Detection rules › Elastic
FortiGate Administrator Login from Multiple IP Addresses
This rule detects successful logins to the FortiGate management interface using the same Administrator account from multiple distinct source IP addresses within an 24-hour period. Administrator logins from multiple locations in a short time window may indicate credential sharing, compromised credentials, or unauthorized access and should be investigated.
MITRE ATT&CK coverage
| Tactic | Techniques |
|---|---|
| Initial Access | T1078 Valid Accounts |
Rule body elastic
[metadata]
creation_date = "2026/01/28"
integration = ["fortinet_fortigate"]
maturity = "production"
updated_date = "2026/04/10"
[rule]
author = ["Elastic"]
description = """
This rule detects successful logins to the FortiGate management interface using the same Administrator account from
multiple distinct source IP addresses within an 24-hour period. Administrator logins from multiple locations in a short
time window may indicate credential sharing, compromised credentials, or unauthorized access and should be investigated.
"""
from = "now-24h"
interval = "5m"
language = "esql"
license = "Elastic License v2"
name = "FortiGate Administrator Login from Multiple IP Addresses"
note = """## ## Triage and Analysis
### Investigating FortiGate Administrator Login from Multiple IP Addresses
This alert indicates that the same **Administrator** account successfully logged in to the FortiGate management interface from **multiple distinct source IP addresses** within an 24-hour period.
Because FortiGate administrator credentials grant full control over network security infrastructure, this behavior may indicate credential compromise, account sharing, or misuse of administrative access.
### Investigation Steps
- **Review the affected account**
- Identify the administrator account in `source.user.name`.
- Confirm whether the account is shared, personal, or service-related.
- Validate whether concurrent or near-concurrent access is expected.
- **Analyze source IP addresses**
- Review the list of `source.ip` values associated with the logins.
- Determine whether the IPs belong to trusted management networks, VPN pools, or jump hosts.
- Investigate geolocation differences using `source.geo.country_name`.
- **Assess timing and session behavior**
- Check whether logins occurred close together in time or overlapped.
- Identify whether access patterns suggest session hopping or credential reuse.
- **Review post-authentication activity**
- Examine FortiGate logs for configuration changes, policy updates, or administrative actions following the logins.
- Look for additional authentication attempts (successful or failed) from the same IPs or user.
- **Correlate with environment context**
- Verify maintenance windows, incident response activity, or operational tasks that could explain the behavior.
- Confirm whether administrators commonly access FortiGate via multiple networks or devices.
### False Positive Considerations
- Administrators connecting through VPNs with dynamic or rotating IP addresses.
- Access via bastion hosts, load-balanced management interfaces, or cloud-based management tools.
- Automation or orchestration systems using shared administrator credentials.
- Incident response or troubleshooting activity involving multiple access points.
### Response and Remediation
- **If the activity is expected**
- Document the behavior and consider tuning the rule or adding exceptions for known IP ranges or accounts.
- Encourage use of named accounts and centralized access paths.
- **If the activity is suspicious**
- Reset or rotate credentials for the affected administrator account.
- Review FortiGate configuration changes made during the session(s).
- Restrict administrative access to trusted IP ranges.
- Enforce MFA for administrative logins if not already enabled.
- Monitor for additional signs of lateral movement or persistence."""
references = [
"https://www.elastic.co/docs/reference/integrations/fortinet_fortigate",
"https://www.cisa.gov/news-events/alerts/2026/01/28/fortinet-releases-guidance-address-ongoing-exploitation-authentication-bypass-vulnerability-cve-2026",
]
risk_score = 73
rule_id = "8a556117-3f05-430e-b2eb-7df0100b4e3b"
severity = "high"
tags = [
"Use Case: Threat Detection",
"Tactic: Initial Access",
"Resources: Investigation Guide",
"Domain: Network",
"Domain: Identity",
"Data Source: Fortinet",
"Data Source: Fortinet FortiGate",
]
timestamp_override = "event.ingested"
type = "esql"
query = '''
FROM logs-fortinet_fortigate.*, filebeat-* metadata _id
| WHERE data_stream.dataset == "fortinet_fortigate.log" and
event.category == "authentication" and event.action == "login" and
event.outcome == "success" and source.user.roles == "Administrator" and
source.user.name is not null and source.ip is not null
| stats Esql.logon_count = count(*),
Esql.source_ip_count_distinct = COUNT_DISTINCT(source.ip),
Esql.max_timestamp = MAX(@timestamp),
Esql.source_ip_values = VALUES(source.ip),
Esql.message_values = VALUES(message),
Esql.source_geo_country_name_values = VALUES(source.geo.country_name) by source.user.name
// last logon event timestamp is within 6m of the rule execution time to avoid duplicates
| eval Esql.recent = DATE_DIFF("minute", Esql.max_timestamp, now())
| where Esql.recent <= 6 and Esql.logon_count >= 2 and Esql.source_ip_count_distinct >= 2
// move dynamic fields to ECS equivalent for rule exceptions
| eval source.ip = MV_FIRST(Esql.source_ip_values)
| keep source.ip, source.user.name, Esql.*
'''
[[rule.threat]]
framework = "MITRE ATT&CK"
[[rule.threat.technique]]
id = "T1078"
name = "Valid Accounts"
reference = "https://attack.mitre.org/techniques/T1078/"
[rule.threat.tactic]
id = "TA0001"
name = "Initial Access"
reference = "https://attack.mitre.org/tactics/TA0001/"
Stages and Predicates
Stage 1: from
FROM logs-fortinet_fortigate.*, filebeat-* metadata _id
Stage 2: where
| WHERE data_stream.dataset == "fortinet_fortigate.log" and
event.category == "authentication" and event.action == "login" and
event.outcome == "success" and source.user.roles == "Administrator" and
source.user.name is not null and source.ip is not null
Stage 3: stats
| stats Esql.logon_count = count(*),
Esql.source_ip_count_distinct = COUNT_DISTINCT(source.ip),
Esql.max_timestamp = MAX(@timestamp),
Esql.source_ip_values = VALUES(source.ip),
Esql.message_values = VALUES(message),
Esql.source_geo_country_name_values = VALUES(source.geo.country_name) by source.user.name
Stage 4: eval
| eval Esql.recent = DATE_DIFF("minute", Esql.max_timestamp, now())
Stage 5: where
| where Esql.recent <= 6 and Esql.logon_count >= 2 and Esql.source_ip_count_distinct >= 2
Stage 6: eval
| eval source.ip = MV_FIRST(Esql.source_ip_values)
Stage 7: keep
| keep source.ip, source.user.name, Esql.*
Indicators
Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.
| Field | Kind | Values |
|---|---|---|
Esql.logon_count | ge |
|
Esql.recent | le |
|
Esql.source_ip_count_distinct | ge |
|
data_stream.dataset | eq |
|
event.action | eq |
|
event.category | eq |
|
event.outcome | eq |
|
source.ip | is_not_null | |
source.user.name | is_not_null | |
source.user.roles | eq |
|
Output fields
Fields the rule emits when it matches. Chronicle authors list these in the outcome block; they appear on the detection and $risk_score drives alerting. Sentinel / Defender XDR rules build them up through project / summarize / extend stages. Sentinel maps these into alert fields via entityMappings and customDetails; Defender XDR custom detections surface them as alert fields directly.
| Field | Source |
|---|---|
source.ip | KEEP source.ip |
source.user.name | KEEP source.user.name |
Esql.* | KEEP Esql.* |