DNS Queue - A Parallelised DNS Prober
What is DNS Parallel Prober?
This is a parallelised domain name prober to find as many subdomains of a given domain as fast as possible.
PLEASE NOTE this script probes DNS servers actively, so please use at your own risk. You are likely to get blacklisted and/or saturate your bandwidth. Whatever you do, it's your responsibility to make sure you have approval for it.
Install the requirements:
pip install -r requirements.txt # if you don't want to install stuff as root, do: # virtualenv venv # source venv/bin/activate # pip install -r requirements.txt
Scan all subdomains of
example.comusing the wordlist
subdomains.txt, using 100 threads. Save the results in
./dns-queue.py example.com 100 out.txt -i subdomains.txt --simulate
--simulatepart to really scan it.
$ python3 dns-queue.py example.com 100 out.txt -i subdomains-short.txt --simulate -f -e err.txt [*] SIMULATION IN PROGRESS [+] Output destination: 'out.txt' [+] Output destination will be overwritten. [+] Press CTRL-C to gracefully stop... [+] Finding authoritative name servers for domain... [+] Using name servers: ['220.127.116.11', '18.104.22.168'] [+] Checking wildcard DNS... [+] Will search for subdomains contained in 'subdomains-short.txt' [+] Saving results to out.txt... [+] DNS probing starting... 100% (200 of 200) |##################################################| Elapsed Time: 0:00:01 Time: 0:00:01 [+] DNS probing done. [+] Waiting for all threads to finish... [+] Done.
Please note: the
--simulate flag will return random results. This is by design.
If you want to bruteforce all subdomains (default length: 3), use:
./dns-queue.py example.com 100 out.txt
If you have a very fast upstream and don't mind flooding DNS servers, use 2000 threads:
./dns-queue.py example.com 2000 out.txt
By default the script uses the authoritative NS servers for the given domain. To use different DNS servers:
./dns-queue.py example.com 100 out.txt -i subdomains.txt -n ns1.example.com, -n ns2.example.com
For help and other options (e.g. subdomain length, DNS timeouts, etc.):
To stop: press
ctrl-c - it will wait for the last threads to finish before exiting.
Why 100 threads?
The optimal number of threads depends on:
- How fast is the uplink
- How quickly the domain name servers respond to queries
The value is best set empirically: on the same server, try with a value of 100 and keep doubling it until it doesn't go any faster.
In more details, the script creates N threads (here N is 100) and gives each thread a domain name to resolve. It then sleeps a small amount of time before checking whether each thread is done or not. The 'sleep time' is adjusted depending on how fast the threads resolve.
Since network I/O is orders of magnitude slower than CPU I/O, the number of threads should not be limited by the number of cores. In other words: each thread will spend most of its time waiting for a DNS response; that "idle time" can safely be used by other threads.
How do I install pip and virtualenv?
git clone ..... cd dns-parallel-prober # if debian/ubuntu: sudo apt-get install python-virtualenv python-pip # create virtualenv to install the required python libs virtualenv venv # activate it source venv/bin/activate pip install dnspython # to deactivate the virtualenv run: # deactivate
What if I want to use more cores / Why not using Multiprocess?
If you have lots of cores and you can send out data faster than your CPU can fork threads and you want to max out your machine then the simplest solution is:
- Split a subdomain list into N files (with
Nmatching the number of cores you want to use)
- Run this executable N times perhaps using
tmux, feeding it each file
- Make sure you use a different output for each process
Alternatively, fork this repo and write a multiprocessing version. Good luck.
Why threads and not processes?
Because in this scenario the bottleneck is the network, not the CPU. I'm happy to be proven wrong! Just fork this repo and submit a pull request and some empirical data to back your claim.
This is a demo of an older version:
The key thing is that the iteration frequency is dynamically adapted to the depletion speed, i.e. the faster the threads complete the sooner new ones will be added until an equilibrium is reached. The tool tries to stay reasonably close to the maximum value set.
subdomains.txt gathered from research carried out in 2014/15.