A dynamic infrastructure framework for distributing security scanning workloads across multiple cloud instances.
Axiom is a dynamic infrastructure framework that automates the deployment and management of disposable cloud instances pre-loaded with security tools. It solves the problem of slow, manual scanning by distributing workloads across many instances, enabling rapid security assessments for bug hunters and pentesters.
Security professionals, bug bounty hunters, and penetration testers who need to perform large-scale distributed scans across multiple targets efficiently.
Developers choose Axiom for its ability to drastically reduce scan times through parallelization, its support for multiple cloud providers, and its extensive library of pre-installed tools, eliminating the need for manual setup.
The dynamic infrastructure framework for everybody! Distribute the workload of many different scanning tools with ease, including nmap, ffuf, masscan, nuclei, meg and many more!
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Supports AWS, Azure, DigitalOcean, IBM Cloud, and Linode, allowing unified control across providers and avoiding vendor lock-in, as highlighted in the README.
Comes with over 100 security tools like nmap, nuclei, and ffuf baked into base images, eliminating manual setup and ensuring consistency for scans.
Enables spinning up hundreds of disposable instances in minutes via fleet commands, distributing workloads for rapid security assessments as demonstrated in demos.
Uses Packer to create fresh, pre-configured images for each deployment, reducing configuration drift and speeding up instance readiness.
Axiom Classic is in maintenance mode with development shifted to Ax Framework, meaning limited future updates and potential deprecation, as warned in the README.
Requires cloud provider API keys, Packer builds, and multi-step configuration, which can be time-consuming and prone to errors, especially on unsupported OSes.
Distributed scans across many instances incur significant cloud provider costs, and without careful management, bills can escalate unexpectedly.