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]
Analyst context for executives and security teams
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.
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]
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.
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.
| Domain | ID | Name | Relationship / procedure |
|---|---|---|---|
| Enterprise | T1213 | Data from Information Repositories | This object subtechnique of Data from Information Repositories. |
Groups, software, and campaigns
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]
G0037: FIN6
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]
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]
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]
S1146: MgBot
S0598: P.A.S. Webshell
P.A.S. Webshell is a publicly available multifunctional PHP webshell in use since at least 2016 that provides remote access and execution on target web servers.[1]
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]
C0040: APT41 DUST
APT41 DUST was conducted by APT41 from 2023 to July 2024 against entities in Europe, Asia, and the Middle East. APT41 DUST targeted sectors such as shipping, logistics, and media for information gathering purposes. APT41 used previously-observed malware such as DUSTPAN as well as newly observed tools such as DUSTTRAP in APT41 DUST.[1]
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]
All related ATT&CK context
Mitigation direction
Object version and sync metadata
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Imported snapshots across ATT&CK releases (1)
| Release | Bundle imported | Object version | Modified | Status | Raw hash |
|---|---|---|---|---|---|
| 19.1 | 1.0 | Current bundle | a4f11e9c2d8c… |
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.
External references and citations
MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.
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[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]
mitre-attack T1213.006Open source URL
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