Okta Authentication Bypass via Skeleton Key Injection - Behavioral

Status: Experimental
Severity: high
Tags: Identity & Access Management, Okta, Active Directory, Defense Evasion:Modify Authentication Process, Persistence:Account Manipulation, Anomaly Detection
Reference: https://www.varonis.com/blog/okta-attack-vectors
Source: github.com/panther-labs/panther-analysis

Detects potential Okta authentication bypass via skeleton key injection using behavioral z-score analysis. Skeleton key attacks in Okta involve manipulating authentication policies to weaken MFA requirements (disabling requireFactor, zeroing maxSessionLifetime) and bulk-enrolling attacker-controlled authenticators on victim accounts. This detection builds a 90-day behavioral baseline for each admin's policy change and factor enrollment patterns, then identifies anomalous spikes in the last 7 days. Detection Logic: - Z-score: Spike in security-weakening policy changes (> 2σ above baseline) - Z-score: Spike in admin-on-behalf-of MFA factor enrollments (> 3σ above baseline) - Cold-start: First-time security weakening (no prior baseline - immediate high-confidence signal) - Cold-start: First-time admin-enrolled factors for other users Why This Matters: Skeleton key attacks require two steps: weaken authentication policies to reduce MFA friction, then enroll attacker-controlled authenticators on victim accounts. This detection catches both steps using behavioral baselines that adapt to legitimate admin workflows. Complementary Detection: Use alongside Okta.ADAgent.TokenAbuse.Behavioral for admin credential theft scenarios.

MITRE ATT&CK coverage

Tactic	Techniques
Persistence	`T1098` Account Manipulation, `T1556` Modify Authentication Process

Rule body yaml

AnalysisType: scheduled_rule
Filename: okta_skeleton_key_bypass_behavioral.py
RuleID: "Okta.SkeletonKeyBypass.Behavioral"
DisplayName: "Okta Authentication Bypass via Skeleton Key Injection - Behavioral"
Enabled: true
ScheduledQueries:
  - Query.Okta.SkeletonKeyBypassBehavioral
Severity: High  # Default, dynamic severity in rule function
Status: Experimental
Tags:
  - Identity & Access Management
  - Okta
  - Active Directory
  - Defense Evasion:Modify Authentication Process
  - Persistence:Account Manipulation
  - Anomaly Detection
Reports:
  MITRE ATT&CK:
    - TA0005:T1556  # Modify Authentication Process
    - TA0003:T1098  # Account Manipulation
Description: |
  Detects potential Okta authentication bypass via skeleton key injection using behavioral z-score analysis.

  Skeleton key attacks in Okta involve manipulating authentication policies to weaken MFA requirements
  (disabling requireFactor, zeroing maxSessionLifetime) and bulk-enrolling attacker-controlled
  authenticators on victim accounts. This detection builds a 90-day behavioral baseline for each
  admin's policy change and factor enrollment patterns, then identifies anomalous spikes in the last
  7 days.

  **Detection Logic:**
  - Z-score: Spike in security-weakening policy changes (> 2σ above baseline)
  - Z-score: Spike in admin-on-behalf-of MFA factor enrollments (> 3σ above baseline)
  - Cold-start: First-time security weakening (no prior baseline - immediate high-confidence signal)
  - Cold-start: First-time admin-enrolled factors for other users

  **Why This Matters:**
  Skeleton key attacks require two steps: weaken authentication policies to reduce MFA friction,
  then enroll attacker-controlled authenticators on victim accounts. This detection catches both
  steps using behavioral baselines that adapt to legitimate admin workflows.

  **Complementary Detection:**
  Use alongside `Okta.ADAgent.TokenAbuse.Behavioral` for admin credential theft scenarios.

Reference: https://www.varonis.com/blog/okta-attack-vectors
Runbook: |
  1. Review recent_total_weakenings and recent_max_weakenings_per_hour for admin_email against baseline_total_weakenings - query Okta SystemLog for policy.rule.update and policy.lifecycle.update events by admin_email in the 24 hours around recent_policy_first_event, focusing on changedAttributes containing requireFactor=false or maxSessionLifetimeMinutes=0
  2. Check recent_total_admin_enrollments and z_score_admin_enrollments for user.mfa.factor.activate events by admin_email in the 7 days around recent_enrollment_first_event - identify target accounts enrolled and verify whether enrollment was authorized and factor types are consistent with corporate standards
  3. Search for Okta.ADAgent.TokenAbuse.Behavioral or other privileged account alerts for admin_email in the 48 hours before recent_policy_first_event to determine whether the admin account itself was compromised prior to the policy manipulation

