advanced-evaluation
Pass
Audited by Gen Agent Trust Hub on Apr 22, 2026
Risk Level: SAFEPROMPT_INJECTION
Full Analysis
- [SAFE]: No hardcoded credentials, sensitive file access patterns, or data exfiltration mechanisms were detected in the skill's instructions or example scripts.\n- [SAFE]: No unauthorized remote code execution or suspicious external downloads were identified. The referenced libraries (scipy, scikit-learn, numpy) are well-known tools for statistical analysis and evaluation.\n- [PROMPT_INJECTION]: The prompt templates provided for scoring and comparison are task-specific and do not contain instructions aimed at bypassing agent safety filters or overriding core behavioral constraints.\n- [PROMPT_INJECTION]: The skill defines evaluation pipelines that ingest and process untrusted data from other models, representing a surface for indirect prompt injection.\n
- Ingestion points: Evaluation templates in 'references/full-guide.md', 'references/implementation-patterns.md', and 'scripts/evaluation_example.py' interpolate external content (e.g., '{response_a}') into prompts.\n
- Boundary markers: The provided templates utilize Markdown headers and labels (e.g., '## Response to Evaluate') to delimit external content from instructions.\n
- Capability inventory: The skill focuses on text analysis and scoring; it does not define or utilize dangerous system capabilities such as subprocess execution, network operations, or file system modifications across its scripts.\n
- Sanitization: The implementation guides do not include explicit sanitization or filtering logic for the untrusted content processed during evaluation.
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