skills/mukul975/anthropic-cybersecurity-skills/implementing-diamond-model-analysis/Gen Agent Trust Hub
implementing-diamond-model-analysis
Pass
Audited by Gen Agent Trust Hub on Sep 25, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill is designed to ingest and process external threat intelligence data, which represents a potential surface for indirect injection if the processed content contains malicious instructions intended for an agent.
- Ingestion points: Untrusted data is loaded from JSON files via the
--dataargument inscripts/agent.pyand the--eventsargument inscripts/process.py. - Boundary markers: The scripts do not implement explicit boundary markers or instructions to treat embedded data as untrusted when generating analysis reports.
- Capability inventory: The scripts are restricted to local file I/O (reading event data and writing analysis reports), JSON parsing, and data correlation. No high-risk capabilities such as network access, subprocess execution, or dynamic code evaluation were found.
- Sanitization: The implementation uses standard JSON parsing but lacks specific sanitization or validation logic for the content of data fields such as adversary names or capability descriptions.
Audit Metadata