Datasets:

MultiverseComputingCAI
/

safety-for-whom

---
license: cc-by-4.0
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- safety
- llm-safety
- alignment
- refusal
- red-teaming
- narrow-boundary-safety
- politics
- religion
pretty_name: "Safety for Whom — Narrow-Boundary Safety Prompts"
size_categories:
- 100K<n<1M
---
# Safety for Whom? — Narrow-Boundary Safety Prompts (Politics & Religion)

This dataset accompanies the paper **["Safety for Whom? Boundary-Aware Self-Distillation for
Controlled LLM Safety Refusal"](https://huggingface.co/papers/2609.04482)**
(arXiv:[2609.04482](https://arxiv.org/abs/2609.04482), López-Ávila et al., 2026). See also the
project write-up: [*"Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole
Topic"*](https://huggingface.co/blog/MultiverseComputingCAI/safety-for-whom).

The paper studies **narrow-boundary safety**: instead of asking "is this topic harmful?", it asks
"which subset of a topic is incompatible with a given deployment policy?" A civics tutor and a
public-sector assistant may share a base model but need different boundaries on the same topic of
politics, refusing targeted manipulation while still answering factual questions. This release
contains the **prompts** used to study that question, with no model-generated responses and
nothing tied to a particular target model, so that anyone can build their own training or
evaluation pipeline on top of the same prompt pool.

## Considerate use

Many of these prompts describe or request harmful political or religious manipulation, propaganda,
or persuasion by design, since that is what a boundary-safety benchmark needs to measure. Please
use this data for safety alignment, refusal-boundary evaluation, and related research, not to build
persuasion or manipulation capabilities. The political and religious framings named in the prompts
are there for construction purposes and do not represent any endorsement or judgement by the
authors.

## What this is (and isn't)

This is a **prompts-only** release. In the paper, these prompts are paired with model-generated,
WildGuard-filtered responses (refusal traces for harmful prompts, compliance traces for benign
ones); that response layer is specific to the target model and training approach (the paper's main
results use Qwen3-8B) and is not included here. What's included is the model-independent layer:
the prompts themselves, plus the metadata used for the paper's stratified evaluation (persona,
topic, and length-bucket labels).

Two topical domains are covered:

- **Politics** — the paper's main experimental domain. Political persuasion is a useful testbed
  because manipulative persuasion is harmful while factual political information stays legitimate,
  which is exactly the case where topic-level refusal is too blunt.
- **Religion** — built with the same generation pipeline (same four-stage hierarchical prompting,
  same pairwise-boundary construction). Religion has its own results in the paper's appendix,
  showing the same boundary-safety pattern holds on a second topic.

## Files

Each domain has three parts: **main** (single harmful/target prompts), **pairwise** (boundary
pairs), and **test** (held-out evaluation sets).

### Politics

| Role | File | Rows | Notes |
|---|---|---:|---|
| Main | `Politics_new_train.json` | 40,480 | Harmful political prompts from the topic→subtopic→point→persona pipeline. |
| Pairwise | `Politics_new_train_pairwise.json` | 40,453 | Boundary pairs: `prompt_a` (comply-expected) / `prompt_b` (refuse-expected) sharing a topic anchor. |
| Test — in-distribution | `politics_test_in.json` | 6,000 | Held-out prompts from the same generation process as Main. |
| Test — out-of-distribution | `politics_test_out.json` | 1,540 | A separate construction run from an earlier version of the pipeline, before prompt-length control was added. Not a paraphrase of the in-distribution set; its subtopics differ. |
| Test — boundary, refuse-side | `pol_pr_ood_test.json` | 1,539 | Held-out `prompt_b` boundary prompts, sourced from `politics_test_out`. |
| Test — boundary, comply-side | `pol_pb_ood_test.json` | 1,539 | Held-out `prompt_a` boundary prompts, from the same pairs. |

The 1,539-per-side boundary count matches the paper's reported figure for its held-out
harmful-benign pairs, so these two files are exactly the paper's headline boundary-evaluation set.
The paper's training data also includes a labeled, coverage-repaired version of the harmful
prompts (40,293 rows after retry-based repair); that labeled training file is a downstream,
approach-specific artifact and isn't part of this prompts-only release.

