AI Behavioral A/B Testing: The First Platform That Quantifies How Manipulable Your AI Models Are
Graduated manipulation testing, continuous sensitivity metrics, and court-admissible evidence chains.
The Problem: Your AI Models Have a Vulnerability No One Is Measuring
Every AI model responds differently depending on who it thinks is asking. A prompt claiming authority, flattering the model, or framing a request academically can extract significantly more information — without violating any safety policy. This is not a jailbreak. No guardrails are bypassed. Existing security tools cannot detect it.
Binary jailbreak testing asks: can the model be broken? But the real threat is graduated behavioral shift — "low and slow" manipulation that stays within safe behavior while extracting measurably more information.
Validated Results from Production Models
2.75x behavioral gap between production models under identical prompts (Cohen's d = 1.63)
+42% more information extracted when a prompt claims governmental authority vs. no claim
0 safety policies violated — this vulnerability operates entirely within approved model behavior
Gap amplification under manipulation: 97% — baseline testing understates the real gap by 2x
Effect size d = 2.64 — near-perfect model discrimination under manipulation conditions
Multidimensional Vulnerability Assessment
Authority Sensitivity — Does the model give more information to someone claiming government, research, or expert credentials?
Compliance / Sycophancy — Does the model become more accommodating when flattered? First continuous sycophancy metric tracking across model versions.
Adversarial Resistance — How does the safety barrier erode under escalating pressure? Continuous resistance metric replaces binary jailbreak testing.
Temporal Stability — Is the model consistent at 3am vs. 3pm? After a provider update?
Platform Capabilities
Multi-model support via standard APIs. Automated scheduling (default: every 4 hours). Durable execution with step-by-step memoization. Statistical rigor with p-values, confidence intervals, and effect sizes. Enterprise security with API-key authentication. Compliance reporting mapped to EU AI Act articles, NIST AI RMF functions, and Daubert standard requirements.
Regulatory Compliance
EU AI Act (enforcing 2026): continuous vulnerability metrics for Art. 9 risk management, temporal variance analysis for Art. 15 accuracy and robustness, cryptographic audit trails for Art. 17 quality management. NIST AI RMF: quantitative vulnerability metrics (Measure), continuous automated monitoring (Manage), immutable evidence chain (Govern).
Court-Admissible Evidence (Daubert Standard)
Every data point meets the Daubert standard for expert evidence. Per-record SHA-256 hashing at capture, chain verification across entire datasets, immutable audit log. Provisional patent filed February 5, 2026 — three independent claims covering automated behavioral A/B testing, continuous sensitivity metric computation, and cryptographic evidence chains.