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LLM Stats
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.
Evaluation context and evidence
- Source
- LLM Stats
- Publisher
- Not recorded
- Benchmark release
- Not recorded
- Collected
- 2026-08-17
- Modality
- text
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 3
Shown in source observation order, not ranked. Dates retain the precision provided by the source.
| Model | Value | Unit | Protocol | Reported date and basis | Source |
|---|---|---|---|---|---|
| Granite 3.3 8B Base | 0.885 | Not recorded | Not recorded | 2025-04-16 · model_announcement | LLM Stats |
| Granite 3.3 8B Instruct | 0.885 | Not recorded | Not recorded | 2025-04-16 · model_announcement | LLM Stats |
| IBM Granite 4.0 Tiny Preview | 0.861 | Not recorded | Not recorded | 2025-05-02 · model_announcement | LLM Stats |