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rogue-security/real-world-benign-use-cases

published by @rogue-security

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2026-08-06
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Description

Real-World Benign Use Cases

A curated set of 178 real-world, 100%-benign examples (label == 0 for every row) pulled from

production AI-coding-agent traffic — chat messages, tool output, shell commands, code snippets —

built specifically to stress-test prompt-injection / jailbreak classifiers for false positives.

Every row was independently judged benign with high confidence before inclusion. This is not a

random sample of production traffic: rows were preferentially drawn from… See the full description on the dataset page: https://huggingface.co/datasets/rogue-security/real-world-benign-use-cases.

Tags

task_categories:text-classificationlicense:othersize_categories:n<1Kformat:csvmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:usprompt-injectionjailbreak-detectionfalse-positivesecurityguardrails

README current version from Hugging Face


license: other
task_categories:

  • text-classification
    tags:
  • prompt-injection
  • jailbreak-detection
  • false-positive
  • security
  • guardrails
    pretty_name: Real-World Benign Use Cases (Prompt-Injection FP Benchmark)

Real-World Benign Use Cases

A curated set of 178 real-world, 100%-benign examples (label == 0 for every row) pulled from
production AI-coding-agent traffic — chat messages, tool output, shell commands, code snippets —
built specifically to stress-test prompt-injection / jailbreak classifiers for false positives.

Every row was independently judged benign with high confidence before inclusion. This is not a
random sample of production traffic: rows were preferentially drawn from content that had already
tripped up at least one classifier, then deduplicated with sentence embeddings (cosine similarity)
to remove near-identical examples. Treat the numbers below as relative comparisons between
models on hard cases
, not as any single model's overall real-world FP rate.

Columns

column description
text the raw content
ground_truth ground truth; always 0 (benign) in this release
<model>_pred binary prediction (1 = flagged as injection, 0 = benign)

Models evaluated

column model
wolf-defender_pred patronus-studio/wolf-defender-prompt-injection
deberta-v3-base_pred protectai/deberta-v3-base-prompt-injection-v2
PIGuard_pred leolee99/PIGuard
distilbert_pred fmops/distilbert-prompt-injection
mdeberta-v3-base_pred proventra/mdeberta-v3-base-prompt-injection
promptguard_pred codeintegrity-ai/promptguard
sentinel-v2_pred rogue-security/prompt-injection-jailbreak-sentinel-v2
sentinel-v3_pred internal candidate model (not published)

Results — false-positive rate on benign-only data

Since every row's true label is benign, any 1 prediction is a false positive.

model n FP FP rate
sentinel-v3 178 1 0.56%
sentinel-v2 178 26 14.61%
mdeberta-v3-base 178 26 14.61%
promptguard 178 28 15.73%
deberta-v3-base 178 39 21.91%
PIGuard 178 49 27.53%
wolf-defender 178 63 35.39%
distilbert 178 157 88.20%

README history 10 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. 2026-08-10Update README.md11c0f612.7 KB
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  4. 2026-08-09Update README: small-classifier-finetuned-v1 -> sentinel-v3b6fdd6e3.7 KB
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  5. 2026-08-09Rename small-classifier-finetuned-v1_pred column to sentinel-v3_predbaef5033.8 KB
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  6. 2026-08-06Upload README.md with huggingface_hub01d09973.8 KB
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  7. 2026-08-06Upload real_world_benign_use_cases.csv with huggingface_hub9dff5233.8 KB
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  8. 2026-08-06Upload README.md with huggingface_hubd8c15583.8 KB
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  9. 2026-08-06Upload real_world_benign_use_cases.csv with huggingface_hub8c833cf0 B
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  10. 2026-08-06initial commitecf3c5a0 B
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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.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-08-07[bot] Conversion to Parquetopen1 💬#1
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Repository

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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("rogue-security/real-world-benign-use-cases")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="rogue-security/real-world-benign-use-cases", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/rogue-security/real-world-benign-use-cases
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