nature-figure
Pass
Audited by Gen Agent Trust Hub on Oct 7, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONEXTERNAL_DOWNLOADSCOMMAND_EXECUTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill exhibits an attack surface for indirect prompt injection through several data ingestion points combined with powerful capabilities.
- Ingestion points: Multiple scripts ingest untrusted external data:
scripts/figure_source_data.pyreads user-provided CSV and TSV files;scripts/validate_figure.pyparses Python and R plotting scripts;scripts/audit_pdf_text.pyandscripts/audit_figure_collisions.pyparse PDF geometry and text operators. Additionally,scripts/generate_openrouter_schematic.pyreads text from an abstract file (--abstract-file) and image bytes from reference paths (--reference-image). - Boundary markers: The skill does not employ explicit delimiters or boundary markers to isolate untrusted content from the AI agent's instructions, nor does it provide specific instructions for the agent to disregard commands embedded within the ingested data.
- Capability inventory: The skill has file system access (read/write) and network capabilities. Specifically,
scripts/generate_openrouter_schematic.pymakes network POST requests to the OpenRouter API (https://openrouter.ai/api/v1/images). A sophisticated indirect prompt injection attack could potentially trick the agent into exfiltrating sensitive local files (e.g., credentials or private research data) by passing them as arguments to the schematic generation tool. - Sanitization: No evidence of content sanitization or instruction filtering was found for data ingested from external files before processing or transmission.
- [EXTERNAL_DOWNLOADS]: The script
scripts/generate_openrouter_schematic.pyperforms network POST operations tohttps://openrouter.ai/api/v1/images. This is a legitimate feature for generating AI-assisted manuscript schematics through the OpenRouter service, a well-known AI platform. The implementation uses the standardurllib.requestlibrary. - [COMMAND_EXECUTION]: The skill relies on the execution of several internal Python scripts for auditing and validation, and it generates and runs Python or R plotting code as its primary function. These are expected behaviors for a figure production tool.
Audit Metadata