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

• Paper
• Code and frozen paper bundle
• Paper results
• Zenodo archive and DOI

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.