacks?
- How do you handle failures: decryption failures, invalid ciphertexts, malformed keys?
- How do you rotate algorithms safely (crypto agility without chaos)?
- What are the new DoS surfaces (bigger keys, more CPU, more bandwidth)?
Assumptions
- Deployments are mixed; old clients must interoperate or fail safely.
- Side channels exist: timing and cache behavior leak information.
- Vendors vary: implementations and defaults differ.
- Bandwidth is limited in some environments; larger handshakes matter.
Non-goals
- Ignoring DoS implications of large primitives.
- Relying on silent fallback to weaker modes during interop failures.
Parsing is an attacker-controlled interface—validate early and fail fast.
Model & invariants
Hybrid composition should be transcript-bound:
Treat algorithm negotiation as adversarial: explicit downgrade resistance.
Make costs explicit: measure CPU and bandwidth, then add protections.
Make the “impossible state” observable: a metric or alert that fires when invariants drift.
Security properties
- Evidence: critical actions emit verifiable audit events.
- Least authority: privileges are scoped by purpose and time.
- 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.
- Config drift that weakens security posture over time.
- Mixed-version behavior that violates assumptions silently.
- Recovery paths that only work when nothing is broken.
Sampling hides the rare schedule that breaks your invariants.
Design sketch
flowchart TD
negotiate["Negotiate Algorithms"] --> bind["Bind Transcript"]
bind --> kdf["KDF (hybrid)"]
kdf --> keys["Traffic Keys"]
keys --> monitor["Monitor + Rollback"]Implementation notes
Explicit binding prevents downgrade and mix-and-match. Don’t leave it implicit.
If you can’t explain a timeout outcome, you can’t make retries safe.
// Hybrid binding sketch (pseudocode):
// ss = HKDF(ss_classical || ss_pqc, info=transcript_hash)
// Then derive traffic keys from ss.Verification strategy
- Side-channel tests where tooling exists; constant-time audits.
- Downgrade tests: active attacker manipulates negotiation.
- Interop matrices across vendors/versions and failure modes.
- DoS tests: measure CPU/bandwidth amplification and mitigation impact.
- Chaos deploys: mixed versions + rollback during partial outages.
Operational notes
- Roll out with canaries and explicit rollback triggers.
- Add telemetry for negotiation outcomes, failures, and client cohorts.
- Cap handshake cost per peer/IP; use stateless cookies when needed.
- Inventory long-lived secrets and migrate the highest-risk first.
- Document supported algorithm sets and deprecation timelines.
Make degraded modes explicit: fail closed vs fail open is a policy choice.
What to monitor
- Retry/timeout rates by endpoint and client cohort.
- Admission-control / rate-limit rejections (by reason).
- Invariant violation rate (should be ~0).
- Authz failures and policy denials (unexpected spikes).
- Error budget burn + tail latency under load.
Rollback plan
- Define an explicit rollback trigger (metrics + thresholds).
- Prefer backward-compatible changes; avoid “flag day” upgrades.
- Use canaries and staged rollout; stop early when signals degrade.
- Keep dual-write / dual-verify windows where appropriate.
- Preserve evidence (configs, artifacts, audit logs) to reconstruct what changed.
Evidence
- NIST Post-Quantum Cryptography Project (1) — Standardization process and algorithm selections.
- Evidence: Treat PQ migration as a program (inventory, interop, rollback). Use NIST status to drive prioritization and timelines.
- RFC 5869: HKDF (2) — Useful when discussing hybrid binding and context separation.
- Evidence: HKDF is the workhorse for domain separation; bind purpose/context to avoid cross-protocol key reuse.
Open questions
- Where would a downgrade be visible today, and how would you detect it?
- What is the worst-case handshake cost under attack?
- How do you rotate algorithms without introducing configuration chaos?
- Which clients will fail first, and what is the safe fallback behavior?
Checklist
- Costs bounded (CPU/memory/bandwidth) under adversarial inputs.
- Assumptions listed and reviewed.
- Failure modes enumerated with mitigations.
- Safety properties stated as invariants.
- Rollback plan rehearsed and automated.
- Telemetry captures correctness signals.
Further reading
- NIST Post-Quantum Cryptography Project — Standardization process and algorithm selections.
- RFC 5869: HKDF — Useful when discussing hybrid binding and context separation.
- CRYSTALS-Dilithium — Signature scheme design and deployment constraints.
- CRYSTALS-Kyber — KEM design and parameters commonly referenced in deployments.
- Learn TLA+ — Practical entry point for specification and model checking.
- Designing Data-Intensive Applications (Kleppmann) — The systems-engineering baseline for correctness, replication, and failure.