Exfiltration via Web Service

A subject uses a cloud storage service, such as Dropbox, OneDrive, or Google Drive to exfiltrate data. They will then access that service again on another device to retrieve the data. Examples include (URLs have been sanitized):

• hxxps://www.dropbox[.]com
• hxxps://drive.google[.]com
• hxxps://onedrive.live[.]com
• hxxps://mega[.]nz
• hxxps://www.icloud[.]com/iclouddrive
• hxxps://www.pcloud[.]com

A subject uses a code repository service, such as GitHub, to exfiltrate data. They will then access that service again on another device to retrieve the data. Examples include (URLs have been sanitized):

• hxxps://github[.]com
• hxxps://gitlab[.]com
• hxxps://bitbucket[.]org
• hxxps://sourceforge[.]net
• hxxps://aws.amazon[.]com/codecommit

A subject uses a cloud collaboration platform, such as Slack, Google Docs, Atlassian Confluence, or Microsoft 365 Online, to exfiltrate data. They will then access that service again on another device to retrieve the data. Examples include (URLs have been sanitized):

• hxxps://docs.google[.]com
  hxxps://*.slack[.]com (* represents a wildcard, where a workspace name would be present)
  hxxps://word.cloud[.]microsoft
  hxxps://excel.cloud[.]Microsoft
• hxxps://powerpoint.cloud[.]Microsoft
• hxxps://*.atlassian[.]net/wiki/ (* represents a wildcard, where a workspace name would be present)

The subject transfers sensitive, proprietary, or classified information into an external generative AI platform through text input, file upload, API integration, or embedded application features. This results in uncontrolled data exposure to third-party environments outside organizational governance, potentially violating confidentiality, regulatory, or contractual obligations.

Characteristics

• Involves manual or automated transfer of sensitive data through:
• Web-based AI interfaces (e.g., ChatGPT, Claude, Gemini).
• Upload of files (e.g., PDFs, DOCX, CSVs) for summarization, parsing, or analysis.
• API calls to generative AI services from scripts or third-party SaaS integrations.
• Embedded AI features inside productivity suites (e.g., Copilot in Microsoft 365, Gemini in Google Workspace).
• Subjects may act with or without malicious intent—motivated by efficiency, convenience, curiosity, or deliberate exfiltration.
• Data transmitted may be stored, cached, logged, or used for model retraining, depending on provider-specific terms of service and API configurations.
• Exfiltration through generative AI channels often evades traditional DLP (Data Loss Prevention) patterns due to novel data formats, variable input methods, and encrypted traffic.

Example Scenario

A subject copies sensitive internal financial projections into a public generative AI chatbot to "optimize" executive presentation materials. The AI provider, per its terms of use, retains inputs for service improvement and model fine-tuning. Sensitive data—now stored outside corporate control—becomes vulnerable to exposure through potential data breaches, subpoena, insider misuse at the service provider, or future unintended model outputs.

A subject uploads confidential organization data to a note-taking web service, such as Evernote. The subject can then access the confidential data outside of the organization from another device. Examples include (URLs have been sanitized):

• hxxps://www.evernote[.]com
• hxxps://keep.google[.]com
• hxxps://www.notion[.]so
• hxxps://www.onenote[.]com
• hxxps://notebook.zoho[.]com

A subject uses a text storage service, such as Pastebin, to exfiltrate data. They will then access that service again on another device to retrieve the data. Examples include (URLs have been sanitized):

• hxxps://pastebin[.]com
• hxxps://hastebin[.]com
• hxxps://privatebin[.]net
• hxxps://controlc[.]com
• hxxps://rentry[.]co
• hxxps://dpaste[.]org

A subject may use an existing, legitimate external Web service to exfiltrate data.