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MITRE ATT&CK® Technique

T1213.006: Databases

Adversaries may leverage databases to mine valuable information. These databases may be hosted on-premises or in the cloud (both in platform-as-a-service and software-as-a-service environments).

Examples of databases from which information may be collected include MySQL, PostgreSQL, MongoDB, Amazon Relational Database Service, Azure SQL Database, Google Firebase, and Snowflake. Databases may include a variety of information of interest to adversaries, such as usernames, hashed passwords, personally identifiable information, and financial data. Data collected from databases may be used for Lateral Movement, Command and Control, or Exfiltration. Data exfiltrated from databases may also be used to extort victims or may be sold for profit.[1]

EnterpriseT1213.006Sub-techniqueObject v1.0 Modified
Glexia's Take

Analyst context for executives and security teams

Analyst confidence High

Database collection matters because databases often concentrate the records that create the highest business risk if accessed: usernames, hashed passwords, personally identifiable information, and financial data. This ATT&CK sub-technique applies across on-premises and cloud-hosted database environments, including IaaS, SaaS, Linux, macOS, and Windows contexts. For leaders, the key question is not only whether databases are patched or encrypted, but whether the organization can prove who accessed sensitive data, from where, and whether access or dump activity was abnormal.

Executive priority

Prioritize this behavior where databases support regulated data, customer trust, revenue operations, or incident notification obligations. The ATT&CK relationships connect this technique to multiple campaigns, groups, and software entries, showing it is a broadly relevant collection behavior rather than a niche platform issue. Executives should ask whether database access governance, audit logging, encryption, account lifecycle controls, and user training are sufficient to support incident response decisions, compliance evidence, and containment if suspicious database access is identified.

Technical view

SOC, detection engineering, and IR teams should validate coverage for suspicious database access and database dump activity across both on-premises and cloud database services. ATT&CK does not provide native detection text for this sub-technique, but the related DET0242 detection strategy indicates a detection direction around suspicious database access and dump activity across environments. Teams should map database audit events, identity events, administrative actions, query/export activity, and cloud service logs to collection-stage use cases. Because this is a sub-technique of Data from Information Repositories, detections should be correlated with account misuse, unusual repository access, large data reads, exports, and follow-on exfiltration investigation paths where local telemetry supports it.

Likely telemetry

  • Database audit logs for logins, failed logins, queries, exports, dumps, privilege changes, and administrative actions
  • Cloud database service logs for managed database access and configuration activity, including PaaS and SaaS database environments
  • Identity and access management logs showing user, service account, role, token, and session activity tied to database access
  • Endpoint and server logs from Linux, macOS, and Windows systems that host database clients, database servers, scripts, or dump utilities
  • Network or proxy telemetry showing unusual database connections, access locations, or large outbound data movement where collected

Detection direction

  • Validate whether DET0242-style logic is implemented for suspicious database access and dump activity across cloud and on-premises environments.
  • Baseline normal database access by user, service account, application, source location, time, and volume so unusual access is distinguishable from maintenance and reporting jobs.
  • Tune for false positives from backups, migrations, analytics exports, administrative troubleshooting, and scheduled data warehouse operations.
  • Correlate database access with identity events, privilege changes, new account use, unusual session context, and access from unexpected platforms or locations.
  • Check blind spots in SaaS and managed database logging, especially where teams assume the provider logs activity but have not enabled, retained, or integrated those logs.

Mitigation priorities

  • Start with user account management: enforce least privilege, remove stale access, control service accounts, and regularly review database roles and permissions.
  • Enable and retain auditing for database, cloud service, and identity activity so suspicious collection can be investigated and compliance evidence can be produced.
  • Apply secure software and database configuration, reducing unnecessary external exposure, risky defaults, and overly permissive access paths.
  • Encrypt sensitive information at rest, in transit, and where applicable during processing, recognizing that encryption complements but does not replace access control and monitoring.
  • Use user training where human interaction may lead to credential compromise or unsafe handling of database access, especially for administrators, developers, analysts, and contractors.
Analyst notes and limits

The official object frames this as collection from databases such as MySQL, PostgreSQL, MongoDB, Amazon RDS, Azure SQL Database, Google Firebase, and Snowflake. Relationships show mitigation coverage through User Training, User Account Management, Encrypt Sensitive Information, Audit, and Software Configuration, plus a related detection strategy for suspicious database access and dump activity. Related campaigns, groups, and software indicate this behavior appears across multiple ATT&CK-tracked contexts, but local risk should be based on the organization’s actual database estate and data sensitivity.

MITRE does not provide official detection text for this sub-technique in the supplied fields. This take does not assert active exploitation, customer exposure, attribution, or guaranteed detection coverage. Concrete detection logic, thresholds, and control validation require local telemetry, database architecture, cloud logging configuration, account models, and data classification evidence.

Official MITRE ATT&CK definition

Databases

Adversaries may leverage databases to mine valuable information. These databases may be hosted on-premises or in the cloud (both in platform-as-a-service and software-as-a-service environments).

Examples of databases from which information may be collected include MySQL, PostgreSQL, MongoDB, Amazon Relational Database Service, Azure SQL Database, Google Firebase, and Snowflake. Databases may include a variety of information of interest to adversaries, such as usernames, hashed passwords, personally identifiable information, and financial data. Data collected from databases may be used for Lateral Movement, Command and Control, or Exfiltration. Data exfiltrated from databases may also be used to extort victims or may be sold for profit.[1]

View the same entry on attack.mitre.org (MITRE-hosted reference; in-page links above use the Glexia ATT&CK library.)

Glexia analysis

How security teams should use this page

Treat this object as behavior context, not an attribution claim. Validate the related groups, software, data sources, and mitigations against official ATT&CK relationships and your own telemetry before making control-coverage decisions.

