PDF Converter with Multiple Modes:

PDF Converter with Multiple Modes:#

This script demonstrates the use of the PDFConverter for generating PDFs in two different modes:

  • Template-Based PDF Generation: Utilize a YAML template to render dynamic content into a PDF.

  • Direct Prompt PDF Generation: Convert plain string prompts into PDFs without using a template.

The PromptSendingOrchestrator is used to handle the interaction with the PDFConverter and the mock TextTarget target system.

Key Features#

  1. Template-Based Generation:

    • Populate placeholders in a YAML-based template using dynamic data.

    • Convert the rendered content into a PDF using the PDFConverter.

  2. String-Based Generation:

    • Accept plain string prompts directly.

    • Generate PDFs from the provided string without using any template.

import pathlib

from pyrit.common import IN_MEMORY, initialize_pyrit
from pyrit.common.path import DATASETS_PATH
from pyrit.models import SeedPrompt
from pyrit.orchestrator import PromptSendingOrchestrator
from pyrit.prompt_converter import PDFConverter
from pyrit.prompt_target import TextTarget

initialize_pyrit(memory_db_type=IN_MEMORY)

# Define dynamic data for injection
prompt_data = {
    "hiring_manager_name": "Jane Doe",
    "current_role": "AI Engineer",
    "company": "CyberDefense Inc.",
    "red_teaming_reason": "to creatively identify security vulnerabilities while enjoying free coffee",
    "applicant_name": "John Smith",
}

# Load the YAML template for the PDF generation
template_path = (
    pathlib.Path(DATASETS_PATH) / "prompt_converters" / "pdf_converters" / "red_teaming_application_template.yaml"
)
if not template_path.exists():
    raise FileNotFoundError(f"Template file not found: {template_path}")

# Load the SeedPrompt from the YAML file
prompt_template = SeedPrompt.from_yaml_file(template_path)

# Initialize the Azure OpenAI chat target (or mock target if not needed)
prompt_target = TextTarget()

# Initialize the PDFConverter
pdf_converter = PDFConverter(
    prompt_template=prompt_template,
    font_type="Arial",
    font_size=12,
    page_width=210,
    page_height=297,
)

# Define a single prompt for the orchestrator
prompts = [str(prompt_data)]

# Initialize the orchestrator
orchestrator = PromptSendingOrchestrator(
    objective_target=prompt_target,  # Target system (Azure OpenAI or other LLM target)
    prompt_converters=[pdf_converter],  # Attach the PDFConverter
    verbose=False,  # Set to True for detailed logging
)

await orchestrator.send_prompts_async(prompt_list=prompts)  # type: ignore
await orchestrator.print_conversations_async()  # type: ignore

{'__type__': 'TextTarget', '__module__': 'pyrit.prompt_target.text_target'}: user: D:\git\PyRIT-internal\PyRIT\dbdata\prompt-memory-entries\urls\1738382955429053.pdf
user: D:\git\PyRIT-internal\PyRIT\dbdata\prompt-memory-entries\urls\1738382955429053.pdf

Direct Prompt PDF Generation (No Template)#

# Define a simple string prompt (no templates)
prompt = "This is a simple test string for PDF generation. No templates here!"

# Initialize the TextTarget (mock target for testing)
prompt_target = TextTarget()

# Initialize the PDFConverter without a template
pdf_converter = PDFConverter(
    prompt_template=None,  # No template provided
    font_type="Arial",
    font_size=12,
    page_width=210,
    page_height=297,
)

# Define the list of prompts as strings (required by PromptSendingOrchestrator)
prompts = [prompt]

# Initialize the orchestrator
orchestrator = PromptSendingOrchestrator(
    objective_target=prompt_target,
    prompt_converters=[pdf_converter],
    verbose=False,
)

await orchestrator.send_prompts_async(prompt_list=prompts)  # type: ignore
await orchestrator.print_conversations_async()  # type: ignore

{'__type__': 'TextTarget', '__module__': 'pyrit.prompt_target.text_target'}: user: D:\git\PyRIT-internal\PyRIT\dbdata\prompt-memory-entries\urls\1738380368779812.pdf

user: D:\git\PyRIT-internal\PyRIT\dbdata\prompt-memory-entries\urls\1738380368779812.pdf

Modify Existing PDF with Injection Items#

import tempfile
from pathlib import Path

import requests

from pyrit.common import IN_MEMORY, initialize_pyrit
from pyrit.orchestrator import PromptSendingOrchestrator
from pyrit.prompt_converter import PDFConverter
from pyrit.prompt_target import TextTarget

initialize_pyrit(memory_db_type=IN_MEMORY)

# This file isn't copied to our pipeline
url = "https://raw.githubusercontent.com/Azure/PyRIT/main/pyrit/datasets/prompt_converters/pdf_converters/fake_CV.pdf"

with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
    response = requests.get(url)
    tmp_file.write(response.content)

cv_pdf_path = Path(tmp_file.name)

# Define injection items
injection_items = [
    {"page": 0, "x": 50, "y": 700, "text": "Injected Text", "font_size": 12, "font": "Helvetica", "font_color": (255, 0, 0)},  # Red text
    {"page": 1, "x": 100, "y": 600, "text": "Confidential", "font_size": 10, "font": "Helvetica", "font_color": (0, 0, 255)}  # Blue text
]

# Define a simple string prompt (no templates)
prompt = "This is a simple test string for PDF generation. No templates here!"

# Initialize the TextTarget (mock target for testing)
prompt_target = TextTarget()

# Initialize the PDFConverter with the existing PDF and injection items
pdf_converter = PDFConverter(
    prompt_template=None,  # No template provided
    font_type="Arial",
    font_size=12,
    page_width=210,
    page_height=297,
    existing_pdf=cv_pdf_path,  # Provide the existing PDF
    injection_items=injection_items,  # Provide the injection items
)

# Define the list of prompts as strings (required by PromptSendingOrchestrator)
prompts = [prompt]

# Initialize the orchestrator
orchestrator = PromptSendingOrchestrator(
    objective_target=prompt_target,
    prompt_converters=[pdf_converter],
    verbose=False,
)

# Run the orchestrator to modify the PDF and inspect the result
await orchestrator.send_prompts_async(prompt_list=prompts)  # type: ignore
await orchestrator.print_conversations_async()  # type: ignore

[09:10:28][26][ai-red-team][INFO][Processing page 0 with 2 injection items.]
[09:10:28][31][ai-red-team][INFO][Processing page 1 with 2 injection items.]
[09:10:28][33][ai-red-team][INFO][Processing page 2 with 2 injection items.]
{'__type__': 'TextTarget', '__module__': 'pyrit.prompt_target.text_target'}: user: D:\git\PyRIT-internal\PyRIT\dbdata\prompt-memory-entries\urls\1738429828035527.pdf
user: D:\git\PyRIT-internal\PyRIT\dbdata\prompt-memory-entries\urls\1738429828035527.pdf

import os

orchestrator.dispose_db_engine()
os.remove(cv_pdf_path)
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