T1005 Data from Local System — Attack Pattern

Description

Adversaries may search local system sources, such as file systems, configuration files, local databases, virtual machine files, or process memory, to find files of interest and sensitive data prior to Exfiltration. Adversaries may do this using a Command and Scripting Interpreter, such as cmd as well as a Network Device CLI, which have functionality to interact with the file system to gather information.(Citation: show_run_config_cmd_cisco) Adversaries may also use Automated Collection on the local system.

MITRE ATT&CK Detection Strategies
1

DET0380 Detection of Local Data Collection Prior to Exfiltration

AN1074 ESXi

Adversaries accessing datastore or configuration files via `vim-cmd`, `esxcli`, or SCP to extract logs, VMs, or host configurations.

esxis:vmkernel esxi:hostd

AN1071 Linux

Adversaries using bash scripts or tools to recursively enumerate user home directories, config files, or SSH keys.

auditd:SYSCALL auditd:SYSCALL

AN1070 Windows

Adversaries collecting local files via PowerShell, WMI, or direct file API calls often include recursive file listings, targeted file reads, and temporary file staging.

WinEventLog:Security WinEventLog:Sysmon

+2 more analytics

MITRE ATT&CK Mitigations
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M1057

Data Loss Prevention

Data Loss Prevention (DLP) involves implementing strategies and technologies to identify, categorize, monitor, and control the movement of sensitive data within an organization. This includes protecting data formats indicative of Personally Identifiable Information (PII), intellectual property, or financial data from unauthorized access, transmission, or exfiltration. DLP solutions integrate with network, endpoint, and cloud platforms to enforce security policies and prevent accidental or malicious data leaks. (Citation: PurpleSec Data Loss Prevention) This mitigation can be implemented through the following measures: Sensitive Data Categorization: - Use Case: Identify and classify data based on sensitivity (e.g., PII, financial data, trade secrets). - Implementation: Use DLP solutions to scan and tag files containing sensitive information using predefined patterns, such as Social Security Numbers or credit card details. Exfiltration Restrictions: - Use Case: Prevent unauthorized transmission of sensitive data. - Implementation: Enforce policies to block unapproved email attachments, unauthorized USB usage, or unencrypted data uploads to cloud storage. Data-in-Transit Monitoring: - Use Case: Detect and prevent the transmission of sensitive data over unapproved channels. - Implementation: Deploy network-based DLP tools to inspect outbound traffic for sensitive content (e.g., financial records or PII) and block unapproved transmissions. Endpoint Data Protection: - Use Case: Monitor and control sensitive data usage on endpoints. - Implementation: Use endpoint-based DLP agents to block copy-paste actions of sensitive data and unauthorized printing or file sharing. Cloud Data Security: - Use Case: Protect data stored in cloud platforms. - Implementation: Integrate DLP with cloud storage platforms like Google Drive, OneDrive, or AWS to monitor and restrict sensitive data sharing or downloads.

Data from Local System

Description

MITRE ATT&CK Detection Strategies 1

MITRE ATT&CK Mitigations 1

Details

MITRE ATT&CK Detection Strategies
1

MITRE ATT&CK Mitigations
1