Awesome

Our mission with this project is to provide an always up-to-date and freely accessible map of the cloud landscape for every major cloud service provider.

We've decided to kick things off with collecting SSL certificate data of AWS EC2 and GCP machines, considering the value of this data to security researchers. However, we plan to expand the project to include more data and providers in the near future. Your input and suggestions are incredibly valuable to us, so please don't hesitate to reach out on Twitter or Discord and let us know what areas you think we should prioritize next!

How can SSL certificate data benefit you?

The SSL data is organized into CSV files, with the following properties collected for every found certificate:

• IP Address
• Common Name
• Organization
• Country
• Locality
• Province
• Subject Alternative DNS Name
• Subject Alternative IP address
• Self-signed (boolean)

So what can you do with this data?

Enumerate subdomains of your target domains

Search for your target's domain names (e.g. example.com) and find hits in the Common Name and Subject Alternative Name fields of the collected certificates. All IP ranges are scanned daily and the dataset gets updated accordingly so you are very likley to find ephemeral hosts before they are taken down.

Enumerate domains of your target companies

Search for your target's company name (e.g. Example, Inc.), find hits in the Organization field, and explore the associated Common Name and Subject Alternative Name fields. The results will probably include subdomains of the domains you're familiar with and if you're in luck you might find new root domains expanding the scope.

Enumerate possible sub-subdomain enumeration target

If the certificate is issued for a wildcard (e.g. *.foo.example.com), chances are there are other subdomains you can find by brute-forcing there. And you know how effective of this technique can be. Here are some wordlists to help you with that!

💡 Note: Remeber to monitor the dataset for daily updates to get notified whenever a new asset comes up!

Perform IP lookups

Search for an IP address (e.g. 3.122.37.147) to find host names associated with it, and explore the Common Name, Subject Alternative Name, and Organization fields to gain find more information about that address.

Discover origin IP addresses to bypass proxy services

When a website is hidden behind security proxy services like Cloudflare, Akamai, Incapsula, and others, it is possible to search for the host name (e.g., example.com) in the dataset. This search may uncover the origin IP address, allowing you to bypass the proxy. We've discussed a similar technique on our blog which you can find here!

Get a fresh dataset of live web servers

Each IP address in the dataset corresponds to an HTTPS server running on port 443. You can use this data for large-scale research without needing to spend time collecting it yourself.

Whatever else you can think of

If you use this data for a cool project or research, we would love to hear about it!

Additionally, below you will find a detailed explanation of our data collection process and how you can implement the same technique to gather information from your own IP ranges.

How it works

TB; DZ (Too big; didn't zoom):

1. We kick off the workflow with 2 simple bash scripts that retrieve the IP ranges:
   • AWS's IP ranges are filtered using a JQ query. We extract the IP ranges of EC2 machines by filtering for .prefixes[] | select(.service=="EC2") | .ip_prefix. Other services are excluded from this workflow since they don't support custom SSL certificates, making their data irrelevant for our dataset.
   • GCP's IP ranges are extracted using another JQ query: .prefixes[].ipv4Prefix.
2. Then, we use mapcidr to divide the IP ranges obtained in step 1 into smaller ranges, each containing up to 100k hosts (Thanks, ProjectDiscovery team!). This step will be handy in the next step when we run the parallel scanning process.
3. At the time of writing, the cloud IP ranges include over 75 million IP addresses, so scanning them all on a single machine would be impractical, which is where our file-splitter node comes into play. This node iterates through the input from mapcidr and triggers individual jobs for each range. When executing this workflow across multiple machines (let's say 30), they concurrently process these jobs in parallel, significantly reducing the overall runtime. This efficiency enables us to run the workflow on a daily basis, keeping the data constantly updated.
4. The parallel machines then spin up zgrab2 instances to collect the SSL certificates. After experimenting with various tools and techniques, zgrab2's tls module emerged as the fastest and most reliable option. Shout out to The ZMap Project team!
5. Each zgrab2 instance passes its data to a bash script that extracts the relevant data and writes its output to CSV files.
6. Finally, we collect and split all the CSV files into manageable 450 KB chunks to get GitHub to render the CSV files. These chunks are then pushed to this repository.

Contribution

All contributions/ideas/suggestions are welcome! Feel free to create a new ticket via GitHub issues, tweet at us @trick3st, or join the conversation on Discord.

Build your own workflows

We believe in the value of tinkering; cookie-cutter solutions rarely cut it. Sign up for a Trickest demo to customize this workflow to your use case, get access to many more workflows, or build your own workflows from scratch!

<img src="images/banner.png" />