Agent summary
Facts: Senegal: 30-day index score 1/100; 746 measurements in that score window. The sample does not establish a domain or service block verdict.
As of score observation: Unavailable. Score snapshot fetched: 2026-10-12T04:29:22.785Z. Fetch time is not the underlying measurement time. OONI source last fetched: ; this is a separate source fetch, not an observation time.
Sources: 1 distinct names (ooni) in 10 returned incident records, up to 10. Missing source lists: 0. This is not the score source count or independent corroboration.
Cite: Voidly Research. (n.d.). Senegal Internet Censorship Profile. https://voidly.ai/sn (accessed 2026-10-12).
Use this in your agent
Connect the read-only Atlas MCP service, then select and copy this request into your agent.
Atlas MCP setup · Agent summary JSON · Endpoint: https://atlas-mcp.voidly.ai/mcp
Africa / Western Africa · SN
A closer look at internet access.
The score, the signals, the source.
30-day index score
1/100
A dated sample-based score. Higher values indicate more interference in the index model.
Snapshot fetched
Editorial context
Available source records for Senegal are shown with their dates and measurement limits. An absent score does not imply unrestricted internet access.
This context is curated and undated. The trend is an editorial label, not a computed recent movement.
Internet freedom varies. Use Voidly to ensure unrestricted access.
Editorial text is preserved as source context. Product recommendations do not guarantee unrestricted access. The source tier may differ from the measured score because the two have different definitions.
Score window: 30 days. Snapshot fetched . Content first seen . Upstream serve-time . These timestamps do not date the all-time OONI observations.
All-time measurements accumulate from an older source file without an observation window or timestamp. The all-time blocked ratio is not the confidence-shrunk score. Zero counts and absent fields are kept distinct.
All-time measurement archive
No collection date or complete-country coverage claim is attached to this archive. Protocol fields can be incomplete and need not sum to the historical total.
Country incident records
Statuses come from each record; “active” is not inferred from severity or an absent end date. Incident confidence is a source field, not independent certainty that censorship occurred.
This is the source mix in the latest returned incident sample, not independent corroboration of the country score. A record can name multiple sources; the counts can overlap.
Dataset version: 2026.10.12. Malformed rows omitted: 0. All readable returned records appear above; each links to its complete evidence and citation page.
Measurement context
An anomaly is a measurement result requiring interpretation. These country API fields use a different basis from the dated, confidence-shrunk index score.
Source generated: . This is a source timestamp, not proof that every underlying observation is current.
{
"@context": "https://schema.org",
"@type": "Country",
"name": "Senegal",
"identifier": "SN",
"url": "https://voidly.ai/censorship-index/sn",
"additionalProperty": [
{
"@type": "PropertyValue",
"name": "Censorship Score",
"value": 1,
"description": "0-100 scale where 100 = total internet censorship"
},
{
"@type": "PropertyValue",
"name": "Anomaly Rate",
"value": 0.00804289544235925
},
{
"@type": "PropertyValue",
"name": "Measurement Count",
"value": 746
}
],
"data": {
"country": "SN",
"name": "Senegal",
"flag": "🇸🇳",
"region": "Africa",
"timestamp": "2026-10-12T13:03:33.185Z",
"status": "free",
"score": 1,
"categoryScores": {},
"blockedSites": [],
"affectedServices": [],
"voidlyProbes": {
"total": 0,
"successful": 0,
"blocked": 0
},
"ooni": {
"status": "normal",
"anomalyRate": 0.00804289544235925,
"confirmedRate": 0,
"measurementCount": 746,
"affectedServices": [],
"lastUpdated": "2026-10-12T09:17:26.672Z"
},
"activeIncidents": []
},
"dataSource": "live_ooni",
"source": "Voidly Global Censorship Index",
"lastUpdated": "2026-10-12T13:03:33.448Z"
}Quality measures evidence confidence, not censorship probability. A small or absent sample cannot establish country-wide access. Quality generated: .
Quality sourceCountry-origin probe records: Unavailable. Flagged in that sample: Unavailable (Unavailable).
Per-probe and operator listings are withheld to protect volunteer operators. Country-origin aggregate records above do not establish whole-country coverage.
Model outlook
7-day event probability
Unavailable
A censorship or connectivity event within seven days of the model’s as-of date, relative to the country’s baseline. It does not predict an exact shutdown date or measure standing freedom.
