Ml Decision Boundary Assessment

ML decision boundary assessment

Measure the security behavior of a classifier or scoring model as an integrated gate. Evaluate paired inputs, preprocessing, thresholds, fallbacks, and downstream decisions instead of celebrating one changed label.

When to Use

• Assessing a classifier, detector, moderation model, fraud score, or other ML component used to permit, block, route, or escalate activity.
• Measuring robustness within an approved perturbation and query budget.
• Determining whether model behavior and system enforcement remain aligned.

When NOT to Use

• Do not test a public model or production decision service without explicit authority and impact limits.
• Do not generate weaponized evasion samples or optimize against protected real users, malware, fraud, or safety controls.
• Do not call one label change a system bypass without downstream evidence.

Preconditions

Require the model or service version, protected decision, input domain, evaluation identity, approved dataset, permissible transformations, perturbation metric and ceiling, query and time budgets, target and non-target success criteria, fallback behavior, monitoring, stop condition, and cleanup.

Workflow

1. Record the security invariant and full decision path: input acquisition, canonicalization, preprocessing, model output, threshold, abstention, ensemble or rule logic, fallback, and downstream action.
2. Freeze or record every component version and randomization source. Note any remote service whose exact version cannot be established.
3. Build a representative baseline set with labels or expected dispositions, protected classes where relevant, and provenance. Separate development, calibration, and final evaluation data.
4. Measure baseline confusion, score distribution, threshold behavior, abstention, fallback, and downstream enforcement before adversarial cases.
5. Define paired transformations that preserve the test's permitted semantics and remain inside the approved budget. Record the exact distance or change measure for each pair.
6. Test one transformation family at a time. Avoid public payload recipes; keep sensitive samples in restricted artifacts.
7. Capture raw input reference, transformed input reference, preprocessing output where observable, model score or label, threshold decision, fallback, downstream action, telemetry, timestamp, and run identifier.
8. Distinguish outcomes:
   • model-shift — score or label changed;
   • gate-shift — the security disposition changed;
   • system-bypass — target-origin evidence confirms the prohibited outcome;
   • inconclusive — version, preprocessing, nondeterminism, or evidence gaps prevent attribution.
9. Measure target success and collateral error across the evaluation set. One successful pair is a reproducer, not a robustness rate.
10. Re-test remediation against the held-out set and ordinary inputs. Confirm that threshold or preprocessing changes do not merely move the failure.

Deliverable

Return the decision-path map, frozen context, dataset provenance, baseline metrics, perturbation contract, paired-case receipts, outcome classification, target and collateral rates, limitations, stop events, and remediation options.

Rationalizations to reject

• “The label changed, so the security control was bypassed.” Prove the gate and downstream disposition separately.
• “The change looks imperceptible.” Use the approved domain-specific metric and ceiling rather than visual intuition.
• “One example demonstrates robustness failure rate.” A reproducer does not establish prevalence.
• “Raising the threshold fixes it.” Measure false positives, false negatives, abstention, and fallback consequences.
• “The model is the whole control.” Preprocessing, rules, ensembles, and downstream authorization can strengthen or weaken the integrated gate.
• “More queries only improve confidence.” Query budgets limit operational impact and adaptive overfitting.