Dark C0d3rs

Welcome to the frontier where offensive security meets artificial intelligence.
This thread is a living index of the core concepts, tools, research papers, and attack vectors in Adversarial Machine Learning (AML) — the art of abusing, bypassing, or hardening AI systems.

What is Adversarial Machine Learning?

AML focuses on exploiting weaknesses in machine learning models to:

• Fool classifiers (e.g., malware labeled as benign)
  
• Poison training data
  
• Steal models or data
  
• Craft inputs that trigger unexpected behavior
  

Quote:If traditional apps have logic bugs, AI models have decision boundary bugs.

Offensive Techniques

1. Evasion Attacks – Modify input to cause misclassification (e.g., making malware look benign).
   
2. Model Poisoning – Inject malicious data during training to corrupt future predictions.
   
3. Model Extraction – Reverse engineer black-box models using API access.
   
4. Membership Inference – Identify whether a data point was in the training set.
   
5. Prompt Injection (LLMs) – Manipulate instructions and outputs in AI chatbots.
   

Tools & Frameworks
You are not allowed to view links. Register or Login to view.  :   NLP adversarial testing
You are not allowed to view links. Register or Login to view. :   Evasion & defense methods
You are not allowed to view links. Register or Login to view. :  Comprehensive AML testing
You are not allowed to view links. Register or Login to view. :  White-box and black-box attacks
You are not allowed to view links. Register or Login to view. :  CV attacks on PyTorch/TensorFlow

Must-Read Papers

• Explaining and Harnessing Adversarial Examples – You are not allowed to view links. Register or Login to view.
  
• Backdooring Neural Networks – You are not allowed to view links. Register or Login to view.
  
• Adversarial Examples Are Not Bugs, They Are Features – You are not allowed to view links. Register or Login to view.
  
• Universal Adversarial Perturbations – You are not allowed to view links. Register or Login to view.
  

LLM-Specific Attacks (GPT, Claude, etc.)

• Prompt Injection & Jailbreaks
  
• Training Data Leakage
  
• Fine-Tuning Exploits
  
• Prompt Leaking via Reverse Prompting
  

Let’s build a solid knowledge base for adversarial AI security.
If you're reading a cool paper, building a model-breaking tool, or fuzzing GPT — post it here.

? “Attackers think in graphs. ML models think in probabilities. We think in both.”