A LinkedIn information gathering tool for penetration testers to collect employee data from organizations.
Raven is a LinkedIn information gathering tool built for penetration testers and security researchers. It automates the collection of public employee data from LinkedIn profiles associated with a target organization, which can then be used to generate email addresses and check for data breaches. The tool streamlines the reconnaissance phase of security assessments by efficiently aggregating and storing potentially useful information.
Security professionals, penetration testers, and red teamers who need to perform organizational reconnaissance and identify potential targets for social engineering or credential-based attacks.
Developers choose Raven for its focused automation of LinkedIn data gathering, its ability to generate and check email addresses against breach databases, and its persistent storage that allows for flexible data export without repeated scanning. It fills a niche for open-source, CLI-based OSINT tools tailored for security assessments.
raven is a Linkedin information gathering tool that can be used by pentesters to gather information about an organization employees using Linkedin.
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Searches Google for LinkedIn profiles and extracts employee information automatically, streamlining reconnaissance by reducing manual effort in data collection.
Builds email addresses in multiple formats (e.g., first.last@domain.com) from collected names, enabling comprehensive testing for phishing simulations as shown in the export formats.
Checks generated emails against Have I Been Pwned to identify compromised accounts, adding a valuable layer to security assessments with a single command.
Uses SQLite to store scan results, allowing repeated data exports and breach checks without re-scanning, which improves workflow efficiency as highlighted in the README.
The README explicitly states it is not being maintained, leading to potential compatibility issues, lack of bug fixes, and no updates for changing web structures.
Requires installing chromedriver, compiling from source, and manually editing config files, making deployment cumbersome and error-prone for quick use.
Relies on scraping LinkedIn and Google, which is prone to breakage from site updates or anti-bot measures, limiting reliability in ongoing assessments.