DedupPeriodMinutes: 1440  # 24 hours
SummaryAttributes:
  - admin_email
  - anomaly_severity_score
  - recent_total_weakenings
Tests:
  # Positive Tests - Should Alert
  - Name: First-Time Security Policy Weakening - Critical Severity
    ExpectedResult: true
    Log:
      {
        "admin_email": "attacker@company.com",
        "baseline_total_policy_changes": 0,
        "baseline_total_weakenings": 0,
        "baseline_mean_weakenings_per_hour": 0.0,
        "baseline_total_enrollments": 0,
        "baseline_total_admin_enrollments": 0,
        "recent_total_policy_changes": 3,
        "recent_max_policy_changes_per_hour": 3,
        "recent_total_weakenings": 2,
        "recent_max_weakenings_per_hour": 2,
        "recent_total_enrollments": 0,
        "recent_total_admin_enrollments": 0,
        "z_score_policy_changes": null,
        "z_score_security_weakenings": null,
        "z_score_enrollments": null,
        "z_score_admin_enrollments": null,
        "is_policy_volume_anomaly": false,
        "is_security_weakening_anomaly": false,
        "is_admin_enrollment_anomaly": false,
        "is_first_time_security_weakening": true,
        "is_first_time_admin_enrollment": false,
        "is_anomalous": true,
        "anomaly_severity_score": 20.0,
        "recent_policy_first_event": "2024-01-15 02:00:00",
        "recent_policy_last_event": "2024-01-15 03:00:00"
      }

  - Name: Security Weakening Z-Score Anomaly with Enrollment Spike - Critical Severity
    ExpectedResult: true
    Log:
      {
        "admin_email": "compromised-admin@company.com",
        "baseline_total_policy_changes": 100,
        "baseline_active_days_policy": 30,
        "baseline_mean_policy_changes_per_hour": 2.0,
        "baseline_stddev_policy_changes_per_hour": 1.0,
        "baseline_total_weakenings": 0,
        "baseline_mean_weakenings_per_hour": 0.0,
        "recent_total_policy_changes": 20,
        "recent_max_policy_changes_per_hour": 15,
        "recent_total_weakenings": 5,
        "recent_max_weakenings_per_hour": 5,
        "baseline_total_enrollments": 50,
        "baseline_active_days_enrollment": 20,
        "baseline_mean_enrollments_per_hour": 1.0,
        "baseline_stddev_enrollments_per_hour": 0.5,
        "baseline_total_admin_enrollments": 2,
        "baseline_mean_admin_enrollments_per_hour": 0.1,
        "recent_total_enrollments": 5,
        "recent_max_enrollments_per_hour": 3,
        "recent_total_admin_enrollments": 2,
        "recent_max_admin_enrollments_per_hour": 1,
        "z_score_policy_changes": 13.0,
        "z_score_security_weakenings": 50.0,
        "z_score_enrollments": 4.0,
        "z_score_admin_enrollments": 9.0,
        "is_security_weakening_anomaly": true,
        "is_first_time_security_weakening": false,
        "is_first_time_admin_enrollment": false,
        "is_anomalous": true,
        "anomaly_severity_score": 109.0,
        "recent_policy_first_event": "2024-01-15 10:00:00",
        "recent_policy_last_event": "2024-01-15 14:00:00"
      }

  - Name: Bulk Admin Factor Enrollment Spike - High Severity
    ExpectedResult: true
    Log:
      {
        "admin_email": "svc-admin@company.com",
        "baseline_total_policy_changes": 50,
        "baseline_active_days_policy": 25,
        "baseline_total_weakenings": 0,
        "baseline_mean_weakenings_per_hour": 0.0,
        "recent_total_policy_changes": 5,
        "recent_max_policy_changes_per_hour": 2,
        "recent_total_weakenings": 0,
        "recent_max_weakenings_per_hour": 0,
        "baseline_total_enrollments": 30,
        "baseline_active_days_enrollment": 15,
        "baseline_mean_enrollments_per_hour": 0.5,
        "baseline_stddev_enrollments_per_hour": 0.3,
        "baseline_total_admin_enrollments": 1,
        "baseline_mean_admin_enrollments_per_hour": 0.02,
        "recent_total_enrollments": 20,
        "recent_max_enrollments_per_hour": 8,
        "recent_total_admin_enrollments": 8,
        "recent_max_admin_enrollments_per_hour": 8,
        "z_score_enrollments": 25.0,
        "z_score_admin_enrollments": 79.8,
        "is_policy_volume_anomaly": false,
        "is_security_weakening_anomaly": false,
        "is_admin_enrollment_anomaly": true,
        "is_first_time_security_weakening": false,
        "is_first_time_admin_enrollment": false,
        "is_anomalous": true,
        "anomaly_severity_score": 79.8,
        "recent_enrollment_first_event": "2024-01-15 09:00:00",
        "recent_enrollment_last_event": "2024-01-15 11:00:00"
      }

  - Name: First-Time Admin Enrollment - Medium Severity
    ExpectedResult: true
    Log:
      {
        "admin_email": "new-admin@company.com",
        "baseline_total_policy_changes": 0,
        "baseline_total_weakenings": 0,
        "baseline_total_enrollments": 0,
        "baseline_total_admin_enrollments": 0,
        "recent_total_policy_changes": 0,
        "recent_total_weakenings": 0,
        "recent_total_enrollments": 3,
        "recent_total_admin_enrollments": 3,
        "recent_max_admin_enrollments_per_hour": 3,
        "z_score_policy_changes": null,
        "z_score_security_weakenings": null,
        "z_score_admin_enrollments": null,
        "is_first_time_security_weakening": false,
        "is_first_time_admin_enrollment": true,
        "is_anomalous": true,
        "anomaly_severity_score": 9.0,
        "recent_enrollment_first_event": "2024-01-15 16:00:00",
        "recent_enrollment_last_event": "2024-01-15 16:00:00"
      }

  # Note: rule() only validates admin_email; anomaly thresholds are enforced at the query level.
  # All rows reaching rule() with a valid admin_email are expected to return true.

