Atomic Runbook: Get File Hash Reputation from GTI

ID: RB-ATOM-HASH-001 Version: 1.0 Last_Updated: 2025-05-30 Purpose: To retrieve a comprehensive file analysis report from Google Threat Intelligence (GTI) for a given file hash (MD5, SHA1, or SHA256) to assess its reputation and known characteristics. Parent_Runbook(s)/Protocol(s): rules-bank/indicator_handling_protocols.md#3-atomic-indicator-file-hash Trigger: When a file hash requires an external reputation check as part of an investigation, malware analysis, or alert triage.

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Inputs Required

• file_hash: string - The file hash (MD5, SHA1, or SHA256) to query.
  • Source Example: Alert field target.file.sha256, EDR detection data, output from malware analysis sandbox.
• hash_type (optional): string - Specify "MD5", "SHA1", or "SHA256" if known. GTI can often auto-detect.
  • Source Example: Derived from context or tool output.

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Execution Steps

1. Tool Selection:
   • Primary_Tool_MCP_Server: Google Threat Intelligence MCP
   • Primary_Tool_Name: get_file_report
2. Parameter Mapping:
   • Map file_hash (Input) to MCP Tool parameter hash.
3. Execute Tool: Call the get_file_report tool with the mapped hash.
   • AI Agent Note: Refer to rules-bank/mcp_tool_best_practices.md for specific guidance on GTI tools.
4. Data Transformation/Extraction:
   • The primary output is a JSON object. Key fields for assessment include data.attributes.last_analysis_stats (especially malicious), data.attributes.popular_threat_classification, data.attributes.tags, data.attributes.meaningful_name.

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Outputs Expected

• gti_file_report: JSON - The full JSON report from the GTI get_file_report tool.
• malicious_score: integer - Engines reporting as malicious (from gti_file_report.data.attributes.last_analysis_stats.malicious).
• suspicious_score: integer - Engines reporting as suspicious.
• harmless_score: integer - Engines reporting as harmless.
• threat_classification: string (optional) - From gti_file_report.data.attributes.popular_threat_classification.suggested_threat_label.
• tags: list - From gti_file_report.data.attributes.tags.
• meaningful_name: string (optional) - From gti_file_report.data.attributes.meaningful_name.
• output_status: string - ["Success", "Failure", "NotFound"]
  • "NotFound" if GTI has no information on the hash.
• output_message: string (if Failure) - Details of the issue.

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Decision Logic / Next Steps (If Applicable)

• IF output_status is "Success":
  • IF malicious_score > 5 OR threat_classification indicates a significant threat (e.g., "ransomware", "trojan") THEN
    • Flag File Hash as "High_Risk_GTI".
    • Proceed to rb_hash_lookup_entity_chronicle.md to check for internal sightings.
    • Consider immediate escalation or containment playbook initiation.
  • ELSE IF malicious_score > 0 OR suspicious_score > 0 OR threat_classification is "PUA" (Potentially Unwanted Application) THEN
    • Flag File Hash as "Medium_Risk_GTI_Needs_Correlation".
    • Proceed to rb_hash_lookup_entity_chronicle.md.
  • ELSE (low malicious/suspicious score, no concerning classification)
    • Flag File Hash as "Low_Risk_GTI".
    • Proceed to rb_hash_lookup_entity_chronicle.md for due diligence.
• IF output_status is "NotFound":
  • Log "File hash {file_hash} not found in GTI."
  • Flag File Hash as "Unknown_Reputation_GTI".
  • Proceed to rb_hash_lookup_entity_chronicle.md and consider dynamic analysis if seen internally.
• ELSE (output_status is "Failure"):
  • Log error: output_message.
  • Consider executing rb_hash_get_secops_threat_intel.md as an alternative.
  • IF alternative also fails, escalate: "Failed to retrieve external reputation for file hash {file_hash}."

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AI Agent Execution Notes

• The AI should parse the gti_file_report to extract the specific output parameters.
• If hash_type is not provided, GTI usually auto-detects. If errors occur, prompting for hash type might be a fallback.

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Metrics Collection Points

• Log execution time.
• Log output_status, malicious_score.
• (Reference rules-bank/ai_performance_logging_requirements.md)

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References

• rules-bank/mcp_tool_best_practices.md
• rules-bank/indicator_handling_protocols.md
• rules-bank/ai_performance_logging_requirements.md