waf-bypass
adversarial-robustness-toolbox
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| waf-bypass | adversarial-robustness-toolbox | |
|---|---|---|
| 5 | 8 | |
| 1,098 | 4,447 | |
| 7.7% | 2.6% | |
| 7.7 | 9.7 | |
| 5 days ago | 7 days ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
waf-bypass
- WAF Bypass Tool - check your WAF before an attacker does
- WAF bypass is an open source tool to analyze the security of any WAF for False Positives and False Negatives using predefined and customizable payloads. Check your WAF before an attacker does.
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Nemesida WAF Free – free Nginx WAF with the minimum False Positive and amazing Web visualisation
We can also recommend our waf-bypass tool to check your WAF https://github.com/nemesida-waf/waf-bypass
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Does Your Waf Have False Positive
Did you check this ruleset with some bypass tools? Like https://github.com/nemesida-waf/waf-bypass or https://github.com/wallarm/gotestwaf ? I assume you have a lot of bypassed attacks (false negative).
adversarial-robustness-toolbox
- [D] Couldn't devs of major GPTs have added an invisible but detectable watermark in the models?
- [D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often?
- [D]: How safe is it to just use a strangers Model?
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[D] Does anyone care about adversarial attacks anymore?
Check out this project https://github.com/Trusted-AI/adversarial-robustness-toolbox
- adversarial-robustness-toolbox: Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
- Library for Machine Learning Security Evasion, Poisoning, Extraction, Inference
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Introduction to Adversarial Machine Learning
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
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[D] Testing a model's robustness to adversarial attacks
Depending on what attacks you want I've found both https://github.com/cleverhans-lab/cleverhans and https://github.com/Trusted-AI/adversarial-robustness-toolbox to be useful.
What are some alternatives?
imagemagick-lfi-poc - ImageMagick LFI PoC [CVE-2022-44268]
DeepRobust - A pytorch adversarial library for attack and defense methods on images and graphs
MHDDoS - Best DDoS Attack Script Python3, (Cyber / DDos) Attack With 56 Methods
auto-attack - Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
Cloudmare - Cloudflare, Sucuri, Incapsula real IP tracker.
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
HULK - Hulk DDos Attack script created using python libs
alpha-zero-boosted - A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
onelinepy - Python Obfuscator to generate One-Liners and FUD Payloads.
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
badblood - SonicWall SMA-100 Unauth RCE Exploit (CVE-2021-20038)
Differential-Privacy-Guide - Differential Privacy Guide