Detection: Risk Rule for Dev Sec Ops by Repository

Updated Date: 2024-05-24 ID: 161bc0ca-4651-4c13-9c27-27770660cf67 Author: Bhavin Patel Type: Correlation Product: Splunk Enterprise Security

Description

The following analytic identifies high-risk activities within repositories by correlating repository data with risk scores. It leverages risk events from the Dev Sec Ops analytic stories, summing risk scores and capturing source and user information. The detection focuses on high-risk scores above 100 and sources with more than three occurrences. This activity is significant as it highlights repositories frequently targeted by threats, providing insights into potential vulnerabilities. If confirmed malicious, attackers could exploit these repositories, leading to data breaches or infrastructure compromise.

Search

1
2| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as sum_risk_score, values(All_Risk.annotations.mitre_attack.mitre_tactic) as annotations.mitre_attack.mitre_tactic, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Dev Sec Ops" All_Risk.risk_object_type = "other" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic 
3| `drop_dm_object_name(All_Risk)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| where source_count > 3 and sum_risk_score > 100 
7| `risk_rule_for_dev_sec_ops_by_repository_filter`

Data Source

Name	Platform	Sourcetype	Source	Supported App
N/A	N/A	N/A	N/A	N/A

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
risk_rule_for_dev_sec_ops_by_repository_filter	`search *`

risk_rule_for_dev_sec_ops_by_repository_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

ID	Technique	Tactic
T1204.003	Malicious Image	Execution
T1204	User Execution	Execution

KillChainPhase.INSTALLATION

NistCategory.DE_AE

Cis18Value.CIS_10

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 Notable	Yes
Rule Title	`%name%`
Rule Description	`%description%`
Notable Event Fields	user, dest
Creates Risk Event	False

This configuration file applies to all detections of type Correlation. These correlations will generate Notable Events.

Implementation

Ensure that all relevant detections in the Dev Sec Ops analytic stories are enabled and are configured to create risk events in Enterprise Security.

Known False Positives

Unknown

Associated Analytic Story

Dev Sec Ops

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
Correlation triggered for repository $risk_object$	70	70	100

The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`aws_ecr_risk_dataset.log`	`stash`
Integration	✅ Passing	Dataset	`aws_ecr_risk_dataset.log`	`stash`

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: 2