  # Negative Tests - Should Not Alert
  - Name: Malformed Row - Missing Admin Email
    ExpectedResult: false
    Log:
      {
        "baseline_total_policy_changes": 100,
        "recent_total_weakenings": 5,
        "is_anomalous": true,
        "anomaly_severity_score": 50.0
      }

  - Name: Malformed Row - Empty Admin Email String
    ExpectedResult: false
    Log:
      {
        "admin_email": "",
        "baseline_total_policy_changes": 100,
        "recent_total_weakenings": 5,
        "is_anomalous": true,
        "anomaly_severity_score": 50.0
      }

Detection logic

Condition

admin_email is_not_null

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
`admin_email`	is_not_null	(no value, null check)

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
`admin_email`
`recent_total_weakenings`
`recent_total_admin_enrollments`
`z_score_security_weakenings`
`z_score_admin_enrollments`
`anomaly_severity_score`
`is_first_time_security_weakening`
`is_first_time_admin_enrollment`
`baseline_total_weakenings`
`recent_policy_first_event`
`recent_policy_last_event`

`j` / `k`	Scroll down / up
`d` / `u`	Half-page down / up
`gg` / `G`	Top / bottom
`h` / `l`	History back / forward
`f`	Follow link (`Shift` = new tab)
`/`	Focus search
`?`	Toggle this help
`↑` / `↓`	Navigate search results
`Enter`	Open highlighted result
`Esc`	Close results / dialog

`type:`	`events` / `rules` / `providers`
`vendor:`	`sigma` / `elastic` / `splunk` / `kusto` / `chronicle` (vendor name alone also works: `sigma:`, `kql:`, `secops:`…)
`tactic:`	TA-id, slug, or name: `credential_access`, `TA0006`
`technique:`	technique or sub-technique ID: `T1003`, `T1003.001` (alias `tech:`)
`severity:`	`critical` / `high` / `medium` / `low` / `informational` (alias `sev:`)
`risk_score`	Numeric comparison on the Elastic risk score (0 to 100): `risk_score>50`, `risk_score<=20`, `risk_score=99` (alias `risk`; Elastic rules only)
`stages:`	Rules with exactly N pipeline stages
`correlation:`	`single_event` / `sequence` / `alternatives` / `alternatives_cross_log` / `all_required` / `correlated`
`with:`	Co-occurrence event-id; stacks (`with:4624 with:4769`) to require all, while a comma list in one occurrence (`with:4624,4769`) is an either-or group. Implies multi-event
`like:`	Structural neighbors of a rule slug (equivalents + subsumption stricter / broader): `like:comsvcs_lsass_memory_dump-splunk-sysmon`
`groupby:`	Entity-grouping substring match against `group_by_keys`: `groupby:user`, `groupby:host`
`uses:`	Rules whose predicate tree touches the field (any kind, any value): `uses:CommandLine`
`excludes:`	Rules with top-level `not()` clauses on the field (FP whitelists): `excludes:ParentImage`
`field:` / `value:`	Predicate search; narrows rule cards to those with a matching leaf and drives the indicator tier. Unquoted = substring, wildcards allowed (`value:mimikatz`)
`indicator:`	Shorthand for `field:F value:V`: `indicator:Image=*\powershell.exe`
`kind:`	Filter by predicate kind. Narrows rule cards to those carrying a matching predicate leaf (`vendor:elastic kind:cidr_match`) and drives the indicator tier: `contains` / `starts_with` / `ends_with` / `regex` / `cidr` / `eq` / `in` … (operator aliases `op:`/`match:`)
`has:` / `no:`	`sample`, `field`, `notes`, `refs`, `trace`, `thirdparty`, `rule`, `pattern`, `timewindow`, `threshold`, `newterms`, `sigma`/`elastic`/`splunk`/`kusto`/`chronicle`
`-op:val`	Exclude matches; works on most operators but not `type:`/`like:`/`has:`/`no:` (use `no:<flag>` to exclude a rule flag): `tactic:execution -vendor:splunk`. Standalone `-kind:`/`-field:`/`-value:` drop every rule carrying a matching predicate leaf (`type:rules -kind:is_null`)
`field:"…"` / `value:"…"`	Quoted value = anchored exact match (also allows spaces): `value:"net user"`
`a,b`	Comma = OR inside one operator (`vendor:sigma,elastic`, `severity:high,critical`); repeating a facet merges the same way. `field:`/`value:` never split (literal commas)
`vendors:` / `stage:`	Singular and plural spellings fold to the canonical operator and value: `tactics:` = `tactic:`, `type:event` = `type:events`, `correlation:sequences` = `correlation:sequence`, `has:thresholds` = `has:threshold`
`"quoted phrase"`	Exact-match a multi-word phrase (free text)