Hai on Hackerone

Leveraging Hai through our API. This repository contains a few tools that allow for retrieving and processing reports from the HackerOne API. It can fetch reports matching specified filters, send them to Hai (HackerOne's AI Copilot) for triage, and perform actions like posting comments and populating custom fields based on Hai's response.

Table of Contents

• Hai on Hackerone
  • Features at a Glance
  • Quick Start
  • Docker Usage
  • CLI Usage
  • CLI Example
  • Webhook Endpoint
  • Testing
  • Contributing
  • Troubleshooting

Features at a Glance

• Fetching Reports: The script retrieves reports that match our specified filters, such as program, severity, and state. This allows us to focus on the most critical issues first.

• AI-Powered Triage: Reports are sent to Hai for assessment. Hai evaluates each report and provides insights, helping us determine the validity and urgency of the issues.

• Automated Actions: Based on Hai's response, the script can post private comments on reports, update custom fields, and export responses to a CSV file for further analysis.

Quick Start

To install this project, you can use Docker Compose. Here are the steps:

1. Clone the repository: git clone hai-on-hackerone
2. Create a new file named .env in the root directory of the project with the following content (see .env.sample)

API_NAME=
API_KEY=
PROGRAM_HANDLE=
WEBHOOK_SECRET=
CUSTOM_FIELD_ID_VALIDITY=
CUSTOM_FIELD_ID_COMPLEXITY=
CUSTOM_FIELD_ID_PRODUCT_AREA=
CUSTOM_FIELD_ID_SQUAD_OWNER=
OWNERSHIP_FILE="./cli/config/ownership.csv.sample"
CSV_OUTPUT_FILE="./cli/data/hai-on-hackerone-output.csv"

Docker Usage

To run the script, simply execute the following command:

docker-compose up

This will start the Python script and begin processing reports.

CLI Usage

The CLI tool accepts the following arguments:

• --report: Specific report ID(s) to retrieve
• -r, --rating: Filter reports based on severity rating
• -s, --state: Filter reports based on report state
• -i, --reference: Filter reports based on NOT having an issue tracker reference
• -c, --comment_hai: Post private comment based on HackerOne AI response
• -f, --custom_field_hai: Update custom fields based on HackerOne AI response
• -o, --csv_output: Output HackerOne AI responses to CSV file
• -v, --verbose: Increase output verbosity

CLI Examples

This will retrieve critical vulnerability reports for the specified program:

python3 main.py -r critical

This will retrieve a specific report to be assessed on validity and its custom field will be updated:

python3 main.py --report 12345 --custom_field_hai

Webhook Endpoint

The project also includes a webhook endpoint for receiving and processing reports. Configure your HackerOne API settings in the .env file to use this endpoint.

Here's an example of how you can use the webhook endpoint:

curl -X POST \
  http://localhost:5000/webhook \
  -H 'Content-Type: application/json' \
  -d '{"data": {"report": {"id": "12345"}}}'

This will trigger the webhook endpoint to process the report with ID 12345.

Testing

Tests will run on each pull request and merge to the primary branch. To run them locally:

pytest 

Contributing

Contributions are welcome! Please open an issue or PR for any enhancements.

Troubleshooting

If you encounter any issues, please don't hesitate to report them.