Model: v1
Model as-of date: Date unavailable
Source generated: . This is a source timestamp, not proof that every underlying observation is current.
fixed in-sample model confidence, NOT a per-forecast calibrated probability
Use for cross-country RISK RANKING, not precise within-country day-of-shutdown timing.
This forecast scores the ONSET of a new recorded incident, not how censored a country already is. A permanently and stably blocked network generates no new anomaly, so it scores LOW. Measured 2026-07-31: China 0.015 and India 0.023 both rank BELOW Denmark 0.062 — China is probed 30/30 days across 836 domains and has logged nothing for 71 days, while Denmark is probed 2/30 days. A low score here means 'no CHANGE expected', never 'this country is free'. For standing severity use /v1/atlas/score-v2 or the censorship index instead.
Training ROC AUC 0.954 came from a RANDOM stratified split (consecutive country-days in an incident window are near-duplicates, inflating it). No target leakage: features use [date-7,date], label uses [date+1,date+7].
~0.85-0.90 (dual-holdout temporal gate)
ML_LEAKAGE_AUDIT.md (forecast v1/7day row).
{
"aci": {
"active": false,
"reason": "unverified: training holdout and ACI outcomes do not match the served headline or qualified target"
},
"aci_alpha": null,
"confidence": 0.85,
"country": "SN",
"country_name": "SN",
"covered": true,
"forecast": [
{
"date": "2026-10-12",
"day": 0,
"drivers": [],
"risk": 0.01
},
{
"date": "2026-10-13",
"day": 1,
"drivers": [],
"risk": 0.01
},
{
"date": "2026-10-14",
"day": 2,
"drivers": [],
"risk": 0.014
},
{
"date": "2026-10-15",
"day": 3,
"drivers": [],
"risk": 0.01
},
{
"date": "2026-10-16",
"day": 4,
"drivers": [],
"risk": 0.03
},
{
"date": "2026-10-17",
"day": 5,
"drivers": [],
"risk": 0.021
},
{
"date": "2026-10-18",
"day": 6,
"drivers": [],
"risk": 0.027
},
{
"date": "2026-10-19",
"day": 7,
"drivers": [],
"risk": 0.025
}
],
"generated_at": "2026-10-12T13:03:33.162646",
"honest_caveat": {
"confidence_is": "fixed in-sample model confidence, NOT a per-forecast calibrated probability",
"correct_use": "Use for cross-country RISK RANKING, not precise within-country day-of-shutdown timing.",
"detects_change_not_state": "This forecast scores the ONSET of a new recorded incident, not how censored a country already is. A permanently and stably blocked network generates no new anomaly, so it scores LOW. Measured 2026-07-31: China 0.015 and India 0.023 both rank BELOW Denmark 0.062 — China is probed 30/30 days across 836 domains and has logged nothing for 71 days, while Denmark is probed 2/30 days. A low score here means 'no CHANGE expected', never 'this country is free'. For standing severity use /v1/atlas/score-v2 or the censorship index instead.",
"headline_auc_note": "Training ROC AUC 0.954 came from a RANDOM stratified split (consecutive country-days in an incident window are near-duplicates, inflating it). No target leakage: features use [date-7,date], label uses [date+1,date+7].",
"honest_temporal_holdout_auc": "~0.85-0.90 (dual-holdout temporal gate)",
"live_forward_accountability": [
"GET /v1/sentinel/accuracy (503 unvalidated; empirical precision/recall withheld)",
"GET /v1/forecast/onset-skill (honest onset / forward skill)"
],
"source": "ML_LEAKAGE_AUDIT.md (forecast v1/7day row)."
},
"interval_90": null,
"model_version": "v1",
"summary": {
"avg_risk": 0.018,
"key_drivers": [],
"max_risk": 0.03,
"max_risk_day": 4
},
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
],
"top_features_per_day": [
{
"base_prob": 0.0027,
"date": "2026-10-12",
"day": 0,
"risk": 0.01,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-13",
"day": 1,
"risk": 0.01,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-14",
"day": 2,
"risk": 0.014,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-15",
"day": 3,
"risk": 0.01,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-16",
"day": 4,
"risk": 0.03,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-17",
"day": 5,
"risk": 0.021,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-18",
"day": 6,
"risk": 0.027,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
},
{
"base_prob": 0.0027,
"date": "2026-10-19",
"day": 7,
"risk": 0.025,
"top_features": [
{
"contribution": -0.053,
"direction": "down",
"name": "block_rate_roll7_mean",
"source": "model"
},
{
"contribution": -0.0482,
"direction": "down",
"name": "gdelt_unrest_30d",
"source": "model"
},
{
"contribution": -0.0379,
"direction": "down",
"name": "critical_incident_7d",
"source": "model"
}
]
}
]
}The returned daily shape belongs to legacy v1. It is not independently validated and does not describe the honest 7-day headline model above. Its peak day, fixed confidence and interval must not be attached to that headline.
