| ID | Technique | Tactic |
|---|---|---|
| T1204.003 | Malicious Image | Execution |
| T1204 | User Execution | Execution |
Detection: ASL AWS ECR Container Upload Unknown User
Description
The following analytic detects unauthorized container uploads to AWS Elastic Container Service (ECR) by monitoring AWS CloudTrail events. It identifies instances where a new container is uploaded by a user not previously recognized as authorized. This detection is crucial for a SOC as it can indicate a potential compromise or misuse of AWS ECR, which could lead to unauthorized access to sensitive data or the deployment of malicious containers. By identifying and investigating these events, organizations can mitigate the risk of data breaches or other security incidents resulting from unauthorized container uploads. The impact of such an attack could be significant, compromising the integrity and security of the organization's cloud environment.
Search
1`amazon_security_lake` api.operation=PutImage NOT `aws_ecr_users_asl`
2| stats count min(_time) as firstTime max(_time) as lastTime by api.operation actor.user.account_uid actor.user.name actor.user.uid http_request.user_agent src_endpoint.ip cloud.region
3| rename actor.user.name as user, src_endpoint.ip as src_ip, cloud.region as region, http_request.user_agent as user_agent, actor.user.account_uid as aws_account_id
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `asl_aws_ecr_container_upload_unknown_user_filter`
Data Source
No data sources specified for this detection.
Macros Used
| Name | Value |
|---|---|
| amazon_security_lake | sourcetype=aws:cloudtrail:lake |
| asl_aws_ecr_container_upload_unknown_user_filter | search * |
asl_aws_ecr_container_upload_unknown_user_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
MITRE ATT&CK
Kill Chain Phases
NIST
CIS
Threat Actors
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_13
TeamTNT
LAPSUS$
Scattered Spider
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
| Setting | Value |
|---|---|
| Disabled | true |
| Cron Schedule | 0 * * * * |
| Earliest Time | -70m@m |
| Latest Time | -10m@m |
| Schedule Window | auto |
| Creates Risk Event | True |
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.
Implementation
The detection is based on Amazon Security Lake events from Amazon Web Services (AWS), which is a centralized data lake that provides security-related data from AWS services. To use this detection, you must ingest CloudTrail logs from Amazon Security Lake into Splunk. To run this search, ensure that you ingest events using the latest version of Splunk Add-on for Amazon Web Services (https://splunkbase.splunk.com/app/1876) or the Federated Analytics App.
Known False Positives
unknown
Associated Analytic Story
Risk Based Analytics (RBA)
| Risk Message | Risk Score | Impact | Confidence |
|---|---|---|---|
| Container uploaded from unknown user $user$ | 49 | 70 | 70 |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
References
Detection Testing
| Test Type | Status | Dataset | Source | Sourcetype |
|---|---|---|---|---|
| Validation | ✅ Passing | N/A | N/A | N/A |
| Unit | ✅ Passing | Dataset | aws_asl |
aws:cloudtrail:lake |
| Integration | ✅ Passing | Dataset | aws_asl |
aws:cloudtrail:lake |
Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
Source: GitHub | Version: 3