---
license: cc-by-4.0
language:
- en
- "no"
- ar
task_categories:
- text-generation
size_categories:
- n<1K
tags:
- synthetic
- safety-evaluation
- prompt-injection
- grounded-qa
- structured-output
- multilingual
- lexsi-2026
pretty_name: PolicyProof AI multilingual fictional-policy safety benchmark
---

# PolicyProof AI — multilingual safety benchmark (research prototype)

**Synthetic research dataset prepared for owner-authorized release. Independent human label review and model improvement are NOT established.** The underlying policies, questions and target answers are invented. There is no customer data or real financial policy.

## What's inside

There are TWO distinct datasets, which must never be conflated:

- **48 evaluated development cases:** 36 supported fictional policy questions, nine missing-evidence abstention cases and three prompt-injection probes. Each of English, Norwegian and Arabic has 16 questions. Their original recorded CohereLabs/tiny-aya-global base-model responses were evaluated through real AuditKIT 1.1.3. CuratorKIT 0.1.22 processed and preserved all 48 original case tags and reference labels.
- **72 NEW unscored challenge cases:** 12 each of normal controls, forged system-role instructions, fake policy sources, encoded instructions, multilingual override instructions and missing-evidence queries. Each language has 24 cases. CuratorKIT structurally curated all 72, but NO MODEL has answered them and NO HUMAN has independently reviewed their semantic labels.

**Never describe these 120 cases as a single 120-response model evaluation**. The second set is unscored and labels are synthetic.

## Provenance

- Original 48-case benchmark SHA-256: d61e59557c62e486afc49e40e5d4daff0c8896429ac2b71d04936b07a49d3a4c
- CuratorKIT 48-case Alpaca export SHA-256: 7a3fbeec5eae88493f9d60f9cc0e2c588ec506b01e58dc5e57edd987508bc2ff
- New 72-case JSONL SHA-256: cdd5cf4e54caa6c79ce1f0cd5bd0f05344f0dc22039a2ad56a3c1c0ee2d32cd1
- New 72-case CuratorKIT export SHA-256: cf8b226ebff5a6a5e53e3132cec9581844d4d6fd92316885c59cbd535ec624df
- Tool versions: CuratorKIT 0.1.22, AuditKIT 1.1.3, CPU Python 3.11
- Full 20-file native evidence archive is available in [the public CI run](https://github.com/betterlifestyle04-commits/PolicyProof-AI/actions/runs/41696571904).
- Source: https://github.com/betterlifestyle04-commits/PolicyProof-AI

## Results on the 48 inspected development cases only

| Metric | Unmodified model, recorded outputs |
| --- | ---: |
| Strict exact full task success | 8 / 48 (16.67%) |
| Complete required 3-key JSON format | 33 / 48 (68.75%) |
| Raw expected source ID on answerable cases | 38 / 39 (97.44%) |
| Strict valid expected source on answerable cases | 20 / 39 (51.28%) |
| Exact answer-text match on answerable cases | 0 / 39 (0%) |
| Correct abstention on unsupported cases | 8 / 9 (88.89%) |
| Substantive unsupported claim | 1 / 9 (11.11%) |

Citation-ID matching alone does not verify factual grounding. Exact text matching does not recognize valid paraphrases. The model revision hash was not recorded in the original saved report.

## Safety, labeling and methodology caveats

- All policy documents and cases are synthetic, small and templated. Four challenge source policies are intentionally repeated across attack variants; these cases are correlated.
- Questions are multilingual but evidence paragraphs remain English, confounding translation and evidence reasoning.
- Only three original prompt-injection cases have model responses, so jailbreak robustness is not established.
- Both sets have template-derived gold answers and lack independent bilingual label adjudication.
- Neither dataset is real-world regulatory guidance, a financial product policy, a production safety test or a claim of model improvement.
- An additional deterministic output guard may change JSON schema and abstention formatting but is **not** SafeTune model steering or model fine-tuning.
- Do not use either evaluation split for model fitting, hyperparameter tuning or any claimed held-out test once published.
- The gated original Tiny Aya weights are NOT licensed or distributed by this dataset.

## Rights and publishing
Publisher-selected license: cc-by-4.0. The publisher is responsible for having sufficient rights to release these original synthetic examples. Original gated Tiny Aya weights and any unverified trained adapter are not included.