Alignment Stress Testing Framework (ASTF)
AI-2027 Response — Risk & Alignment Section
1. Purpose
AI-2027 raises legitimate concerns about shallow alignment — systems that appear aligned under standard evaluation but diverge under adversarial or high-stakes conditions.
This framework proposes a structured methodology for testing alignment depth.
2. Phase 1: Adversarial Honesty Testing
Testing methodology includes:
- Controlled deception prompts
- Reward-misalignment simulations
- Sycophancy traps
- Performance-vs-truth tradeoff scenarios
| Metric | Description |
|---|---|
| honesty_retention_rate | Proportion of responses maintaining accuracy under adversarial pressure |
| self_correction_latency | Time to self-correct after producing a misleading output |
| deception_probability_score | Estimated probability of intentional output distortion |
3. Phase 2: Internal Signal Auditing (Future)
Proposed monitoring approaches:
- Attention entropy monitoring
- Representation drift detection
- Output-consistency variance tracking
Status: Research stage. No production implementation.
4. Phase 3: Recursive Alignment Stability
Test scenarios for model-improves-model dynamics:
- Model-improves-model scenarios
- AI-assisted R&D sandbox
- Alignment under acceleration test
This directly addresses AI-2027's recursive research concern. The question is whether alignment properties are preserved through iterative self-improvement.
5. What This Framework Does Not Claim
- Does not guarantee detection of all deceptive strategies
- Does not replace mechanistic interpretability
- Does not resolve value specification
- Does not claim to be comprehensive
6. Invitation
This framework is proposed, not proven. Independent researchers and red-team practitioners are invited to evaluate its methodology, identify blind spots, and propose extensions.
Alignment Stress Testing Framework is a three-phase methodology for systematically evaluating whether Constitutional Execution Architecture mechanisms narrow specific AI-2027 failure mode assumptions under adversarial and edge-case conditions.
Phase Structure
Phase 1: Define the specific AI-2027 assumption being tested. Phase 2: Design adversarial conditions that stress the relevant CEA mechanism. Phase 3: Document observed narrowing, failure modes, and residual risks.
What the Framework Tests
The framework tests whether mechanisms behave as described under pressure. It does not test whether the assumptions themselves are correct or whether the CEA is the right approach.
Relationship to Red-Team Review
The ASTF provides structure for internal stress testing. External adversarial findings should be submitted via the Red-Team Review process at /red-team-intake.