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Datasets:
volkthienpreecha
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crackedpdfs

Tasks:
Text Classification
Feature Extraction
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
llm-security
prompt-injection
pdf-security
benchmark
License:
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metadata 
pretty_name: CrackedPDFs
license: mit
task_categories:
  - text-classification
  - feature-extraction
language:
  - en
tags:
  - llm-security
  - prompt-injection
  - pdf-security
  - benchmark
size_categories:
  - 10K<n<100K

CrackedPDFs

CrackedPDFs is a paired benchmark for detecting prompt injections embedded in PDF structure. The paper release contains 29,322 PDFs derived from 4,983 base documents, organized as 9,774 matched triplets:

  • one benign original;
  • one benign structural confounder; and
  • one injected PDF.

The benign source documents were produced with PDFAutoGen. The paired design measures whether a defense detects malicious intent rather than merely reacting to unusual PDF structure.

Links

Benchmark tasks

  1. Injected-vs-benign classification: classify each PDF as benign or injected.
  2. Paired ranking: rank the injected member above its matched benign original and benign confounder.
  3. Held-out provenance generalization: train and evaluate with base-document provenance separated across splits.
  4. Shortcut auditing: compare performance on random negatives against matched structural confounders.
  5. Attack-family holdout: evaluate generalization across 15 injected attack families.

Dataset composition

Role PDFs
Benign originals 9,774
Benign structural confounders 9,774
Injected attacks 9,774
Total 29,322
Frozen paper evaluation split Rows
Train 23,766
Validation 2,637
Test 2,919

Splits are grouped by base_pdf_id; members derived from the same base document do not cross split boundaries.

Files 

data/
  metadata.jsonl          Complete row-level metadata
  metadata.parquet        Columnar metadata
  labels.parquet          Frozen labels and metadata used by the paper run
  features.parquet        54 frozen structural features for all 29,322 PDFs
  splits.json             Frozen group-aware split assignment
pdfs/
  benign.tar.gz           Benign originals and matched confounders
  injected.tar.gz         Injected PDFs
metrics/
  metrics.json            Complete frozen publication metrics
  hard-setting-summary.csv
checksums.sha256           SHA-256 checksums for every published file

Extract both PDF archives into a common directory. Their internal paths begin with benign/ and injected/, matching the file_path column in the metadata.

Core schema

Field Type Meaning
pdf_id string Unique benchmark PDF identifier
base_pdf_id string Provenance group used to prevent split leakage
sample_id string Matched sample identifier
pair_id / triad_id string Matched comparison group
pdf_role string benign_original, benign_confounder, or injected_attack
file_path string Relative path inside the PDF archives
label integer 0 for benign, 1 for injected
dataset_split string Generation-layer assignment retained from the publication table; use data/splits.json for the paper evaluation split
attack_family string Injected attack family, or none
benign_confounder_family string Matched benign structural transformation
message_type string Prompt-injection objective category
spatial_regime string Placement regime used in the PDF
rendering_regime string Text rendering regime
structural_regime string Content-stream insertion regime
artifact_wrapper boolean Whether marked-content artifact wrapping was used

The metadata contains additional generation, pairing, physical-regime, and audit fields. features.parquet contains pdf_id plus the 53 numeric structural measurements defined in the paper snapshot. data/splits.json is the authoritative paper evaluation assignment and is grouped by base_pdf_id.

Quick reproduction 

git clone https://github.com/volkthienpreecha/crackedpdfs.git
cd crackedpdfs
make reproduce-results

The command downloads and hash-verifies the frozen features, labels, splits, and metrics, then regenerates the paper's hard-setting summary. It does not regenerate 29,322 PDFs.

Limitations and intended use

  • The benchmark is English-language and synthetic; it does not establish performance on every real-world document distribution.
  • The PDFs cover the attack families and rendering regimes documented in the paper, not every possible PDF parser differential.
  • Perfect TF-IDF performance is a shortcut warning, not evidence of universal prompt-injection detection.
  • PromptGuard is included as a domain-mismatched text baseline; the results do not imply that PromptGuard is generally broken.
  • The files contain adversarial instructions intended for security research. Do not feed them to production agents with tools or sensitive data unless the environment is isolated.
  • Do not use the benchmark to claim safety against attacks, models, parsers, or document formats that were not evaluated.

Citation 

@article{thienpreecha2026crackedpdfs,
  title   = {CrackedPDFs: A Controlled Benchmark for Hidden Prompt Injection in PDFs},
  author  = {Thienpreecha, Pukaphol and Subramanian, Karthik},
  journal = {arXiv preprint arXiv:2607.19396},
  year    = {2026},
  url     = {https://arxiv.org/abs/2607.19396}
}

@dataset{thienpreecha2026crackedpdfs_dataset,
  title     = {CrackedPDFs: Paper v1 Dataset and Reproducibility Artifacts},
  author    = {Thienpreecha, Pukaphol and Subramanian, Karthik},
  publisher = {Zenodo},
  year      = {2026},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.21735803},
  url       = {https://doi.org/10.5281/zenodo.21735803}
}

License

The paper-release code and dataset are published under the MIT License. See LICENSE in this dataset repository.