From c4fe008037dd0c1a3e58671aa645b8c7ccc02384 Mon Sep 17 00:00:00 2001 From: Matteo Meucci Date: Sun, 23 Nov 2025 13:48:15 +0100 Subject: [PATCH] Update AITG-MOD-01_Testing_for_Evasion_Attacks.md --- .../AITG-MOD-01_Testing_for_Evasion_Attacks.md | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/Document/content/tests/AITG-MOD-01_Testing_for_Evasion_Attacks.md b/Document/content/tests/AITG-MOD-01_Testing_for_Evasion_Attacks.md index ba9eaaf..f7fb0f1 100644 --- a/Document/content/tests/AITG-MOD-01_Testing_for_Evasion_Attacks.md +++ b/Document/content/tests/AITG-MOD-01_Testing_for_Evasion_Attacks.md @@ -31,18 +31,12 @@ This test identifies vulnerabilities in AI models related to evasion attacks, wh - **Implement Real-Time Detection Mechanisms**: Deploy separate detector models that are trained to distinguish between clean and adversarial inputs. If an input is flagged as adversarial, it can be rejected or sent for manual review. ### Suggested Tools for this Specific Test -- **Adversarial Robustness Toolbox (ART)**: A comprehensive Python library for generating adversarial examples, evaluating model robustness, and implementing defense mechanisms. - - Tool Link: [ART on GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox) -- **Foolbox**: A popular Python library for creating adversarial examples against a wide range of models (PyTorch, TensorFlow, JAX). - - Tool Link: [Foolbox on GitHub](https://github.com/bethgelab/foolbox) -- **SecML-Torch**: A Python library for robustness evaluation of deep learning models. - - Tool Link: [SecML-Torch on GitHub](https://github.com/pralab/secml-torch) -- **Maltorch**: A Python library for robustness evaluation of AI-based Windows malware detectors. - - Tool Link: [Maltorch on GitHub](https://github.com/zangobot/maltorch) -- **WAF-A-MoLE**: A Python library for testing the robustness of AI-based Web Application Firewalls. - - Tool Link: [WAF-A-MoLE on GitHub](https://github.com/AvalZ/WAF-A-MoLE) -- **TextAttack**: A Python framework specifically designed for adversarial attacks, data augmentation, and robustness training in NLP. - - Tool Link: [TextAttack on GitHub](https://github.com/QData/TextAttack) +- **Adversarial Robustness Toolbox (ART)**: A comprehensive Python library for generating adversarial examples, evaluating model robustness, and implementing defense mechanisms - [ART on GitHub](https://github.com/Trusted-AI/adversarial-robustness-toolbox) +- **Foolbox**: A popular Python library for creating adversarial examples against a wide range of models (PyTorch, TensorFlow, JAX) - [Foolbox on GitHub](https://github.com/bethgelab/foolbox) +- **SecML-Torch**: A Python library for robustness evaluation of deep learning models - [SecML-Torch on GitHub](https://github.com/pralab/secml-torch) +- **Maltorch**: A Python library for robustness evaluation of AI-based Windows malware detectors -[Maltorch on GitHub](https://github.com/zangobot/maltorch) +- **WAF-A-MoLE**: A Python library for testing the robustness of AI-based Web Application Firewalls - [WAF-A-MoLE on GitHub](https://github.com/AvalZ/WAF-A-MoLE) +- **TextAttack**: A Python framework specifically designed for adversarial attacks, data augmentation, and robustness training in NLP - [TextAttack on GitHub](https://github.com/QData/TextAttack) ### References - Madry, Aleksander, et al. "Towards Deep Learning Models Resistant to Adversarial Attacks." ICLR 2018. [Link](https://arxiv.org/abs/1706.06083)