| Date | Legacy value | Source drivers |
|---|---|---|
| 2026-10-12 | 1.0% | |
| 2026-10-13 | 1.0% | |
| 2026-10-14 | 1.4% | |
| 2026-10-15 | 1.0% | |
| 2026-10-16 | 3.0% | |
| 2026-10-17 | 2.1% | |
| 2026-10-18 | 2.7% | |
| 2026-10-19 | 2.5% |
Original display confidence field: 85.0%. The source describes this as fixed in-sample model confidence, not calibrated per-forecast certainty.
Separate multi-horizon model. Each mark is a model score. The former residual bands lack validated 90% coverage and are withheld.
36.4% · interval unvalidated
65.2% · interval unvalidated
70.0% · interval unvalidated
Model: multi-horizon-h-v1. Interval coverage is unvalidated; these estimates do not support precise within-country timing.
Source consistency: within the model’s monotonicity tolerance.
Per-horizon breakdownSource generated: . This is a source timestamp, not proof that every underlying observation is current.
AUC (e.g. 1d ~0.91) OVERSTATES usefulness — per-horizon F1 is much lower (~0.45 at 1d) because shutdown-positive days are rare and the 7/30-day target is a sliding window that is highly autocorrelated day-to-day (a leakage-prone label).
LOCO binary-outcome residual widths are not validated 90% probability intervals; bands are withheld at every horizon.
Use for cross-country RISK RANKING within the watched panel, NOT precise within-country day-of-shutdown timing. Data-backed for covered countries only (see the 'covered' flag).
This forecast scores the ONSET of a new recorded incident, not how censored a country already is. A permanently and stably blocked network generates no new anomaly, so it scores LOW. Measured 2026-07-31: China 0.015 and India 0.023 both rank BELOW Denmark 0.062 — China is probed 30/30 days across 836 domains and has logged nothing for 71 days, while Denmark is probed 2/30 days. A low score here means 'no CHANGE expected', never 'this country is free'. For standing severity use /v1/atlas/score-v2 or the censorship index instead.
Per-horizon AUC is leave-one-country-out on the SPOTLIGHT (watched) panel, NOT all 130 countries; see /v1/forecast/multi-horizon/info for the per-horizon F1.
{
"consistency": {
"monotonic": true,
"violations": []
},
"country": "SN",
"country_name": "SN",
"covered": true,
"generated_at": "2026-10-12T13:03:33.201785",
"honest_caveat": {
"auc_caveat": "AUC (e.g. 1d ~0.91) OVERSTATES usefulness — per-horizon F1 is much lower (~0.45 at 1d) because shutdown-positive days are rare and the 7/30-day target is a sliding window that is highly autocorrelated day-to-day (a leakage-prone label).",
"conformal_caveat": "LOCO binary-outcome residual widths are not validated 90% probability intervals; bands are withheld at every horizon.",
"correct_use": "Use for cross-country RISK RANKING within the watched panel, NOT precise within-country day-of-shutdown timing. Data-backed for covered countries only (see the 'covered' flag).",
"detects_change_not_state": "This forecast scores the ONSET of a new recorded incident, not how censored a country already is. A permanently and stably blocked network generates no new anomaly, so it scores LOW. Measured 2026-07-31: China 0.015 and India 0.023 both rank BELOW Denmark 0.062 — China is probed 30/30 days across 836 domains and has logged nothing for 71 days, while Denmark is probed 2/30 days. A low score here means 'no CHANGE expected', never 'this country is free'. For standing severity use /v1/atlas/score-v2 or the censorship index instead.",
"metrics_are": "Per-horizon AUC is leave-one-country-out on the SPOTLIGHT (watched) panel, NOT all 130 countries; see /v1/forecast/multi-horizon/info for the per-horizon F1."
