llm-evaluation
Pass
Audited by Gen Agent Trust Hub on Aug 6, 2026
Risk Level: SAFEPROMPT_INJECTIONEXTERNAL_DOWNLOADS
Full Analysis
- [PROMPT_INJECTION]: Indirect prompt injection vulnerability surface detected in the LLM-as-Judge implementations in
reference/evaluation-harness.mdandreference/trajectory-evaluation.md.\n - Ingestion points: Untrusted agent responses and user queries are ingested into the evaluation context via the
llm_judgeandEffectivenessJudgecomponents.\n - Boundary markers: The judging prompts use clear labels for inputs but lack strict boundary delimiters or instructions to ignore embedded commands within the content being evaluated.\n
- Capability inventory: Since the results are intended for use in CI/CD gates and automated benchmarks, successful injection could lead to misleading quality metrics or bypassed safety checks.\n
- Sanitization: The skill does not perform sanitization or escaping on the content being evaluated before it is interpolated into the judge's prompt.\n- [EXTERNAL_DOWNLOADS]: The skill facilitates the download of standard data and models from well-known services.\n
- Downloads text processing data from the NLTK registry using
nltk.downloadinreference/evaluation-metrics.md.\n - Fetches pre-trained machine learning models for NLI and toxicity evaluation from the Hugging Face model hub.
Audit Metadata