AN1161: Analytic 1161
Command-line tools (e.g., curl, rsync, wget, or custom Python scripts) used to scrape documentation systems or internal REST APIs. Unusual access patterns to knowledge base folders or shared team drives.
Analyst context for executives and security teams
This analytic points to a practical insider or post-compromise risk pattern: Linux command-line tools being used to pull large or unusual amounts of data from documentation systems, internal REST APIs, knowledge base folders, or shared team drives. For leaders, the value is not the specific tool name; it is whether the organization can tell normal automation and collaboration apart from suspicious bulk access to internal knowledge assets.
Executive priority
Prioritize this where internal documentation, APIs, and shared drives contain sensitive operational, engineering, customer, or incident response information. The business question is whether teams have enough logging and access governance to investigate unusual scraping quickly, preserve evidence, and show auditors that sensitive knowledge repositories are monitored and access-controlled. This is especially relevant for SOC readiness, identity/access review, cloud or SaaS repository governance where applicable, and incident response scoping.
Technical view
For Linux environments, validate visibility into command-line network utilities and scripts such as curl, rsync, wget, and Python-based access to internal documentation systems or REST APIs. Because no official detection logic is provided, teams should baseline expected administrative jobs, CI/CD tasks, backup processes, and approved integrations, then look for unusual volume, frequency, paths, destinations, authentication context, or access to knowledge base folders and shared team drives. Investigation should connect endpoint process evidence with application/API access logs and identity context.
Likely telemetry
- Linux process execution telemetry with command-line arguments
- Shell history or equivalent endpoint activity records where available
- Network connection logs from Linux hosts
- Proxy, web gateway, or egress logs showing requests to documentation systems or internal REST APIs
- Application access logs for knowledge bases, documentation platforms, shared drives, and internal APIs
Detection direction
- Confirm that Linux endpoint telemetry captures command-line tools and script interpreters sufficiently to distinguish approved automation from unusual scraping behavior.
- Baseline normal access patterns for documentation systems, shared drives, and internal REST APIs by user, service account, host, path, request rate, and data volume.
- Tune for anomalies such as new hosts accessing sensitive folders, unusual request bursts, broad enumeration of folders or API endpoints, uncommon user-agent strings, or command-line tools used where browser or application access is normally expected.
- Reduce false positives by documenting legitimate backup, synchronization, search indexing, CI/CD, and administrative scripts that may use curl, rsync, wget, or Python.
- Correlate endpoint process activity with application/API logs; either source alone may be insufficient to determine whether access was authorized or unusual.
Mitigation priorities
- Inventory documentation systems, shared team drives, and internal REST APIs that contain sensitive business or operational information.
- Enforce least-privilege access and review user and service-account permissions for knowledge repositories and internal APIs.
- Require identifiable, non-shared automation identities for approved scraping, backup, synchronization, or indexing jobs.
- Set logging retention and investigation procedures for endpoint process activity, API access, and repository access before an incident occurs.
- Apply rate limiting, scoped tokens, and access reviews where supported by the relevant documentation or API platform.
Analyst notes and limits
The object is an ATT&CK detection analytic for Linux describing command-line scraping of documentation systems, internal REST APIs, knowledge base folders, or shared team drives. There are no supplied tactics, relationships, aliases, labels, or official detection logic, so this take focuses on defensive validation and telemetry planning rather than a specific rule.
The supplied ATT&CK fields do not identify a tactic, technique relationship, adversary, campaign, impact, or tested detection method. Local baselines, repository architecture, identity model, and logging coverage are required to determine materiality and build reliable detections.
Analytic 1161
Command-line tools (e.g., curl, rsync, wget, or custom Python scripts) used to scrape documentation systems or internal REST APIs. Unusual access patterns to knowledge base folders or shared team drives.
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.
All related ATT&CK context
No relationships are available in the current normalized data for this object.
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 .
Imported snapshots across ATT&CK releases (1)
| Release | Bundle imported | Object version | Modified | Status | Raw hash |
|---|---|---|---|---|---|
| 19.1 | 1.0 | Current bundle | 6b81c10d1123… |
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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mitre-attack AN1161Open source URL
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