A machine learning job detected an unusual error in a CloudTrail message. These can be byproducts of attempted or successful persistence, privilege escalation, defense evasion, discovery, lateral movement, or collection.
Rule type: machine_learning
Machine learning job: rare_error_code
Machine learning anomaly threshold: 50
Risk score: 21
Runs every: 15 minutes
Searches indices from: now-60m (Date Math format, see also
Additional look-back time)
Maximum alerts per execution: 100
Version: 3 (version history)
Added (Elastic Stack release): 7.9.0
Last modified (Elastic Stack release): 7.12.0
Rule authors: Elastic
Rule license: Elastic License v2
Rare and unusual errors may indicate an impending service failure state. Rare and unusual user error activity can also be due to manual troubleshooting or reconfiguration attempts by insufficiently privileged users, bugs in cloud automation scripts or workflows, or changes to IAM privileges.
Alerts from this rule indicate a rare and unusual error code that is associated with the response to an AWS API command or method call. Here are some possible avenues of investigation:
Examine the history of the error. Has it manifested before? If the error,
which is visible in the
aws.cloudtrail.error_code field, manifested only very recently, it might be related to recent changes in an automation module or script.
- Examine the request parameters. These may provide indications as to the nature of the task being performed when the error occurred. Is the error related to unsuccessful attempts to enumerate or access objects, data, or secrets? If so, this can sometimes be a byproduct of discovery, privilege escalation, or lateral movement attempts.
Consider the user as identified in the
user.namefield. Is this activity part of an expected workflow for the user context? Examine the user identity in the
aws.cloudtrail.user_identity.arnfield and the access key id in the
aws.cloudtrail.user_identity.access_key_idfield, which can help identify the precise user context. The user agent details in the
user_agent.originalfield may also indicate what type of client made the request.
- Consider the source IP address and geolocation for the calling user who issued the command. Do they look normal for the calling user? If the source is an EC2 IP address, is it associated with an EC2 instance in one of your accounts or could it be sourcing from an EC2 instance not under your control? If it is an authorized EC2 instance, is the activity associated with normal behavior for the instance role or roles? Are there any other alerts or signs of suspicious activity involving this instance?
- Version 3 (7.12.0 release)
- Formatting only
- Version 2 (7.10.0 release)
- Formatting only
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