phoenix-observability
Pass
Audited by Gen Agent Trust Hub on Sep 9, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTION
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The skill provides templates and methods for LLM-based evaluation (LLM-as-a-judge) that ingest untrusted data, creating a potential surface for indirect prompt injection.\n
- Ingestion points: External data is interpolated into evaluation templates via
input_text,output_text, andreference_textparameters in evaluation functions defined inSKILL.mdandreferences/advanced-usage.md.\n - Boundary markers: The evaluation prompt templates (e.g.,
CUSTOM_EVAL_TEMPLATE) lack explicit delimiters or specific instructions to the evaluator model to ignore embedded commands in the data being assessed.\n - Capability inventory: The skill facilitates network communication with external LLM providers (OpenAI, Anthropic) and observability servers, and includes functionality for data logging and alerting via webhooks.\n
- Sanitization: The provided code implementation examples do not demonstrate sanitization, escaping, or validation of the input data before it is included in evaluation prompts.
Audit Metadata