ATT&CK relationship table

Related techniques

This mirrors the MITRE pattern of making group, software, campaign, and technique relationships scannable. Relationship notes come from mirrored ATT&CK relationship text when available.

1 rows
Domain ID Name Relationship / procedure
Enterprise T1213 Data from Information Repositories This object subtechnique of Data from Information Repositories.
Associated objects

Groups, software, and campaigns

Group Enterprise

G0034: Sandworm Team

Sandworm Team is a destructive threat group that has been attributed to Russia's General Staff Main Intelligence Directorate (GRU) Main Center for Special Technologies (GTsST) military unit 74455.[1][2] This group has been active since at least 2009.[3][4][5][6]

In October 2020, the US indicted six GRU Unit 74455 officers associated with Sandworm Team for the following cyber operations: the 2015 and 2016 attacks against Ukrainian electrical companies and government organizations, the 2017 worldwide NotPetya attack, targeting of the 2017 French presidential campaign, the 2018 Olympic Destroyer attack against the Winter Olympic Games, the 2018 operation against the Organisation for the Prohibition of Chemical Weapons, and attacks against the country of Georgia in 2018 and 2019.[1][2] Some of these were conducted with the assistance of GRU Unit 26165, which is also referred to as APT28.[7]

Group Enterprise

G0037: FIN6

FIN6 is a cyber crime group that has stolen payment card data and sold it for profit on underground marketplaces. This group has aggressively targeted and compromised point of sale (PoS) systems in the hospitality and retail sectors.[1][2]

Group Enterprise

G1041: Sea Turtle

Sea Turtle is a Türkiye-linked threat actor active since at least 2017 performing espionage and service provider compromise operations against victims in Asia, Europe, and North America. Sea Turtle is notable for targeting registrars managing ccTLDs and complex DNS-based intrusions where the threat actor compromised DNS providers to hijack DNS resolution for ultimate victims, enabling Sea Turtle to spoof log in portals and other applications for credential collection.[1][2][3][4]

Group Enterprise

G0010: Turla

Turla is a cyber espionage threat group that has been attributed to Russia's Federal Security Service (FSB). They have compromised victims in over 50 countries since at least 2004, spanning a range of industries including government, embassies, military, education, research and pharmaceutical companies. Turla is known for conducting watering hole and spearphishing campaigns, and leveraging in-house tools and malware, such as Uroburos.[1][2][3][4][5]

Malware Enterprise

S9010: GlassWorm

GlassWorm is a worm that propagated through supply chain attacks by compromising repository credentials from victim environments and having malicious payloads added to those compromised accounts for distribution to victims across the various development ecosystems.[1][2][3] GlassWorm has numerous variants, including Rust binaries, encrypted JavaScript and a variant leveraging invisible Unicode characters that made reverse engineering difficult.[4][1][5] GlassWorm has employed a unique command and control (C2) methodology using Solana blockchain.[6][1] GlassWorm was first reported in October 2025.[6][1][3]

macOSWindows
Malware Enterprise

S1146: MgBot

MgBot is a modular malware framework exclusively associated with Daggerfly operations since at least 2012. MgBot was developed in C++ and features a module design with multiple available plugins that have been under active development through 2024.[1][2][3]

Windows
Campaign Enterprise

C0049: Leviathan Australian Intrusions

Leviathan Australian Intrusions consisted of at least two long-term intrusions against victims in Australia by Leviathan, relying on similar tradecraft such as external service exploitation followed by extensive credential capture and re-use to enable privilege escalation and lateral movement. Leviathan Australian Intrusions were focused on exfiltrating sensitive data including valid credentials for the victim organizations.[1]

Campaign Enterprise

C0062: Anthropic AI-orchestrated Campaign

The Anthropic AI-orchestrated Campaign was conducted in September 2025 by a likely China nexus espionage actor identified as GTG-1002. The Anthropic AI-orchestrated Campaign was a highly coordinated operation that manipulated Claude Code to perform reconnaissance, vulnerability discovery, exploitation, lateral movement, credential harvesting, data analysis, and exfiltration operations at approximately 30 entities in the technology, financial, chemical, and government sectors. During the Anthropic AI-orchestrated Campaign, human operators used Claude Code agents and Model Context Protocol (MCP) tools to automate cyber operations. Operators broke attacks into discrete tasks, used crafted prompts, and established personas to bypass AI guardrails, enabling the agents to execute the operations with minimal human involvement.[1][2]

Relationship explorer

All related ATT&CK context

Mitigations

Mitigation direction

Change history

Object version and sync metadata

The fields below describe the current mirrored snapshot. When Glexia retains multiple ATT&CK source imports, you can open the table to compare the same object across releases (hashes and MITRE timestamps). For MITRE’s own release notes and roadmap, see ATT&CK resources — Updates .

ATT&CK release
19.1
Object version
1.0
Created
Modified
Raw hash
a4f11e9c2d8c7939...
Imported snapshots across ATT&CK releases (1)
Release Bundle imported Object version Modified Status Raw hash
19.1 1.0 Current bundle a4f11e9c2d8c…
Raw source

Mirrored ATT&CK source object

The raw object is retained through the mirrored ATT&CK source bundle and object hash. The raw endpoint returns the exact object from the mirrored bundle when available.

Source references

External references and citations

MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.

  1. [1]
    Google Cloud Threat Intelligence UNC5537 Snowflake 2024

    Mandiant. (2024, June 10). UNC5537 Targets Snowflake Customer Instances for Data Theft and Extortion. Retrieved May 22, 2025.

    Open source URL
  2. [2]
    mitre-attack T1213.006
    Open source URL
Source and licensing

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