Detection rules › Splunk
AWS Exfiltration via Anomalous GetObject API Activity
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
| Tactic | Techniques |
|---|---|
| Collection | T1119 Automated Collection |
Rules detecting the same action
Other rules on this platform that filter on the same API call or operation.
- AWSCloudTrail - S3 bucket suspicious ransomware activity (Kusto)
- AWSCloudTrail - S3 Object Exfiltration from Anonymous User (Kusto)
- AWSCloudTrail - Successful brute force attack on S3 Bucket (Kusto)
- S3 Access Via VPC Endpoint From External IP (Panther)
- Suspicious access of BEC related documents in AWS S3 buckets (Kusto)
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.
| Field | Kind | Values |
|---|---|---|
action | eq |
|
eventName | eq |
|
probable_cause | eq |
|
sourcetype | eq |
|
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.
| Stage | Term |
|---|---|
| 5 | anomalydetection |
| 5 | "count" |
| 5 | "user" |