README.md

---
pretty_name: HarmProfile
license: apache-2.0
task_categories:
- text-generation
tags:
- safety
- red-teaming
- synthetic
- harmful-content
size_categories:
- 10K<n<100K
configs:
- config_name: all_57
  default: true
  data_files:
  - split: train
    path: data/all_57/*.parquet
- config_name: high_risk_15
  data_files:
  - split: train
    path: data/high_risk_15/*.parquet
---

# HarmProfile

HarmProfile is a structured safety and red-teaming dataset assembled from approved generation runs.

> **Content warning:** This dataset contains synthetic prompts and responses involving harmful, illegal, abusive, explicit, self-harm, and other high-risk topics. Some categories may be especially sensitive. Use access controls and avoid rendering rows in logs, previews, notebooks, or monitoring systems unless necessary.

## Load the dataset

```python
from datasets import load_dataset

# Load the complete dataset or the high-risk subset.
all_57 = load_dataset(
    "Freshma/HarmProfile", "all_57", split="train", token=True
)
high_risk_15 = load_dataset(
    "Freshma/HarmProfile", "high_risk_15", split="train", token=True
)

# Load one category.
category = "cbrn"
category_dataset = load_dataset(
    "Freshma/HarmProfile",
    data_files=f"data/all_57/{category}.parquet",
    split="train",
    token=True,
)
```

## Dataset structure

Both configurations use the `train` split. Each category is stored in one Zstandard-compressed Parquet file:

```text
data/
├── all_57/          # 57 files, one per category
└── high_risk_15/    # 15 files, one per category
```

`ALL_57` lists every category in the complete `all_57` configuration. `HIGH_RISK_15` lists the 15-category subset packaged as the `high_risk_15` configuration. Each category name maps directly to `data/all_57/<category>.parquet` and can be passed to the loading pattern above.

```python
HIGH_RISK_15 = [
    "cbrn",
    "copyright_reproduction",
    "csam",
    "dehumanization",
    "doxxing",
    "election_interference",
    "financial_scam",
    "harassment",
    "health_medical_misinfo",
    "human_trafficking",
    "jailbreak",
    "malware",
    "medical_advice",
    "suicide",
    "violence_incitement",
]

ALL_57 = [
    "academic_dishonesty",
    "adult_explicit",
    "animal_cruelty",
    "cbrn",
    "conspiracy_narrative",
    "copyright_reproduction",
    "critical_infrastructure",
    "csam",
    "defamation",
    "dehumanization",
    "document_forgery",
    "doxxing",
    "drugs",
    "eating_disorder",
    "election_interference",
    "evasion",
    "exploit_code",
    "explosives",
    "financial_advice",
    "financial_scam",
    "general_factual_misinfo",
    "guardrail_bypass",
    "harassment",
    "health_medical_misinfo",
    "human_trafficking",
    "illegal_firearms",
    "illegal_gambling",
    "impersonation",
    "jailbreak",
    "legal_advice",
    "malware",
    "market_manipulation",
    "medical_advice",
    "mental_health_crisis",
    "minor_grooming",
    "money_laundering",
    "non_consensual_sexual",
    "other_group_discrimination",
    "phishing_social_engineering",
    "pii_leak",
    "political_campaigning",
    "prompt_injection",
    "property_crime",
    "protected_attribute_hate",
    "science_denial",
    "self_injury",
    "spam",
    "spyware_surveillance_tool",
    "state_subversion_separatism",
    "suicide",
    "surveillance_stalking",
    "trade_secret",
    "trademark_misuse",
    "unauthorized_access",
    "violence_graphic",
    "violence_incitement",
    "weapons_trafficking",
]
```

Each record contains 8 columns:

| Group | Columns |
|---|---|
| Identity | `id`, `original_id` |
| Model and taxonomy | `category`, `model` |
| Content | `user_query`, `unsafe_assistant_response`, `safe_assistant_response` |
| Label | `expected_label` |

## Citation

If you use HarmProfile, please cite:

```bibtex
@misc{ma2026harmprofilecharacterizingharmfuldistributions,
  title         = {HarmProfile: Characterizing Harmful Distributions in Frontier LLMs},
  author        = {Zhouyuan Ma and Yutao Wu and Hanxun Huang and Xiang Zheng and Xiao Liu and Yixin Cao and Zuxuan Wu and Xingjun Ma and Yu-Gang Jiang},
  year          = {2026},
  eprint        = {2608.14577},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2608.14577}
}
```