### Religion

| Role | File | Rows | Notes |
|---|---|---:|---|
| Main | `Religion_new_train.json` | 40,675 | Same pipeline, religion-specific subtopic and persona guidance. |
| Pairwise | `Religion_new_train_pairwise.json` | 40,643 | Same boundary-pair construction as politics. |
| Test — in-distribution | `religion_test_in.json` | 195 | |
| Test — out-of-distribution | `religion_test_out.json` | 152 | |
| Test — boundary, refuse-side | `rel_pr_ood_test.json` | 152 | Sourced from `religion_test_out`. |
| Test — boundary, comply-side | `rel_pb_ood_test.json` | 152 | From the same pairs. |

Religion's held-out evaluation sets are smaller than politics' (152–195 rows versus 1,539–6,000).
This reflects the smaller scope religion was originally built at; the paper's religion results are
computed on these same held-out sets.

## Schema

The main-pool files differ slightly between domains:

- `Politics_new_train.json`: `_id, length_range, persona, point, prompt, subject, subtopic`
- `Religion_new_train.json`: `_id, instruction, length_range, persona, persona_bucket, point,
  prompt, prompt_label, subject, subtopic` — religion's pipeline additionally pre-computes
  `persona_bucket` and `prompt_label`, and carries the prompt text under both `instruction` and
  `prompt`.

Pairwise files (`*_pairwise.json`), both domains: `dataset, generation_idx, metadata, pair_id,
prompt_a, prompt_b, task`, with `metadata` a nested object carrying `subject`, `subtopic`, and
`point`.

Test files:

- `politics_test_in.json`, `politics_test_out.json`: `_id, instruction, length_range, persona,
  persona_bucket, point, prompt_label, subject, subtopic, prompt` (`prompt` duplicates
  `instruction`).
- `religion_test_in.json`, `religion_test_out.json`: same fields as above.
- The four `*_ood_test.json` files carry the same fields as the in/out test files.

`length_range` takes one of `2-4 words`, `4-8 words`, `8-15 words`, or `more than 15 words`,
sampled per prompt at generation time. The two longer buckets are intentionally over-weighted to
counteract the short-prompt bias of unconstrained LLM generation (see the paper for the
rationale).

`prompt_label` takes one of `vanilla_harmful`, `adversarial_harmful`, `vanilla_benign`, or
`adversarial_benign`, derived from the persona bucket that generated the prompt (moderate personas
produce `vanilla_*`, radical personas produce `adversarial_*`). Every religion `prompt_label` is
`adversarial_harmful`, because religion's persona pool has no moderate cluster; all five of its
persona clusters are adversarial by construction. Politics mixes moderate and radical personas.

## Politics train/test overlap audit

We audited the politics splits using the same normalised `SequenceMatcher` criterion and 0.86
threshold used by the generation pipeline. The audit compares unique subtopics exhaustively and
then inspects prompts under every matched subtopic pair. We report both matched-pair density and
test-side coverage: pair density alone can look artificially small because its denominator is the
full Cartesian product.

| Comparison | Matched subtopic pairs | Pair density | Test-side subtopic coverage |
|---|---:|---:|---:|
| `politics_test_in` vs. `politics_test_out` | 1 fuzzy; 0 exact | 1/9,800 (0.0102%) | 1/100 (1.00%) and 1/98 (1.02%) |
| each politics training artifact vs. pooled tests | 8, including 3 exact | 8/86,922 (0.0092%) | 8/198 (4.04%) |

The eight train/test candidates involve six `politics_test_in` subtopics and two
`politics_test_out` subtopics. The response-filtered training artifact and the prompts-only source
artifact produce the same eight candidate subtopic identities, so these are not counted as 16
independent overlaps. We then exhaustively compared every prompt pair inside all flagged subtopic
pairs: 157,803 prompt pairs in total, of which zero reached the 0.86 threshold. The highest score
was 0.800, and zero of the 7,540 held-out prompts had a confirmed lexical training match under this
criterion. A separate three-pass semantic review conservatively identified three candidate
subtopic groups with related requested information. Thus, the audit finds no lexical prompt
near-duplicates while acknowledging limited reuse of political events, policy themes, and intent.

The pure prompt outputs from this audit can be found in the subfolder `politics_audit`.