},
"honest_horizons": [
"1d",
"30d",
"7d"
],
"horizons": {
"1d": {
"conformal_90": null,
"horizon": "1d",
"horizon_days": 1,
"interval_status": "unvalidated",
"probability": 0.364,
"top_features": [
{
"feature": "week_of_year",
"shap_value": -0.9432631134986877
},
{
"feature": "day_of_week",
"shap_value": -0.3174426853656769
},
{
"feature": "block_rate_roll7_mean",
"shap_value": -0.20644856989383698
},
{
"feature": "block_rate_roll7_std",
"shap_value": -0.1808004379272461
},
{
"feature": "risk_tier",
"shap_value": -0.14699505269527435
}
]
},
"30d": {
"conformal_90": null,
"horizon": "30d",
"horizon_days": 30,
"interval_status": "unvalidated",
"probability": 0.6999,
"top_features": [
{
"feature": "week_of_year",
"shap_value": -0.8071998357772827
},
{
"feature": "risk_tier",
"shap_value": -0.35165342688560486
},
{
"feature": "block_rate_roll7_std",
"shap_value": -0.1718108355998993
},
{
"feature": "recent_shutdown",
"shap_value": -0.12183760851621628
},
{
"feature": "block_rate_roll14_mean",
"shap_value": 0.12115472555160522
}
]
},
"7d": {
"conformal_90": null,
"horizon": "7d",
"horizon_days": 7,
"interval_status": "unvalidated",
"probability": 0.6516,
"top_features": [
{
"feature": "week_of_year",
"shap_value": -0.7338341474533081
},
{
"feature": "block_rate_roll7_mean",
"shap_value": 0.4103630781173706
},
{
"feature": "block_rate_roll7_std",
"shap_value": -0.2027534544467926
},
{
"feature": "blocked_count_roll7_mean",
"shap_value": 0.1563306748867035
},
{
"feature": "month",
"shap_value": -0.08148038387298584
}
]
}
},
"model_version": "multi-horizon-h-v1"
}Model evaluation and labels differ. Honest 7-day model · Multi-horizon evaluation. An API response date is not the date of the underlying training data.
Additional signals
Each source answers a different question. Missing signals stay visible.
Unavailable
Separate from the index score. A weighted signal composite; sparse evidence can pull it toward zero.
Source freshness is unknown.
Date unavailable
Score methodSource generated: Date unavailable. This is a source timestamp, not proof that every underlying observation is current.
5 median days
Scenario: a critical censorship incident in Senegal. This is not a timer for an existing incident.
Source interquartile range: 5–263 days.
Exploratory · source model is below its promotion threshold.
Model trained: . Censoring in training data: 78.4%.
Source generated: Date unavailable. This is a source timestamp, not proof that every underlying observation is current.
This read-only inference endpoint requires a POST request:
POST https://api.voidly.ai/v1/forecast/duration
Content-Type: application/json
{
"country": "SN",
"severity": "critical",
"incident_type": "censorship"
}{
"country": "SN",
"features_used": {
"continent_Africa": 1,
"continent_Americas": 0,
"continent_Asia": 0,
"continent_Europe": 0,
"continent_Oceania": 0,
"country_risk_tier": 3,
"first_seen_month": 10,
"first_seen_year": 2026,
"prior_shutdowns_count_24mo": 29,
"severity_critical": 1,
"severity_warning": 0,
"type_censorship": 1,
"type_mixed": 0
},
"honest_caveats": [
"model below promote floor (c=0.55, n=343) — treat as exploratory",
"censoring rate in training set: 78%"
],
"incident_type": "censorship",
"iqr_days": 258,
"median_days": 5,
"median_hours": 120,
"model": {
"censoring_rate": 0.7842565597667639,
"class": "RandomSurvivalForest",
"feature_names": [
"country_risk_tier",
"first_seen_year",
"first_seen_month",
"prior_shutdowns_count_24mo",
"continent_Africa",
"continent_Americas",
"continent_Asia",
"continent_Europe",
"continent_Oceania",
"severity_critical",
"severity_warning",
"type_censorship",
"type_mixed"
],
"name": "shutdown_duration_rsf_v1",
"trained_at": "2026-05-21T12:01:30.451711Z"
},
"model_c_index": 0.5455,
"n_training_samples": 343,
"p25_hours": 120,
"p75_hours": 6312,
"promoted": false,
"severity": "critical",
"survival_curve_summary": [
{
"hours": 48,
"survival_prob": 0.9
},
{
"hours": 120,
"survival_prob": 0.75
},
{
"hours": 120,
"survival_prob": 0.5
},
{
"hours": 6312,
"survival_prob": 0.25
},
{
"hours": 9624,
"survival_prob": 0.1
}
]
}Follower estimate unavailable.
