LoFP LoFP / mobile clients on cellular networks that peer through regional hubs may geo-resolve differently than the user location.

Techniques

Sample rules

OpenAI ChatGPT Enterprise Impossible Travel Login

Description

Detects successful ChatGPT Enterprise logins for the same user email from source IPs whose geo-locations are separated by at least 1,000 km, with implied travel faster than 800 km/h, within a 24-hour window. That pattern can indicate account sharing, VPN or proxy egress mismatches, or an adversary authenticating from a geography far from the legitimate user’s baseline.

Detection logic

from logs-openai_chatgpt_enterprise.auth_log-*
| where
    data_stream.dataset == "openai_chatgpt_enterprise.auth_log" and
    event.action in ("login_success", "native_login_success") and
    user.email is not null and
    source.ip is not null and
    source.geo.location is not null and
    source.geo.country_iso_code is not null
| eval
    Esql.source_geo_lat = st_y(source.geo.location),
    Esql.source_geo_lon = st_x(source.geo.location)
| where Esql.source_geo_lat is not null and Esql.source_geo_lon is not null
| stats
    Esql.first_lat = first(Esql.source_geo_lat, @timestamp),
    Esql.first_lon = first(Esql.source_geo_lon, @timestamp),
    Esql.last_lat = last(Esql.source_geo_lat, @timestamp),
    Esql.last_lon = last(Esql.source_geo_lon, @timestamp),
    Esql.event_count = count(*),
    Esql.country_count = count_distinct(source.geo.country_iso_code),
    Esql.event_action_values = values(event.action),
    Esql.source_ip_values = values(source.ip),
    Esql.source_geo_country_iso_code_values = values(source.geo.country_iso_code),
    Esql.source_geo_region_name_values = values(source.geo.region_name),
    Esql.source_geo_city_name_values = values(source.geo.city_name),
    Esql.user_agent_original_values = values(user_agent.original),
    Esql.timestamp_first_seen = min(@timestamp),
    Esql.timestamp_last_seen = max(@timestamp)
  by user.email
| where Esql.event_count >= 2 and Esql.country_count >= 2
| eval
    Esql.p1 = to_geopoint(concat("POINT(", to_string(Esql.first_lon), " ", to_string(Esql.first_lat), ")")),
    Esql.p2 = to_geopoint(concat("POINT(", to_string(Esql.last_lon), " ", to_string(Esql.last_lat), ")"))
| eval
    Esql.distance_km = round(st_distance(Esql.p1, Esql.p2) / 1000.0, 0),
    Esql.window_minutes = date_diff("minute", Esql.timestamp_first_seen, Esql.timestamp_last_seen),
    Esql.travel_kmh = case(Esql.window_minutes > 0, round(Esql.distance_km * 60.0 / Esql.window_minutes, 0), null)
| where Esql.distance_km >= 1000 and Esql.travel_kmh >= 800
| keep user.email, Esql.*