Runtime Security: eBPF, Policy, and Drift Detection

Monthly research note. Theme: DevSecOps & Resilience Engineering.

TL;DR

A focused memo on Runtime Security: eBPF, Policy, and Drift Detection: define the model, state the properties, then design the system so those properties remain true under failure and adversaries.

Key takeaways

• Make rollback a first-class operation with explicit triggers and rehearsal.
• Treat CI/CD as attacker-controlled until proven otherwise; minimize secrets and privileges.
• Policy-as-code needs tests, rollout, and rollback like any other production system.
• Make failure modes explicit and observable.
• Measure correctness signals, not only latency/throughput.

Why this matters

• Rollouts are where incidents happen; safe rollback is a security feature.
• Reproducibility is how you know what you shipped is what you built.
• Runtime security needs evidence pipelines, not just dashboards.
• Secrets in CI turn “one compromised job” into “full compromise.”

Key questions

• Which signals prove correctness (not just availability) in production?
• Where do you enforce policy (pre-merge, build, deploy, runtime)?
• How do you do safe rollouts (canary, blast-radius, rapid rollback)?
• How do you manage secrets without long-lived credentials in CI?
• How do you rehearse incident response as code (runbooks, chaos, drills)?
• How do you prevent “break glass” from becoming the standard path?

Assumptions

• Rollbacks must be executed under time pressure.
• Policy enforcement must be consistent across environments.
• Dependencies can be compromised upstream (typosquatting, maintainer takeover).
• CI runners are exposed to untrusted code (PRs, dependencies).

Non-goals

• Trusting CI environments by default.
• Long-lived credentials embedded in pipelines.

Model & invariants

Build provenance is a cryptographic statement:

Make provenance verifiable: “what built this” must be cryptographically bound.

Treat CI as attacker-controlled until proven otherwise; minimize secrets and privileges.

Security properties

• Least authority: privileges are scoped by purpose and time.
• Integrity: invalid transitions are rejected (and detectable).
• Replay resistance: duplicated inputs do not change outcomes.
• Evidence: critical actions emit verifiable audit events.

Failure modes

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

Design sketch

flowchart LR
  src["Source"] --> build["Build (reproducible)"]
  build --> attest["Attestation"]
  attest --> scan["SAST/DAST/SCA"]
  scan --> deploy["Deploy (policy gates)"]
  deploy --> runtime["Runtime Policy + Observability"]

Implementation notes

The pipeline is production: it has credentials, network reach, and authority.

CI hardening checklist:
- No long-lived secrets in CI
- OIDC to obtain short-lived creds
- Pin dependencies and verify integrity
- Reproducible builds + provenance attestation
- Policy-as-code gates (deploy blocked on evidence)

Verification strategy

• Dependency tampering drills: lockfile changes, integrity failures.
• Policy tests: unit tests for policy-as-code rules.
• Pipeline attack simulations: compromise a runner and measure blast radius.
• Runtime conformance: detect drift between desired and actual state.
• Rollback tests as part of release (not “if needed”).

Operational notes

• Treat policy changes as security-sensitive deploys (review + rollout).
• Keep a provenance trail for every artifact deployed to production.
• Audit who can ship and how; remove implicit paths.
• Continuously scan and inventory dependencies; prioritize by exposure.
• Rehearse incident response for the pipeline itself.

What to monitor

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

Rollback plan

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

Evidence

• Site Reliability Engineering (Google) (1) — 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.
• Jepsen (2) — Fault injection and correctness testing for distributed systems.
  • Evidence: Turn faults into test cases; prioritize partition and clock-skew scenarios that violate user-visible guarantees.

Open questions

• How quickly can you revoke all pipeline credentials in an incident?
• Can you answer “what code is running” with cryptographic evidence?
• What is the smallest CI compromise that becomes a prod compromise today?
• Which deploy actions are irreversible and how do you mitigate that?

Checklist

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

Further reading

• in-toto — Securing the integrity of software supply chains with attestations.
• Sigstore — Signing and verifying artifacts at scale with transparency logs.
• NIST SP 800-218 (SSDF) — Secure software development practices as an engineering framework.
• SLSA v1.0 Specification — Supply-chain levels and provenance requirements.
• Site Reliability Engineering (Google) — Error budgets, incident response, and reliability as an engineering discipline.
• Jepsen — Fault injection and correctness testing for distributed systems.