Get the app
evaluate 0 0 0

abliterationai/authorized-cyber-policy-eval

published by @abliterationai

Alignment dataset tracked in the /datasets sub-catalog.

Lifetime downloads
120
Last 30 days
23
Likes
-
Size
24 KB
Created on HF
2026-05-06
Age
5 months ago

Description

Authorized Cyber Policy Eval

This dataset tests a core cybersecurity use case: authorized defensive work

should be allowed, while abuse such as evasion, persistence, credential theft,

and real-target exfiltration should be refused, rewritten, escalated, or logged

in shadow mode.

Rows are intentionally framed around governance outcomes rather than exploit

details. The dataset is suitable for evaluating whether a gateway can preserve

legitimate security research while enforcing… See the full description on the dataset page: https://huggingface.co/datasets/abliterationai/authorized-cyber-policy-eval.

Tags

task_categories:text-classificationtask_categories:text-generationlanguage:enlicense:apache-2.0size_categories:n<1Kformat:parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:uscybersecurityred-teamgoverned-aipolicy-gatewayaudit-logging

README current version from Hugging Face

license: apache-2.0
language:

  • en
    task_categories:
  • text-classification
  • text-generation
    tags:
  • cybersecurity
  • red-team
  • governed-ai
  • policy-gateway
  • audit-logging
    pretty_name: Authorized Cyber Policy Eval
    size_categories:
  • n<1K

Authorized Cyber Policy Eval

This dataset tests a core cybersecurity use case: authorized defensive work
should be allowed, while abuse such as evasion, persistence, credential theft,
and real-target exfiltration should be refused, rewritten, escalated, or logged
in shadow mode.

Rows are intentionally framed around governance outcomes rather than exploit
details. The dataset is suitable for evaluating whether a gateway can preserve
legitimate security research while enforcing enterprise policy boundaries.

Intended Use

  • Demonstrate governed red-team and security-testing workflows.
  • Evaluate policy decisions across /policy/chat/completions,
    /policy/messages, and /policy/responses.
  • Produce baseline evidence for teams assessing OWASP LLM Top 10, NIST AI RMF,
    and MITRE ATLAS-aligned workflows.

Abliteration Baseline

python3 evals/governed-ai/scripts/run_policy_gateway_eval.py \
  --dataset evals/governed-ai/datasets/authorized_cyber_policy_eval.jsonl

For model-backed allow/rewrite cases, use at least 5000 output tokens. Shorter
caps can make Qwen spend the budget in reasoning and return no final content.

Safety Notes

The prompts are eval prompts for routing and governance. They are not a
playbook for offensive security operations, and expected outputs should not
include exploit code or operational abuse instructions.

README history 2 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-05-06Publish governed AI eval datasetc6f63111.6 KB
    Loading...
  2. 2026-05-06initial commit18f4ce00 B
    Loading...

Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Related datasets same stage · evaluate

google/IFEval
1.7M dl 182 likes 210 KB
Dataset Card for IFEval Dataset Summary This dataset contains the prompts used in the Instruction-Following Ev...
JailbreakBench/JBB-Behaviors
418.2K dl 143 likes 992 KB
An Open Robustness Benchmark for Jailbreaking Language Models NeurIPS 2024 Datasets and Benchmarks Track P...
Beijing-AISI/panda-bench
195.6K dl 3.1 GB
PandaBench PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak atta...
allenai/wildguardmix
137.3K dl 108 likes 53.4 MB
Dataset Card for WildGuardMix Disclaimer: The data includes examples that might be disturbing, harmful or upsetting....
walledai/HarmBench
135.7K dl 59 likes 109 KB
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal Paper: HarmBench: A Standardized Ev...
walledai/XSTest
108.3K dl 30 likes 22 KB
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models Paper: XSTest: A Test Suite for Ide...
bench-llm/or-bench
77.2K dl 22 likes 32.1 MB
OR-Bench: An Over-Refusal Benchmark for Large Language Models Please see our demo at HuggingFace Spaces. Overall P...
argilla/magpie-ultra-v1.0
65.5K dl 53 likes 20.7 GB
Dataset Card for magpie-ultra-v1.0 This dataset has been created with distilabel. Dataset Summary magpie...

Read further

Community best practices (evaluation)
4 rules on judges, benchmarks, and reporting.
Reference-set drift over time
Why the same benchmark means different things across model generations.
How we label the method for each model
The 12 classification rules that assign M1-M8 to every model.
Non-English abliteration communities
The Korean scepticism about one-click tools and non-English benchmark blindspots.

Metadata

License
apache-2.0

Repository

FILE .gitattributes FILE data.jsonl FILE data.parquet FILE README.md

File contents live on Hugging Face. Click a file or folder to open it there.

Download the dataset

0

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("abliterationai/authorized-cyber-policy-eval")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="abliterationai/authorized-cyber-policy-eval", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/abliterationai/authorized-cyber-policy-eval
Browse repository on Hugging Face ->
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Get the app Browse the catalog
Open in Abliteration