png Offensive Security Tool: Breacher2 months ago
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Kali Linux Tutorials
Kconfig : Hardened-Check – A Tool For Checking The Hardening Options In The Linux Kernel Config

Kconfig is a tool For Checking The Hardening Options In The Linux Kernel Config There are plenty of Linux kernel hardening config options. A lot of them are not enabled by the major distros. We have to enable these options ourselves to make our systems more secure. But nobody likes checking configs manually. So let […]

The post Kconfig : Hardened-Check – A Tool For Checking The Hardening Options In The Linux Kernel Config appeared first on Kali Linux Tutorials.

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Hacking on Medium
eJPT as a 15 year old

https://cdn-images-1.medium.com/max/881/1*7RJL7AlTEqdHrU8oTQg96Q.png
Hello! My name is Arav Budhiraja, a 15 year old from India. Since the onset of the covid-19 pandemic, I gained an interest in the field of…

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Hacking on Medium
Best Approaches to Engage a Hacker for Mobile Phone Hack: Hackers for Hire

https://cdn-images-1.medium.com/max/1280/1*8nZtL6F_T3gAYOzpbymBlQ.jpeg
is a perfect choice in order to hack iPhone remotely.

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Hacking on Medium
8 bits only: DNS namespaces and domain resolution in action

https://cdn-images-1.medium.com/max/700/0*syeLdiIlH-viHx0W.gif
Note: I want to start writing more often, and I think that part of doing that will simply be word vomiting whatever i’m doing onto this…

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Hacking on Medium
What is multi-factor authentication? And it’s use.

https://cdn-images-1.medium.com/max/2000/1*cQjDY5s4aLlsbythPO4QTA.jpeg
There is a hacker attack every 39 seconds. With that continuous period, it is only a matter of time before the user names and unprotected…

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Business logic error leads to watching a standup comedy show for free!

Hi There,Continue reading on InfoSec Write-ups »
Read more...

Business logic error leads to watching a standup comedy show for free!
https://infosecwriteups.com/business-logic-error-leads-to-watching-a-standup-comedy-show-for-free-4300aeb714dc?source=rss------bug_bounty-5

Hi There,Continue reading on InfoSec Write-ups » (https://infosecwriteups.com/business-logic-error-leads-to-watching-a-standup-comedy-show-for-free-4300aeb714dc?source=rss------bug_bounty-5)

How I was able to Takeover accounts on Foxit.com

Hello to all Security Researchers and Bug Hunters who is reading this blog, Im Jefferson Gonzales also new in bug hunting, so without…Continue reading on Medium »
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WAF-A-MoLE - A Guided Mutation-Based Fuzzer For ML-based Web Application Firewalls
http://www.kitploit.com/2021/06/waf-mole-guided-mutation-based-fuzzer.html

A guided mutation-based fuzzer for ML-based Web Application Firewalls, inspired by AFL and based on the FuzzingBook (https://www.fuzzingbook.org/) by Andreas Zeller et al. Given an input SQL injection (https://www.kitploit.com/search/label/Injection) query, it tries to produce a semantic invariant query that is able to bypass the target WAF. You can use this tool for assessing the robustness of your product by letting WAF-A-MoLE explore the solution space to find dangerous "blind spots" left uncovered by the target classifier.
Architecture

How to cite us
WAF-A-MoLE implements the methodology presented in "WAF-A-MoLE: Evading Web Application (https://www.researchgate.net/publication/340917525_WAF-A-MoLE_Evading_Web_Application_Firewalls_through_Adversarial_Machine_Learning)Firewalls (https://www.kitploit.com/search/label/Firewalls) through Adversarial (https://www.kitploit.com/search/label/Adversarial) Machine Learning". If you want to cite us, please use the following (BibTeX) reference: @inproceedings{demetrio20wafamole,
title={WAF-A-MoLE: evading web application firewalls through adversarial machine learning},
author={Demetrio, Luca and Valenza, Andrea and Costa, Gabriele and Lagorio, Giovanni},
booktitle={Proceedings of the 35th Annual ACM Symposium on Applied Computing},
pages={1745--1752},
year={2020}
}

Running WAF-A-MoLE

Prerequisites
numpy (https://numpy.org/) keras (https://keras.io/) scikit-learn (https://scikit-learn.org/stable/) joblib (https://github.com/joblib/joblib) sqlparse (https://github.com/andialbrecht/sqlparse) networkx (https://networkx.github.io/) Click (https://click.palletsprojects.com/en/7.x/)
Setup
pip install -r requirements.txt
Sample Usage
You can evaluate the robustness of your own WAF, or try WAF-A-MoLE against some example classifiers. In the first case, have a look at the Model (https://github.com/AvalZ/waf-a-mole/blob/master/wafamole/models/model.py) class. Your custom model needs to implement this class in order to be evaluated by WAF-A-MoLE. We already provide wrappers for sci-kit learn and keras classifiers that can be extend to fit your feature extraction phase (if any).
Help
wafamole --help Usage: wafamole [OPTIONS] COMMAND [ARGS]...

Options:
--help Show this message and exit.

Commands:
evade Launch WAF-A-MoLE against a target classifier.
wafamole evade --help Usage: wafamole evade [OPTIONS] MODEL_PATH PAYLOAD

Launch WAF-A-MoLE against a target classifier.

Options:
-T, --model-type TEXT Type of classifier to load
-t, --timeout INTEGER Timeout when evading the model
-r, --max-rounds INTEGER Maximum number of fuzzing rounds
-s, --round-size INTEGER Fuzzing step size for each round (parallel fuzzing
steps)
--threshold FLOAT Classification threshold of the target WAF [0.5]
--random-engine TEXT Use random transformations instead of evolution
engine. Set the number of trials
--output-path TEXT Location were to save the results of the random
engine. NOT USED WITH REGULAR EVOLUTION ENGINE
--help Show this message and exit.


Evading example models
We provide some pre-trained models you can have fun with, located in wafamole/models/custom/example_models (https://github.com/AvalZ/waf-a-mole/tree/master/wafamole/models/custom/example_models). The classifiers we used are listed in the table below. Classifier name Algorithm WafBrain (https://github.com/BBVA/waf-brain) Recurrent Neural Network Token-based Naive Bayes Token-based Random Forest Token-based Linear SVM Token-based Gaussian SVM SQLiGoT (https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Directed Proportional Gaussian SVM SQLiGoT (https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Directed Unproportional Gaussian SVM SQLiGoT (https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Undirected Proportional Gaussian SVM SQLiGoT (https://www.sciencedirect.com/science/article/pii/S0167404816300451) - Undirected Unproportional Gaussian SVM
WAF-BRAIN - Recurrent Neural Newtork
Bypass the pre-trained WAF-Brain classifier using a admin' OR 1=1# equivalent. wafamole evade --model-type waf-brain wafamole/models/custom/example_models/waf-brain.h5 "admin' OR 1=1#"
Token-based - Naive Bayes