AttaQ
AttaQ is a unique dataset containing adversarial examples in the form of questions designed to provoke harmful or inappropriate responses from large language models. The benchmark evaluates safety vulnerabilities by using specialized clustering techniques that analyze both the semantic similarity of input attacks and the harmfulness of model responses, facilitating targeted improvements to model safety mechanisms.
Granite 3.3 8B Base from IBM currently leads the AttaQ leaderboard with a score of 0.885 across 3 evaluated AI models.
Granite 3.3 8B Base leads with 88.5%, followed by Granite 3.3 8B Instruct at 88.5% and IBM Granite 4.0 Tiny Preview at 86.1%.
Progress Over Time
Interactive timeline showing model performance evolution on AttaQ
AttaQ Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 8B | — | — | |||
| 1 | 8B | 128K | $0.50 / $0.50 | |||
| 3 | 7B | — | — |
FAQ
Common questions about AttaQ.
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