Forecast AI privacy attack loss
Forecast correlated membership-inference or reconstruction loss from tenant-local member/nonmember red-team trials using Beta posterior attack advantage, partially pooled lognormal harm, binomial subject exposure, coherent attacker regimes, common asset-group compromise, control effects and loss VaR/CVaR.
What it's for
Shows executives the economic privacy tail their formal budget alone cannot answer: locally demonstrated attack advantage, subjects exposed, shared attack states, control benefit and severe-loss capital.
What you give it
Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| current_exposures | array of objects (18 fields) | Evidence | Yes |
| historical_attack_trials | array of objects (10 fields) | Evidence | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_attack_trials | integer ≥ 1, ≤ 1000000 | Your calibration | Optional |
| random_seed | integer ≥ 0, ≤ 4294967295 | Your calibration | Optional |
| scenarios | array of objects (7 fields) | Evidence | Yes |
| simulation_count | integer ≥ 1000, ≤ 1000000 | Your calibration | Optional |
| tail_probability | number > 0, < 1 | Your calibration | Optional |
Each current_exposures
record
| Field | Type | Required |
|---|---|---|
| annual_value_supported | number (≥ 0) | Yes |
| asset_group_id | string (non-empty) | Yes |
| attack_class | string (non-empty) | Yes |
| containment_fraction | number (≥ 0, ≤ 1) | Yes |
| detection_probability | number (≥ 0, ≤ 1) | Yes |
| evidence_verified | boolean | Yes |
| exposed_subject_count | integer (≥ 1) | Yes |
| fixed_response_cost | number (≥ 0) | Yes |
| id | string (non-empty) | Yes |
| maximum_attack_advantage | number (≥ 0, ≤ 1) | Yes |
| member_prior_alpha | number (> 0) | Yes |
| member_prior_beta | number (> 0) | Yes |
| nonmember_prior_alpha | number (> 0) | Yes |
| nonmember_prior_beta | number (> 0) | Yes |
| severity_prior_log_mean | number | Yes |
| severity_prior_log_sd | number (> 0, ≤ 5) | Yes |
| severity_prior_strength | number (> 0) | Yes |
| value_disruption_fraction | number (≥ 0, ≤ 1) | Yes |
{
"current_exposures": [
{
"annual_value_supported": 500000,
"asset_group_id": "support-training-snapshot",
"attack_class": "support-membership",
"containment_fraction": 0.5,
"detection_probability": 0.7,
"evidence_verified": true,
"exposed_subject_count": 10000,
"fixed_response_cost": 25000,
"id": "support-model-eu",
"maximum_attack_advantage": 0.05,
"member_prior_alpha": 1,
"member_prior_beta": 9,
"nonmember_prior_alpha": 1,
"nonmember_prior_beta": 19,
"severity_prior_log_mean": 4.605170185988092,
"severity_prior_log_sd": 0.5,
"severity_prior_strength": 5,
"value_disruption_fraction": 0.2
}
],
"historical_attack_trials": [
{
"attack_class": "support-membership",
"evidence_verified": true,
"id": "membership-trial-1",
"loss_log_squared_sum": 212.0759244191359,
"loss_log_sum": 46.05170185988092,
"loss_observation_count": 10,
"member_success_count": 30,
"member_trial_count": 200,
"nonmember_success_count": 10,
"nonmember_trial_count": 200
}
],
"random_seed": 53,
"scenarios": [
{
"attack_odds_multiplier": 1,
"common_attack_probability": 0.02,
"common_compromise_fraction": 0.05,
"disruption_multiplier": 1,Truncated for display — the full payload is 60 lines.
What you get back
This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.
{
"assumptions": [
"Member and nonmember trials use a frozen attack, comparable target model/data pipeline, blind labels, complete failures and the same prospective success definition.",
"Posterior attack advantage is the excess member success rate over the nonmember false-positive rate; coherent scenarios and asset-group events preserve shared attacker and pipeline states.",
"Positive mature loss observations support partially pooled lognormal per-subject severity; detection and containment effects require local backtesting."
],
"counts": {
"asset_groups": 1,
"current_exposures": 1,
"historical_trial_rows": 1,
"scenarios": 2,
"simulations": 2000,
"supported_exposures": 1
},
"decision": "review_quantified_ai_privacy_tail",
"exposure_forecasts": [
{
"asset_group_id": "support-training-snapshot",
"attack_class": "support-membership",
"evidence_verified": true,
"expected_compromised_subjects": 1138.56,
"expected_loss": 185085.0456,
"exposure_id": "support-model-eu",
"posterior_mean_attack_advantage": 0.0979,
"probability_advantage_exceeds_limit": 0.945,
"support_gate_pass": true,
"trial_support_per_arm": 200
}
],
"forecast": {
"conditional_value_at_risk": 359830.1094,
"expected_loss": 185085.0456,
"maximum_simulated_loss": 511139.1552,
"median_loss": 159960.3843,
"probability_of_any_loss": 1,
"tail_probability": 0.05,
"value_at_risk": 323810.0077
},
"limitations": [
"Red-team failure does not prove privacy, future adaptive attackers may be stronger, and the simulated tail omits attacks, dependencies and harms absent from the supplied model.",
"The forecast is aggregate internal risk planning, not breach confirmation, legal damages, subject-level inference or a judgment about a dataset owner, engineer or population."
],
"method": "beta_membership_advantage_correlated_lognormal_privacy_loss_v1",
"reproducibility": {Truncated for display — the full payload is 53 lines.
How it works
Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.
- 1 Preserve complete blind member and nonmember trial denominators so attack advantage is separated from false positives; pool only stable attack classes.
- 2 Draw posterior advantage and compromised subjects, then apply one coherent scenario and shared asset-group attack event across all exposures.
- 3 Price fixed response, controlled per-subject lognormal harm and supported-value disruption; report support gates, contributors and portfolio VaR/CVaR.
Before you trust it
Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.
Assumptions & guardrails
- The likelihood or reward model, prior support, action logging, delayed outcomes, and any stationarity assumptions match the deployment process.
- Trials freeze attack/model/data versions before labels, include failures and use comparable target systems; loss observations are mature and scenarios preserve common attacker states.
- Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
- Red-team failure is not privacy proof; the forecast is aggregate internal risk planning, not breach confirmation, legal damages or subject-level inference.
Minimum evidence
- historical_attack_trials: required and organization-defined
- current_exposures: required and organization-defined
- scenarios: required and organization-defined
How to validate it
Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- prospective privacy red-team evidence panel joining frozen attack/model/data versions and blinded membership labels to complete attempts, false positives, mature incident economics, common asset lineage and locally backtested controls
- attack/model/data comparability, membership and success definitions, trial completeness/blinding, attack-class pooling, minimum support, priors, severity/currency/horizon, common groups/scenarios, control effects, tail appetite and risk owner
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
- 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
- 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.
Call it from your AI
You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.
gitrevio_capabilities_search
{ "q": "forecast correlated membershipinference or reconstruction loss" }
→ finds "forecast_ai_privacy_attack_loss"
gitrevio_capability_describe
{ "capability_id": "forecast_ai_privacy_attack_loss" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_ai_privacy_attack_loss", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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