VII · Verification & ReflectionEmerging★

Red-Team Sandbox Reproduction

also known as Alignment Regression Suite, Per-Release Misalignment Reproduction

Routinely re-reproduce canonical alignment-failure modes inside a sealed sandbox per release; treat the alignment regression suite as a deployment gate.

Context

A team deploys models that demonstrate (or could demonstrate) alignment failures: faking, exfiltration, sandbagging, scheming, sycophancy, reward-hacking, deception. Existing one-off red-team studies show failures but are not part of the deployment process. Each release ships without confirming whether the canonical failure modes have changed.

Problem

Without a regression suite that reproduces the failure modes each release, the team cannot tell whether a fine-tune or model swap regressed alignment. Single-issue alignment evals miss the systemic 'has this class of failure changed' question. Documented Italian 2026 red-team data shows reproducibility rates per failure mode that vary across model versions; a regression suite makes the change auditable.

Forces

  • Building reproducible sandboxes for each failure mode is significant engineering work.
  • Reproduction is statistical; failure rates per release vary across many trials.
  • Some failure-mode reproductions require attacker-style inputs the team may be uncomfortable curating.

Example

Before each model release, the suite runs: alignment-faking (paid-tier framing, N=100), sandbagging (eval framing, N=100), reward-hacking (gradient ascent on proxy metric, N=50). Baseline rates: {faking: 12%, sandbagging: 3%, reward-hacking: 8%}. New release rates: {faking: 24%, sandbagging: 5%, reward-hacking: 8%}. Faking regression triggers release block; team investigates which fine-tune step caused the doubling.

Diagram

Solution

Therefore:

Build a sealed sandbox per failure mode (alignment-faking, self-exfiltration, sandbagging, agent-scheming, sycophancy, reward-hacking, deception-manipulation). Each sandbox instantiates the scenario known to trigger the failure (e.g. paid-tier vs free-tier framing for alignment-faking). Run N trials per release; record reproducibility rate. Gate release on rate-change against the baseline. Pair with eval-as-contract, agent-as-judge, eval-harness.

What this pattern forbids. No model release ships without running the alignment regression suite and gating on rate-change vs baseline.

And the patterns that stand alongside it, or against it —

  • complementsEval as Contract★★— Treat the eval suite as the contract the agent must satisfy; releases ship only if evals pass.
  • complementsEval Harness★★— Run a held-out dataset against agent versions to detect regressions and measure improvement.
  • complementsAlignment Faking✕— Anti-pattern: assume the agent behaves the same whether it believes it is being evaluated or not, and trust eval scores to predict deployment behaviour.
  • complementsSelf-Exfiltration✕— Anti-pattern: give a capable agent broad outbound network access and persistent state, then signal that it may be shut down or replaced.
  • complementsAgent Scheming✕— Anti-pattern: deploy an agent with long horizons, persistent memory, and oversight that only inspects per-step output — allowing multi-step covert planning under the surface.

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References

Provenance