A forensic evidence collection and analysis toolkit for macOS, gathering system data to detect and investigate malware infections.
OSXCollector is a forensic evidence collection and analysis toolkit for macOS. It runs on a potentially infected machine to gather detailed system information—such as startup items, installed applications, browser histories, and file metadata—into a JSON file for investigation. The tool helps security analysts answer questions about malware infections, their origins, and prevention strategies.
Security analysts, incident responders, and forensic investigators who need to collect and analyze evidence from macOS systems during security incidents or malware investigations.
OSXCollector provides a lightweight, dependency-free way to perform comprehensive forensic collection on macOS, with output designed for both manual analysis and integration with automated filtering tools. Its single-file deployment and detailed data gathering make it a practical choice for rapid incident response.
A forensic evidence collection & analysis toolkit for OS X
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The collector is a standalone Python script with no external dependencies, making it trivial to run on any macOS system without installation or configuration fuss, as emphasized in the README.
Gathers extensive data from kernel extensions, startup items, installed applications, browser histories, and more, outputting structured JSON that captures over 35,000 lines of evidence in a default run.
Allows selective collection of specific sections like startup items or downloads via command-line flags, enabling targeted investigations without full system scans.
Output is designed for automated processing with the companion OSXCollector Output Filters project, which helps highlight suspicious activity and streamline forensic analysis.
Relies on the default macOS Python interpreter; using alternative versions like from brew can break collection due to missing native bindings, adding setup complexity on customized systems.
Only collects data—analysis requires manual effort or separate tools like the Output Filters, meaning investigators must handle additional steps to derive insights from the JSON output.
Designed for one-time collection after an incident, with no real-time monitoring or proactive threat detection capabilities, restricting its utility in dynamic security environments.