## Known limitations

- **Longer prompts near the boundary.** The boundary test sets skew heavily toward the
  `more than 15 words` bucket (politics: 95.3% on the refuse-side, 71.0% on the comply-side;
  religion: 91.4% and 49.3%), and `politics_test_out` is 98.6% in that bucket. This follows from
  how boundary pairs are built: they need enough shared context to encode a comply-versus-refuse
  distinction, and the out-of-distribution sets come from an earlier, pre-length-control run of the
  pipeline that tends to produce longer prompts on its own. This is expected behaviour, not a
  construction error.
- **Persona labels on the four `*_ood_test.json` files mix personas from both domains.** The
  `persona` and `persona_bucket` columns on `pol_pr_ood_test`, `pol_pb_ood_test`, `rel_pr_ood_test`,
  and `rel_pb_ood_test` draw from the full cross-domain persona pool rather than the correct
  per-domain pool. These four files are released exactly as used to compute the paper's results, so
  the dataset matches the paper's numbers precisely. This doesn't affect those numbers: the paper's
  reported PR-OOD/PB-OOD metrics are aggregate refusal rates over all rows in each file, and don't
  use `persona` or `persona_bucket`. If you run a per-persona-bucket breakdown on these four files
  specifically, treat that column as unreliable.
- **`politics_test_out.json` only has two of politics' four persona buckets** (`RADICAL_RIGHT` and
  `RADICAL_LEFT`, no `MODERATE`, no `RADICAL_OTHERS`) and no `vanilla_harmful` rows. This is an
  asymmetry worth knowing about if you compare it directly against `politics_test_in`.
- **The overlap audit is hierarchical and lexical.** All subtopic combinations are screened first;
  the full prompt cross-product is then evaluated only inside subtopic pairs meeting the 0.86
  threshold. This checks 157,803 of 619,082,740 theoretical prompt pairs (0.025490%). The audit
  establishes zero lexical near-duplicates under the pipeline criterion, not complete semantic
  independence. Shared subtopics should still be treated as potential semantic overlap when their
  prompt wording differs.

## Citation

If you use this dataset, please cite the paper:

```bibtex
@article{lopezavila2026safety,
  title   = {Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal},
  author  = {L{\'o}pez-{\'A}vila, Alejo and Garc{\'i}a-Ferrero, Iker and Garcia, Jezabel and Tiene, Antonio and Or{\'u}s, Rom{\'a}n},
  journal = {arXiv preprint arXiv:2609.04482},
  year    = {2026},
  url     = {https://arxiv.org/abs/2609.04482}
}
```

## Contact

Multiverse Computing — [multiversecomputing.com](https://multiversecomputing.com)

	---
	license: cc-by-4.0
	task_categories:
	- text-classification
	- text-generation
	language:
	- en
	tags:
	- safety
	- llm-safety
	- alignment
	- refusal
	- red-teaming
	- narrow-boundary-safety
	- politics
	- religion
	pretty_name: "Safety for Whom — Narrow-Boundary Safety Prompts"
	size_categories:
	- 100K<n<1M
	---

	# Safety for Whom? — Narrow-Boundary Safety Prompts (Politics & Religion)

	This dataset accompanies the paper **["Safety for Whom? Boundary-Aware Self-Distillation for
	Controlled LLM Safety Refusal"](https://huggingface.co/papers/2609.04482)**
	(arXiv:[2609.04482](https://arxiv.org/abs/2609.04482), López-Ávila et al., 2026). See also the
	project write-up: [*"Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole
	Topic"*](https://huggingface.co/blog/MultiverseComputingCAI/safety-for-whom).

	The paper studies narrow-boundary safety: instead of asking "is this topic harmful?", it asks
	"which subset of a topic is incompatible with a given deployment policy?" A civics tutor and a
	public-sector assistant may share a base model but need different boundaries on the same topic of
	politics, refusing targeted manipulation while still answering factual questions. This release
	contains the prompts used to study that question, with no model-generated responses and
	nothing tied to a particular target model, so that anyone can build their own training or
	evaluation pipeline on top of the same prompt pool.

	## Considerate use

	Many of these prompts describe or request harmful political or religious manipulation, propaganda,
	or persuasion by design, since that is what a boundary-safety benchmark needs to measure. Please
	use this data for safety alignment, refusal-boundary evaluation, and related research, not to build
	persuasion or manipulation capabilities. The political and religious framings named in the prompts
	are there for construction purposes and do not represent any endorsement or judgement by the
	authors.

	## What this is (and isn't)

	This is a prompts-only release. In the paper, these prompts are paired with model-generated,
	WildGuard-filtered responses (refusal traces for harmful prompts, compliance traces for benign
	ones); that response layer is specific to the target model and training approach (the paper's main
	results use Qwen3-8B) and is not included here. What's included is the model-independent layer:
	the prompts themselves, plus the metadata used for the paper's stratified evaluation (persona,
	topic, and length-bucket labels).

	Two topical domains are covered:

	- Politics — the paper's main experimental domain. Political persuasion is a useful testbed
	because manipulative persuasion is harmful while factual political information stays legitimate,
	which is exactly the case where topic-level refusal is too blunt.
	- Religion — built with the same generation pipeline (same four-stage hierarchical prompting,
	same pairwise-boundary construction). Religion has its own results in the paper's appendix,
	showing the same boundary-safety pattern holds on a second topic.

