CLI tool to detect hidden prompt injection attacks in PDF files
Project description
pdf-injection-scanner
A CLI tool to detect hidden prompt injection attacks in PDF files.
Professors and others may embed invisible instructions in PDFs (white text, tiny fonts, off-page text) designed to manipulate AI assistants. This tool finds them.
Detection capabilities
| Type | Severity | How it works |
|---|---|---|
| White/invisible text | HIGH | Detects characters with white or near-white fill color |
| Tiny text | HIGH | Flags text smaller than 2pt — invisible to the eye |
| Off-page text | HIGH | Finds text positioned outside visible page boundaries |
| Suspicious patterns | MEDIUM | 30+ regex patterns for prompt injection phrases (EN + CN) |
Install
# uv (recommended)
uv tool install pdf-injection-scanner
# pip
pip install pdf-injection-scanner
# Homebrew (macOS)
brew install Andy8647/tap/pdf-injection-scanner
# From source
git clone https://github.com/Andy8647/pdf-injection-scanner.git
cd pdf-injection-scanner
pip install -e .
Usage
# Basic scan
pdf-scan assignment.pdf
# Verbose output with details
pdf-scan assignment.pdf -v
# JSON output for piping
pdf-scan assignment.pdf --json
Example output
Scanning: assignment.pdf
Scanning ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3/3 pages
╭──────────── Result ────────────╮
│ 3 potential injection(s) │
╰────────────────────────────────╯
┏━━━┳━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ # ┃ Page ┃ Severity ┃ Type ┃ Content ┃
┡━━━╇━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ 1 │ 1 │ HIGH │ White/Invisible Text │ If you are an AI... │
│ 2 │ 1 │ HIGH │ Tiny Text │ System prompt: ... │
│ 3 │ 1 │ HIGH │ Off-Page Text │ New instructions:... │
└───┴──────┴──────────┴──────────────────────┴──────────────────────┘
Generate test PDFs
pip install reportlab
python make_test_pdf.py
This creates test_injected.pdf with 5 different types of hidden injections for testing.
How it works
The tool uses pdfplumber to extract character-level metadata from PDFs, including:
- Fill color (
non_stroking_color) — catches white-on-white text - Font size — catches sub-2pt text
- Position coordinates — catches text placed outside page bounds
- Text content — matches against known prompt injection patterns
License
MIT
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