AWS SageMaker Execution Role Passed by Unusual Principal
Identifies the first time an IAM principal passes a given execution role (roleArn) to an Amazon SageMaker resource,
via CreateNotebookInstance, CreateTrainingJob, CreateProcessingJob, CreateAutoMLJob, or CreatePipeline. These
actions require iam:PassRole and attach an IAM role that the created resource then runs as. An adversary holding both
SageMaker create permissions and a broad iam:PassRole grant can pass a more privileged role to a resource they control
and execute code as that role, escalating privileges. The rule keys on the combination of the calling principal and the
passed roleArn, so it surfaces a principal using an execution role it has not used before in the last 7 days; a role
whose account differs from the caller's, or that is more privileged than the caller, is especially suspicious.
Elastic rule (View on GitHub)
1[metadata]
2creation_date = "2026/07/13"
3integration = ["aws"]
4maturity = "production"
5updated_date = "2026/07/13"
6
7[rule]
8author = ["Elastic"]
9description = """
10Identifies the first time an IAM principal passes a given execution role (`roleArn`) to an Amazon SageMaker resource,
11via `CreateNotebookInstance`, `CreateTrainingJob`, `CreateProcessingJob`, `CreateAutoMLJob`, or `CreatePipeline`. These
12actions require `iam:PassRole` and attach an IAM role that the created resource then runs as. An adversary holding both
13SageMaker create permissions and a broad `iam:PassRole` grant can pass a more privileged role to a resource they control
14and execute code as that role, escalating privileges. The rule keys on the combination of the calling principal and the
15passed `roleArn`, so it surfaces a principal using an execution role it has not used before in the last 7 days; a role
16whose account differs from the caller's, or that is more privileged than the caller, is especially suspicious.
17"""
18false_positives = [
19 """
20 MLOps pipelines and data science teams routinely create SageMaker resources with execution roles, and new pipelines
21 or team members appear as new principals on first use. Verify the principal in `aws.cloudtrail.user_identity.arn`,
22 the passed roleArn in `aws.cloudtrail.request_parameters`, and whether the role's privileges and the activity are
23 approved. Known automation roles can be excluded after validation.
24 """,
25]
26from = "now-7d"
27interval = "10m"
28language = "esql"
29license = "Elastic License v2"
30name = "AWS SageMaker Execution Role Passed by Unusual Principal"
31note = """## Triage and analysis
32
33### Investigating AWS SageMaker Execution Role Passed by Unusual Principal
34
35SageMaker resource-creation actions accept a `roleArn` execution role and require the caller to hold `iam:PassRole`
36for it. The created resource (notebook, training job, processing job, AutoML job, or pipeline) then runs as that
37role. This is a known cloud privilege-escalation path: a principal with SageMaker create rights and a broad
38`PassRole` permission can attach a more privileged role to a resource it controls and run code as that role. This
39rule keys on the principal and the passed `roleArn` together, so it flags the first time a principal uses a given
40execution role within the last 7 days, which should then be reviewed for over-privilege or a cross-account owner.
41
42#### Possible investigation steps
43
44- Identify the actor in `aws.cloudtrail.user_identity.arn`, and review `Esql.source_ip_values` and
45 `Esql.user_agent_original_values` for an unexpected origin.
46- Inspect `Esql.aws_cloudtrail_request_parameters_role_arn` and review that role's policies; determine whether it is
47 more privileged than the caller.
48- Determine whether the principal normally creates SageMaker resources and whether this aligns with an approved
49 pipeline or project.
50- Correlate with follow-on activity by the passed role, such as actions outside SageMaker, presigned URL generation,
51 or lifecycle configuration changes that would provide interactive execution as the role.
52
53### False positive analysis
54
55- Legitimate MLOps creates SageMaker resources with execution roles; new pipelines and users appear as new
56 principals on first use. Confirm the role and activity are approved and exclude known automation roles on
57 `aws.cloudtrail.user_identity.arn` after validation.
58
59### Response and remediation
60
61- If unauthorized, stop and delete the created resource, and review any actions taken by the passed role.
62- Rotate or restrict credentials for the principal if compromise is suspected, and constrain `iam:PassRole` and