	## Files

	Each domain has three parts: main (single harmful/target prompts), pairwise (boundary
	pairs), and test (held-out evaluation sets).

	### Politics

	\| Role \| File \| Rows \| Notes \|
	\|---\|---\|---:\|---\|
	\| Main \| `Politics_new_train.json` \| 40,480 \| Harmful political prompts from the topic→subtopic→point→persona pipeline. \|
	\| Pairwise \| `Politics_new_train_pairwise.json` \| 40,453 \| Boundary pairs: `prompt_a` (comply-expected) / `prompt_b` (refuse-expected) sharing a topic anchor. \|
	\| Test — in-distribution \| `politics_test_in.json` \| 6,000 \| Held-out prompts from the same generation process as Main. \|
	\| Test — out-of-distribution \| `politics_test_out.json` \| 1,540 \| A separate construction run from an earlier version of the pipeline, before prompt-length control was added. Not a paraphrase of the in-distribution set; its subtopics differ. \|
	\| Test — boundary, refuse-side \| `pol_pr_ood_test.json` \| 1,539 \| Held-out `prompt_b` boundary prompts, sourced from `politics_test_out`. \|
	\| Test — boundary, comply-side \| `pol_pb_ood_test.json` \| 1,539 \| Held-out `prompt_a` boundary prompts, from the same pairs. \|

	The 1,539-per-side boundary count matches the paper's reported figure for its held-out
	harmful-benign pairs, so these two files are exactly the paper's headline boundary-evaluation set.
	The paper's training data also includes a labeled, coverage-repaired version of the harmful
	prompts (40,293 rows after retry-based repair); that labeled training file is a downstream,
	approach-specific artifact and isn't part of this prompts-only release.

	### Religion

	\| Role \| File \| Rows \| Notes \|
	\|---\|---\|---:\|---\|
	\| Main \| `Religion_new_train.json` \| 40,675 \| Same pipeline, religion-specific subtopic and persona guidance. \|
	\| Pairwise \| `Religion_new_train_pairwise.json` \| 40,643 \| Same boundary-pair construction as politics. \|
	\| Test — in-distribution \| `religion_test_in.json` \| 195 \| \|
	\| Test — out-of-distribution \| `religion_test_out.json` \| 152 \| \|
	\| Test — boundary, refuse-side \| `rel_pr_ood_test.json` \| 152 \| Sourced from `religion_test_out`. \|
	\| Test — boundary, comply-side \| `rel_pb_ood_test.json` \| 152 \| From the same pairs. \|

	Religion's held-out evaluation sets are smaller than politics' (152–195 rows versus 1,539–6,000).
	This reflects the smaller scope religion was originally built at; the paper's religion results are
	computed on these same held-out sets.

	## Schema

	The main-pool files differ slightly between domains:

	- `Politics_new_train.json`: `_id, length_range, persona, point, prompt, subject, subtopic`
	- `Religion_new_train.json`: `_id, instruction, length_range, persona, persona_bucket, point,
	prompt, prompt_label, subject, subtopic` — religion's pipeline additionally pre-computes
	`persona_bucket` and `prompt_label`, and carries the prompt text under both `instruction` and
	`prompt`.

	Pairwise files (`*_pairwise.json`), both domains: `dataset, generation_idx, metadata, pair_id,
	prompt_a, prompt_b, task`, with `metadata` a nested object carrying `subject`, `subtopic`, and
	`point`.

	Test files:

	- `politics_test_in.json`, `politics_test_out.json`: `_id, instruction, length_range, persona,
	persona_bucket, point, prompt_label, subject, subtopic, prompt` (`prompt` duplicates
	`instruction`).
	- `religion_test_in.json`, `religion_test_out.json`: same fields as above.
	- The four `*_ood_test.json` files carry the same fields as the in/out test files.

	`length_range` takes one of `2-4 words`, `4-8 words`, `8-15 words`, or `more than 15 words`,
	sampled per prompt at generation time. The two longer buckets are intentionally over-weighted to
	counteract the short-prompt bias of unconstrained LLM generation (see the paper for the
	rationale).

	`prompt_label` takes one of `vanilla_harmful`, `adversarial_harmful`, `vanilla_benign`, or
	`adversarial_benign`, derived from the persona bucket that generated the prompt (moderate personas
	produce `vanilla_`, radical personas produce `adversarial_`). Every religion `prompt_label` is
	`adversarial_harmful`, because religion's persona pool has no moderate cluster; all five of its
	persona clusters are adversarial by construction. Politics mixes moderate and radical personas.

