knowledge-graph-builder
Pass
Audited by Gen Agent Trust Hub on Feb 24, 2026
Risk Level: SAFEPROMPT_INJECTIONEXTERNAL_DOWNLOADS
Full Analysis
- [PROMPT_INJECTION]: The skill describes patterns for ingesting unstructured text to build and query knowledge graphs, which represents a standard indirect prompt injection surface characteristic of retrieval-augmented generation (RAG) systems.
- Ingestion points: Untrusted text is processed in
references/entity-extraction.mdandreferences/ai-integration.mdduring entity extraction and subgraph retrieval. - Boundary markers: The LLM prompts in
references/ai-integration.mduse basic headers (e.g., 'User query:') but do not demonstrate the use of robust delimiters or explicit instructions to ignore embedded commands. - Capability inventory: The architectural patterns provided do not include scripts with dangerous capabilities like arbitrary command execution or unauthorized network access.
- Sanitization: The provided examples focus on architecture and do not include specific input validation or sanitization logic.
- [EXTERNAL_DOWNLOADS]: The documentation references integration with several well-known and trusted technology services for graph databases, vector storage, and machine learning.
- Evidence: References include Neo4j, Amazon Neptune, ArangoDB, TigerGraph, Pinecone, OpenAI, spaCy, and Hugging Face across files such as
references/database-selection.mdandreferences/hybrid-architecture.md.
Audit Metadata