Detection: Windows Credentials from Web Browsers Saved in TEMP Folder

Updated Date: 2024-09-24 ID: b36b23ea-763c-417b-bd4a-6a378dabad1a Author: Teoderick Contreras, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects the creation of files containing passwords, cookies, and saved login account information by the Braodo stealer malware in temporary folders. Braodo often collects these credentials from browsers and applications, storing them in temp directories before exfiltration. This detection focuses on monitoring for the creation of files with patterns or formats commonly associated with stolen credentials. By identifying these activities, security teams can take needed action to prevent sensitive login data from being leaked, reducing the risk of unauthorized access to user accounts and systems.

1
2|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("login*", "pass*","cookie*","master_key*") Filesystem.file_path = "*\\temp\\*" by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time 
3| `drop_dm_object_name(Filesystem)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `windows_credentials_from_web_browsers_saved_in_temp_folder_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
windows_credentials_from_web_browsers_saved_in_temp_folder_filter search *
windows_credentials_from_web_browsers_saved_in_temp_folder_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
T1555.003 Credentials from Web Browsers Credential Access
T1555 Credentials from Password Stores Credential Access
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
APT3
APT33
APT37
APT41
Ajax Security Team
FIN6
HEXANE
Inception
Kimsuky
LAPSUS$
Leafminer
Malteiro
Molerats
MuddyWater
OilRig
Patchwork
RedCurl
Sandworm Team
Stealth Falcon
TA505
Volt Typhoon
ZIRCONIUM
APT33
APT39
APT41
Evilnum
FIN6
HEXANE
Leafminer
Malteiro
MuddyWater
OilRig
Stealth Falcon
Volt Typhoon

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 True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.

Known False Positives

unknown

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A known credential file name - [$file_name$] was saved in %temp% folder of [$dest$]. 64 80 80
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 XmlWinEventLog:Microsoft-Windows-Sysmon/Operational XmlWinEventLog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational XmlWinEventLog

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: 1