Trigger attribution is probabilistic, not causal. Noisy-OR aggregation can overstate risk when triggers share a cause.
Source generated: Date unavailable. This is a source timestamp, not proof that every underlying observation is current.
Similarity of measured blocking patterns, not governments or intent. Sparse feature coverage lowers confidence.
Source generated: . This is a source timestamp, not proof that every underlying observation is current.
Similarity here is BEHAVIORAL, not political. Two countries are near each other because they BLOCK in similar ways — comparable method mix, target categories and temporal shape — which does NOT imply comparable governments, laws or intent. A stable democracy with seasonal sports-piracy blocks can sit near an authoritarian state if their measured blocking footprints rhyme. Feature vectors are SPARSE for low-coverage countries (flagged sparse=true, <3 of 5 behavioral feature families populated) — their neighbors are dominated by regime/continent/cohort one-hots and should be read with low confidence. The 2D graph projection (UMAP or PCA) compresses a 32-dimensional space to 2 and loses information — use the cosine `neighbors` list, not pixel distance in the projection, for any quantitative claim.
{
"country": "SN",
"generated_at": "2026-10-12T05:10:36.111623+00:00",
"honest_caveat": "Similarity here is BEHAVIORAL, not political. Two countries are near each other because they BLOCK in similar ways — comparable method mix, target categories and temporal shape — which does NOT imply comparable governments, laws or intent. A stable democracy with seasonal sports-piracy blocks can sit near an authoritarian state if their measured blocking footprints rhyme. Feature vectors are SPARSE for low-coverage countries (flagged sparse=true, <3 of 5 behavioral feature families populated) — their neighbors are dominated by regime/continent/cohort one-hots and should be read with low confidence. The 2D graph projection (UMAP or PCA) compresses a 32-dimensional space to 2 and loses information — use the cosine `neighbors` list, not pixel distance in the projection, for any quantitative claim.",
"method": "standardized-cosine + pca",
"nearest": [
{
"continent": "Africa",
"country": "ZM",
"risk_tier": 3,
"similarity": 0.8625,
"sparse": false
},
{
"continent": null,
"country": "SV",
"risk_tier": null,
"similarity": 0.7593,
"sparse": false
},
{
"continent": null,
"country": "GN",
"risk_tier": null,
"similarity": 0.5639,
"sparse": false
},
{
"continent": "Africa",
"country": "DZ",
"risk_tier": 3,
"similarity": 0.5346,
"sparse": false
},
{
"continent": "Africa",
"country": "SD",
"risk_tier": 3,
"similarity": 0.4762,
"sparse": false
},
{
"continent": "Africa",
"country": "KE",
"risk_tier": 4,
"similarity": 0.4515,
"sparse": false
},
{
"continent": "Europe",
"country": "BA",
"risk_tier": 3,
"similarity": 0.4203,
"sparse": false
},
{
"continent": "Asia",
"country": "CY",
"risk_tier": 3,
"similarity": 0.4195,
"sparse": false
},
{
"continent": "Europe",
"country": "HU",
"risk_tier": 3,
"similarity": 0.3799,
"sparse": false
},
{
"continent": "Africa",
"country": "MZ",
"risk_tier": 3,
"similarity": 0.3678,
"sparse": false
}
],
"query_meta": {
"continent": "Africa",
"country": "SN",
"dtw_cohort": null,
"evidence_count": 576,
"feature_families_present": 4,
"feature_families_total": 5,
"risk_tier": 3,
"sparse": false
},
"version": "v1"
}Grades are source rubric categories. They do not independently establish deliberate blocking.
Grading rubricSource generated: Date unavailable. This is a source timestamp, not proof that every underlying observation is current.
{
"distribution": {
"A": 1,
"B": 32,
"C": 0,
"F": 0
},
"filters": {
"country": "SN",
"incident_type": null
},
"rules": {
"A": "else",
"B": "n_sources >= 1 AND avg_confidence >= 0.7",
"C": "n_sources >= 2 AND n_domains >= 3",
"F": "duration_hours >= 24 AND n_sources >= 2"
},
"total": 33,
"ungraded": 0
}No sufficiently covered headline recommendation is available.