63 SageMaker create permissions so principals can only pass narrowly scoped, approved execution roles.
64
65"""
66references = [
67 "https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html",
68 "https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateNotebookInstance.html",
69 "https://stratus-red-team.cloud/attack-techniques/AWS/aws.execution.sagemaker-update-lifecycle-config/",
70]
71risk_score = 73
72rule_id = "ca8c2751-5507-44f2-b58d-08958200cde9"
73severity = "high"
74tags = [
75 "Domain: Cloud",
76 "Data Source: AWS",
77 "Data Source: Amazon Web Services",
78 "Data Source: AWS SageMaker",
79 "Use Case: Threat Detection",
80 "Tactic: Privilege Escalation",
81 "Resources: Investigation Guide",
82]
83timestamp_override = "event.ingested"
84type = "esql"
85
86query = '''
87FROM logs-aws.cloudtrail-*
88| WHERE data_stream.dataset == "aws.cloudtrail"
89 AND event.provider == "sagemaker.amazonaws.com"
90 AND event.action IN (
91 "CreateNotebookInstance",
92 "CreateTrainingJob",
93 "CreateProcessingJob",
94 "CreateAutoMLJob",
95 "CreatePipeline"
96 )
97 AND event.outcome == "success"
98 AND aws.cloudtrail.user_identity.type != "AWSService"
99| GROK aws.cloudtrail.request_parameters """.*roleArn=(?<Esql.aws_cloudtrail_request_parameters_role_arn>arn:aws[a-z-]*:iam::[0-9]{12}:role/[^,}]+).*"""
100| WHERE Esql.aws_cloudtrail_request_parameters_role_arn IS NOT NULL
101| EVAL Esql.principal_arn = COALESCE(
102 aws.cloudtrail.user_identity.session_context.session_issuer.arn,
103 aws.cloudtrail.user_identity.arn
104 )
105| STATS
106 Esql.timestamp_min = MIN(@timestamp),
107 Esql.timestamp_max = MAX(@timestamp),
108 Esql.ingested_min = MIN(COALESCE(event.ingested, @timestamp)),
109 Esql.event_count = COUNT(*),
110 Esql.event_action_values = VALUES(event.action),
111 Esql.source_ip_values = VALUES(source.ip),
112 Esql.user_agent_original_values = VALUES(user_agent.original),
113 Esql.user_identity_arn_values = VALUES(aws.cloudtrail.user_identity.arn),
114 Esql.cloud_account_id_values = VALUES(cloud.account.id),
115 Esql.cloud_region_values = VALUES(cloud.region)
116 BY Esql.principal_arn,
117 Esql.aws_cloudtrail_request_parameters_role_arn
118| WHERE Esql.ingested_min >= NOW() - 10 minutes
119| KEEP Esql.*
120'''
121
122[[rule.threat]]
123framework = "MITRE ATT&CK"
124
125[[rule.threat.technique]]
126id = "T1078"
127name = "Valid Accounts"
128reference = "https://attack.mitre.org/techniques/T1078/"
129
130[[rule.threat.technique.subtechnique]]
131id = "T1078.004"
132name = "Cloud Accounts"
133reference = "https://attack.mitre.org/techniques/T1078/004/"
134
135[rule.threat.tactic]
136id = "TA0004"
137name = "Privilege Escalation"
138reference = "https://attack.mitre.org/tactics/TA0004/"
139
140[rule.investigation_fields]
141field_names = [
142 "Esql.principal_arn",
143 "Esql.aws_cloudtrail_request_parameters_role_arn",
144 "Esql.user_identity_arn_values",
145 "Esql.timestamp_min",
146 "Esql.timestamp_max",
147 "Esql.event_count",
148 "Esql.event_action_values",
149 "Esql.source_ip_values",
150 "Esql.user_agent_original_values",
151 "Esql.cloud_account_id_values",
152 "Esql.cloud_region_values",
153]
Triage and analysis
Investigating AWS SageMaker Execution Role Passed by Unusual Principal
SageMaker resource-creation actions accept a roleArn execution role and require the caller to hold iam:PassRole
for it. The created resource (notebook, training job, processing job, AutoML job, or pipeline) then runs as that
role. This is a known cloud privilege-escalation path: a principal with SageMaker create rights and a broad
PassRole permission can attach a more privileged role to a resource it controls and run code as that role. This
rule keys on the principal and the passed roleArn together, so it flags the first time a principal uses a given
execution role within the last 7 days, which should then be reviewed for over-privilege or a cross-account owner.
Possible investigation steps
- Identify the actor in
aws.cloudtrail.user_identity.arn, and reviewEsql.source_ip_valuesandEsql.user_agent_original_valuesfor an unexpected origin. - Inspect
Esql.aws_cloudtrail_request_parameters_role_arnand review that role's policies; determine whether it is more privileged than the caller. - Determine whether the principal normally creates SageMaker resources and whether this aligns with an approved pipeline or project.
- Correlate with follow-on activity by the passed role, such as actions outside SageMaker, presigned URL generation, or lifecycle configuration changes that would provide interactive execution as the role.
False positive analysis
- Legitimate MLOps creates SageMaker resources with execution roles; new pipelines and users appear as new
principals on first use. Confirm the role and activity are approved and exclude known automation roles on
aws.cloudtrail.user_identity.arnafter validation.
Response and remediation
- If unauthorized, stop and delete the created resource, and review any actions taken by the passed role.
- Rotate or restrict credentials for the principal if compromise is suspected, and constrain
iam:PassRoleand SageMaker create permissions so principals can only pass narrowly scoped, approved execution roles.
References
Related rules
- AWS Bedrock AgentCore Execution Role Used Outside Its Runtime
- AWS Sensitive IAM Operations Performed via CloudShell
- AWS Bedrock API Key Phantom User Activity Outside Bedrock
- AWS IAM Credentials Added to a Bedrock API Key Phantom User
- AWS SageMaker Notebook Lifecycle Configuration With Suspicious Script Content