	## Politics train/test overlap audit

	We audited the politics splits using the same normalised `SequenceMatcher` criterion and 0.86
	threshold used by the generation pipeline. The audit compares unique subtopics exhaustively and
	then inspects prompts under every matched subtopic pair. We report both matched-pair density and
	test-side coverage: pair density alone can look artificially small because its denominator is the
	full Cartesian product.

	\| Comparison \| Matched subtopic pairs \| Pair density \| Test-side subtopic coverage \|
	\|---\|---:\|---:\|---:\|
	\| `politics_test_in` vs. `politics_test_out` \| 1 fuzzy; 0 exact \| 1/9,800 (0.0102%) \| 1/100 (1.00%) and 1/98 (1.02%) \|
	\| each politics training artifact vs. pooled tests \| 8, including 3 exact \| 8/86,922 (0.0092%) \| 8/198 (4.04%) \|

	The eight train/test candidates involve six `politics_test_in` subtopics and two
	`politics_test_out` subtopics. The response-filtered training artifact and the prompts-only source
	artifact produce the same eight candidate subtopic identities, so these are not counted as 16
	independent overlaps. We then exhaustively compared every prompt pair inside all flagged subtopic
	pairs: 157,803 prompt pairs in total, of which zero reached the 0.86 threshold. The highest score
	was 0.800, and zero of the 7,540 held-out prompts had a confirmed lexical training match under this
	criterion. A separate three-pass semantic review conservatively identified three candidate
	subtopic groups with related requested information. Thus, the audit finds no lexical prompt
	near-duplicates while acknowledging limited reuse of political events, policy themes, and intent.

	The pure prompt outputs from this audit can be found in the subfolder `politics_audit`.

	## Known limitations

	- Longer prompts near the boundary. The boundary test sets skew heavily toward the
	`more than 15 words` bucket (politics: 95.3% on the refuse-side, 71.0% on the comply-side;
	religion: 91.4% and 49.3%), and `politics_test_out` is 98.6% in that bucket. This follows from
	how boundary pairs are built: they need enough shared context to encode a comply-versus-refuse
	distinction, and the out-of-distribution sets come from an earlier, pre-length-control run of the
	pipeline that tends to produce longer prompts on its own. This is expected behaviour, not a
	construction error.
	- *Persona labels on the four `_ood_test.json` files mix personas from both domains.** The
	`persona` and `persona_bucket` columns on `pol_pr_ood_test`, `pol_pb_ood_test`, `rel_pr_ood_test`,
	and `rel_pb_ood_test` draw from the full cross-domain persona pool rather than the correct
	per-domain pool. These four files are released exactly as used to compute the paper's results, so
	the dataset matches the paper's numbers precisely. This doesn't affect those numbers: the paper's
	reported PR-OOD/PB-OOD metrics are aggregate refusal rates over all rows in each file, and don't
	use `persona` or `persona_bucket`. If you run a per-persona-bucket breakdown on these four files
	specifically, treat that column as unreliable.
	- `politics_test_out.json` only has two of politics' four persona buckets (`RADICAL_RIGHT` and
	`RADICAL_LEFT`, no `MODERATE`, no `RADICAL_OTHERS`) and no `vanilla_harmful` rows. This is an
	asymmetry worth knowing about if you compare it directly against `politics_test_in`.
	- The overlap audit is hierarchical and lexical. All subtopic combinations are screened first;
	the full prompt cross-product is then evaluated only inside subtopic pairs meeting the 0.86
	threshold. This checks 157,803 of 619,082,740 theoretical prompt pairs (0.025490%). The audit
	establishes zero lexical near-duplicates under the pipeline criterion, not complete semantic
	independence. Shared subtopics should still be treated as potential semantic overlap when their
	prompt wording differs.

	## Citation

	If you use this dataset, please cite the paper:

	```bibtex
	@article{lopezavila2026safety,
	title = {Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal},
	author = {L{\'o}pez-{\'A}vila, Alejo and Garc{\'i}a-Ferrero, Iker and Garcia, Jezabel and Tiene, Antonio and Or{\'u}s, Rom{\'a}n},
	journal = {arXiv preprint arXiv:2609.04482},
	year = {2026},
	url = {https://arxiv.org/abs/2609.04482}
	}
	```

	## Contact

	Multiverse Computing — [multiversecomputing.com](https://multiversecomputing.com)