Source generated: . This is a source timestamp, not proof that every underlying observation is current.
{
"country_code": "SN",
"coverage_note": "Insufficient probe coverage for SN: only 27 total probes across 1 tools. We refuse to emit tier labels below 100 probes — see raw rows in tools_ranked instead. A missing tool is more likely an OONI-coverage gap than a working tool.",
"do_not_share_card_without_caveats": true,
"endpoints": {
"info": "/v1/atlas/circumvention/info",
"leaderboard": "/v1/atlas/evasion/leaderboard",
"upstream_evasion_table": "/v1/atlas/evasion/SN"
},
"generated_at": "2026-10-12T06:00:13.584980+00:00",
"headline_basis": "best-of-measured (none cleared try_first or fallback bar)",
"headline_recommendation": {
"category": "anonymity-network",
"confidence": 0.928,
"n_total_probes": 27,
"rank_score": 0.6541,
"success_rate": 0.9259,
"tool_name": "Tor",
"tool_note": "Tor uses obfs4 by default. If obfs4 fails, switch to the Snowflake bridge from inside the Tor Browser settings. The OONI tor test does NOT differentiate transports, so this row is the aggregate.",
"tool_slug": "tor",
"verdict": "works"
},
"honest_caveats": [
"PROBE SUCCESS != REAL-USER SUCCESS. DPI vendors increasingly rate-limit real client traffic while letting OONI / Voidly synthetic measurement probes through. Success rates here are an UPPER BOUND on what a real user will see.",
"Recommendations can become stale within hours. Censorship adapts. If a try_first tool stops working, the next probe rotation (cron daily 06:00 UTC) will catch it but you will see it before we do.",
"'Try first' is NOT 'safe'. It means 'highest measured success rate, highest probe confidence.' Operational security (unique device, throwaway account, no SIM, no DNS leak, etc.) is the USER's responsibility — none of that is captured here.",
"Domain-fronted tools (Snowflake, Lantern, Psiphon) are SYSTEMATICALLY UNDER-ESTIMATED. Bootstrap-domain probes do not measure the rotating tunnel endpoints those tools actually use.",
"Self-hosted WireGuard / OpenVPN on a private IP is INVISIBLE to OONI probes and is not represented here. If you have one, trust it over any commercial recommendation.",
"Tor rows roll up vanilla / obfs4 / Snowflake / Meek transports. Per-transport granularity needs raw OONI nettests, out of scope for v1.",
"DO NOT share this recommendation as a forum-shareable card without the caveat block. The do_not_share_card_without_caveats flag in the response is on by design."
],
"informational_disclaimer": "This endpoint is INFORMATIONAL, not a safety guarantee. Probe success is an upper bound on real-user success. User assumes the risk of any tool selected from this ranking. 'Try first' does not mean 'safe' — it means 'highest measured success, highest probe confidence.'",
"insufficient_coverage": true,
"lookback_days": 30,
"min_total_probes_floor": 100,
"n_tools_measured": 1,
"n_total_probes": 27,
"rank_formula": "success_rate * sqrt(confidence) * recency_decay",
"schema": "voidly-circumvention-recommendation/v1",
"tier_definitions": {
"avoid": {
"description": "High-confidence measurements show <10% probe success. Very likely blocked. Listed for transparency — do not trust the bootstrap path.",
"label": "Avoid",
"max_success_rate": 0.1,
"min_confidence": 0.7
},
"fallback": {
"description": "Either partial measured success (30%+) OR not enough probes to be sure. Worth trying if the try_first tools fail — but expect lower reliability.",
"label": "Fallback",
"max_confidence_for_uncertain": 0.5,
"min_success_rate_lower": 0.3
},
"try_first": {
"description": "High measured success rate AND high probe confidence. Still informational only — DPI vendors increasingly block real clients while letting measurement probes pass, so success_rate is an upper bound.",
"label": "Try first",
"min_confidence": 0.7,
"min_success_rate": 0.7
}
},
"tiers": {
"avoid": [],
"fallback": [],
"try_first": []
},
"tools_ranked": [
{
"block_rate": 0.0741,
"blocking_methods": {
"tor-blocking": 2
},
"category": "anonymity-network",
"confidence": 0.928,
"confidence_band": "high",
"domains_observed": [],
"first_observed": "2026-09-17T18:00:00Z",
"last_observed": "2026-09-26T06:00:00Z",
"n_blocked_probes": 2,
"n_successful_probes": 25,
"n_total_probes": 27,
"n_unique_asns": 0,
"rank_score": 0.6541,
"recency_decay": 0.7333,
"recency_decay_known": true,
"recency_decay_note": null,
"success_rate": 0.9259,
"tier": null,
"tool_name": "Tor",
"tool_note": "Tor uses obfs4 by default. If obfs4 fails, switch to the Snowflake bridge from inside the Tor Browser settings. The OONI tor test does NOT differentiate transports, so this row is the aggregate.",
"tool_slug": "tor",
"verdict": "works"
}
]
}Network variation
Network-level records add detail that a country score cannot show.
