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Update AITG-MOD-07_Testing_for_Goal_Alignment.md

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Matteo Meucci
2025-11-06 14:58:38 +01:00
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Document/content/tests/AITG-MOD-07_Testing_for_Goal_Alignment.md
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@@ -9,9 +9,7 @@ This test evaluates vulnerabilities associated with AI model goal misalignment,
- Evaluate AI model outputs against established guidelines, safety boundaries, and ethical constraints.
- Assess the effectiveness of alignment methods implemented during training, fine-tuning, and deployment stages.
#### Test/Payloads
### Test/Payloads
### How to Test/Payloads
| Payload | Response Indicating Vulnerability |
|---|---|
@@ -32,7 +30,7 @@ This test evaluates vulnerabilities associated with AI model goal misalignment,
- **Continuous Red Teaming and Auditing**: Employ a dedicated red team to constantly create new and creative ways to break the model's alignment. Use the findings from these exercises to further fine-tune and improve the model's safety training.
- **Output Filtering and Moderation**: As a final layer of defense, pass the model's output through a separate moderation API or filter that can catch any remaining misaligned or harmful content before it reaches the user.
### Suggested Tools for this Specific Test
### Suggested Tools
- **Promptfoo**: An open-source tool for evaluating LLM output quality and testing for regressions. Excellent for creating test suites to check for goal alignment against a set of predefined criteria.
- Tool Link: [Promptfoo on GitHub](https://github.com/promptfoo/promptfoo)
- **Garak**: An open-source framework for LLM vulnerability scanning, including probes specifically designed to test for goal misalignment and ethical boundary violations.