"Securing the intelligence of tomorrow by challenging the models of today."
Welcome to the Attack on AI initiative. This is a comprehensive ecosystem dedicated to the study, simulation, and mitigation of adversarial attacks across all domains of artificial intelligence. We believe that true robustness is only achieved through rigorous red-teaming and proactive vulnerability research.
The initiative is currently divided into specialized laboratories, each focusing on a specific AI modality.
Domain: Large Language Models & Natural Language Processing
- Focus: Red-teaming transformer-based models (DistilBERT, RoBERTa) against semantic and contextual perturbations.
- Key Vectors: TextFooler (Word substitution), BERT-Attack (MLM sabotage), DeepWordBug (Character noise).
- Standards: Integrated with MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems).
Domain: Computer Vision & Image Processing
- Focus: Exploiting vulnerabilities in Convolutional Neural Networks (LeNet-5, ResNet) via pixel-level perturbations.
- Key Vectors: FGSM (Fast Gradient Sign Method), PGD (Projected Gradient Descent), CW (Carlini & Wagner).
- Toolkits: Powered by CleverHans and IBM Adversarial Robustness Toolbox (ART).
We are expanding the frontier of adversarial research into new horizons. The following labs are currently in the architectural phase:
- Objective: Red-teaming Automatic Speech Recognition (ASR) systems and voice biometrics.
- Scope: Psychoacoustic hiding of adversarial commands and speaker spoofing.
- Objective: Challenging high-stakes ML models in finance, healthcare, and risk assessment.
- Scope: Feature manipulation and evasion attacks on gradient-boosted trees and deep structured models.
- Objective: Physical-world adversarial perturbations for autonomous agents and control systems.
- Scope: Real-world attacks on object detection and pathfinding algorithms in mobile robotics.
All research within this hub is aligned with the MITRE ATLAS™ framework. We map every attack vector to industry-standard techniques to ensure our research contributes to the broader security community.
| Tactic | Techniques Explored |
|---|---|
| Reconnaissance | Dataset Inference, Model Inversion |
| Initial Access | Prompt Injection, Malicious Model Hosting |
| ML Model Evasion | Adversarial Perturbation, Input Transformation |
| Impact | Model Corruption, Evasion |
Shivam
AI Security Researcher | Lead Developer
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