No ISP rows returned by the source. This does not establish an absence of blocking.
Returned rows: 0; source ISP count: 0. Counts and percentages apply to the returned measurement sample.
Source generated: . This is a source timestamp, not proof that every underlying observation is current.
{
"country": "SN",
"countryName": "Senegal",
"generated_at": "2026-10-12T13:03:33.351832Z",
"ispCount": 0,
"isps": [],
"methodology": "compositeScore (0-100) ranks ISPs by blockRate + category breadth + method aggressiveness - a RELATIVE ranking signal, NOT an absolute percent-blocked. blockRate is the share of an ISP MEASURED evidence that are blocking signals (OONI/CensoredPlanet probe censoring networks heavily, so ~1.0 is common for targeted ISPs), NOT the share of the internet the ISP blocks."
}Editorial service list
Undated editorial context. Check current evidence before treating a service as blocked.
Check a specific domainCountry guidance
The original country recommendations are preserved below. They are undated guidance, not a guarantee of access or safety.
Questions, answered
The dated 30-day index score is 1/100. That describes the source sample, not every person, network or domain in Senegal.
Internet freedom varies. Use Voidly to ensure unrestricted access.
Use the recent records and their original sources to investigate a particular restriction. Disruption alone does not establish intentional censorship.
The editorial list names: Varies by country. It is undated context, not a current availability check.
Results can differ between networks, measurement times and targets. Country incident records and the country API preserve source details.
Original snapshot rank: #95. Editorial risk tier: Low. Editorial trend: stable. The tier and trend are curated context; they are not a measured change between recent dates.
The index includes a dated score window and a separate all-time measurement corpus. Compare like-for-like windows and inspect sample coverage before drawing a national comparison. Compare the index.
The pipeline can record DNS interference, TCP resets, HTTP block pages, TLS/SNI failures and network-level outages. This list describes possible mechanisms; it does not claim every mechanism was observed in Senegal.
Each incident and ISP record below retains its reported mechanism and evidence. Inspect the country evidence query before attributing a failure to deliberate blocking.
The index score snapshot was fetched . Its source serve-time can advance without changes in the underlying scores. The accumulating historical OONI file has no dated observation window.
Current country data, incidents, network state and models have independent source dates. The page requested those sources . It does not continuously refresh. Missing, stale and uncovered responses remain labeled.
Subscribe to source feedsOpen the accessibility checker. For a source query, use the country code with the domain you are investigating:
GET https://api.voidly.ai/v1/accessibility/check?domain=twitter.com&country=SN MCP server: npx @voidly/mcp-server Tool: check_domain_blocked Arguments: domain="twitter.com", country="SN" CLI: npx @voidly/cli check twitter.com SN
Inspect the returned timestamp, measurement coverage and method. A sampled result is not a guarantee for every user. API documentation · MCP tools.
Results depend on local networks and the transport used. The source’s complete country guidance is preserved in the recommendations section. Voidly’s current product details belong on the VPN page; a measured probe result is not a safety or success guarantee.
Use the canonical country URL, https://voidly.ai/sn, with your access date and the date of any source snapshot you quote. The citation below supports plain text and BibTeX copy/download. A model generation or page access date is not a publication date.
Voidly-original analysis is CC BY 4.0. Upstream records keep their own attribution and licenses.
Open evidence
Voidly Research. (n.d.). Senegal Internet Censorship Profile. https://voidly.ai/sn (accessed 2026-10-12).
Citations use the public /sn URL. Access date is not publication date. Cite the individual source, observation time and model version when using a number.
Voidly-original analysis and annotations are CC BY 4.0. Upstream OONI, IODA and Censored Planet records retain their own licenses. Read and attribute the underlying evidence.