Detection rules › Splunk

AWS Exfiltration via Anomalous GetObject API Activity

Status
production
Severity
low
Group by
aws::awsRegion, aws::recipientAccountId, aws::userAgent, dest, signature, src, user, vendor_product
Author
Bhavin Patel, Splunk
Source
github.com/splunk/security_content

The following analytic identifies anomalous GetObject API activity in AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail logs and uses the anomalydetection command to detect unusual patterns in the frequency of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" within a 10-minute window. This activity is significant as it may indicate unauthorized data access or exfiltration from S3 buckets. If confirmed malicious, attackers could exfiltrate sensitive data, leading to data breaches and compliance violations.

MITRE ATT&CK coverage

TacticTechniques
CollectionT1119 Automated Collection

Rules detecting the same action

Other rules on this platform that filter on the same API call or operation.

Rule body splunk

name: AWS Exfiltration via Anomalous GetObject API Activity
id: e4384bbf-5835-4831-8d85-694de6ad2cc6
version: 10
creation_date: '2023-04-10'
modification_date: '2026-05-13'
author: Bhavin Patel, Splunk
status: production
type: Anomaly
description: The following analytic identifies anomalous GetObject API activity in AWS, indicating potential data exfiltration attempts. It leverages AWS CloudTrail logs and uses the `anomalydetection` command to detect unusual patterns in the frequency of GetObject API calls by analyzing fields such as "count," "user_type," and "user_arn" within a 10-minute window. This activity is significant as it may indicate unauthorized data access or exfiltration from S3 buckets. If confirmed malicious, attackers could exfiltrate sensitive data, leading to data breaches and compliance violations.
data_source:
    - AWS CloudTrail GetObject
search: |-
    `cloudtrail` eventName=GetObject
      | bin _time span=10m
      | rename user_name as user
      | stats count values(requestParameters.bucketName) as bucketName
        BY signature dest user
           user_agent src vendor_account
           vendor_region vendor_product
      | anomalydetection "count" "user" action=annotate
      | search probable_cause=*
      | `aws_exfiltration_via_anomalous_getobject_api_activity_filter`
how_to_implement: You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.
known_false_positives: It is possible that a user downloaded these files to use them locally and there are AWS services in configured that perform these activities for a legitimate reason. Filter is needed.
references:
    - https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/
    - https://help.splunk.com/en/splunk-enterprise/search/spl-search-reference/9.4/search-commands/anomalydetection
    - https://www.vectra.ai/blogpost/abusing-the-replicator-silently-exfiltrating-data-with-the-aws-s3-replication-service
drilldown_searches:
    - name: View the detection results for - "$user$"
      search: '%original_detection_search% | search  user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: 7d
      latest_offset: "0"
intermediate_findings:
    entities:
        - field: user
          type: user
          score: 20
          message: Anomalous S3 activities detected by user $user$ from $src$
threat_objects:
    - field: src
      type: ip_address
analytic_story:
    - Data Exfiltration
asset_type: AWS Account
mitre_attack_id:
    - T1119
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: cloud
security_domain: threat
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_exfil_high_no_getobject/cloudtrail.json
          sourcetype: aws:cloudtrail
          source: aws_cloudtrail
      test_type: unit

Stages and Predicates

Stage 1: search

`cloudtrail` eventName=GetObject

Stage 2: bucket

| bin _time span=10m

Stage 3: rename

| rename user_name as user

Stage 4: stats

| stats count values(requestParameters.bucketName) as bucketName
    BY signature dest user
       user_agent src vendor_account
       vendor_region vendor_product

Stage 5: search

| anomalydetection "count" "user" action=annotate

Stage 6: search

| search probable_cause=*

Stage 7: search

| `aws_exfiltration_via_anomalous_getobject_api_activity_filter`

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.

FieldKindValues
actioneq
  • annotate
eventNameeq
  • GetObject
probable_causeeq
  • *
sourcetypeeq
  • aws:cloudtrail

Search terms

Bare-string tokens in the SPL search body. Splunk matches each token against _raw (the untyped raw event text) anywhere it appears, not against a specific field. These don't surface in the Indicators table because they aren't predicates on a known field.

StageTerm
5anomalydetection
5"count"
5"user"