π Use Cases
| Use Case | Description |
|---|---|
| π Password strength scoring | Quantitative scoring (0β10) for any given password |
| π§ Risk classification | Categorizes passwords as Weak, Fairly Strong, Strong |
| π΅οΈ Threat emulation | Emulates password cracking heuristics to spot vulnerable patterns |
| π§° DevSecOps integration | Plug into CI/CD pipelines for password policy enforcement |
| π¨βπ» User awareness tools | Build frontend UX tools to give users feedback on password creation |
π Trace.AI - AI-Powered Password Intelligence Engine
Trace.AI is an intelligent, ML-driven password checker designed to evaluate the strength, structure, and policy compliance of passwords. Built for modern security infrastructures, it leverages machine learning to identify weak, predictable, or non-compliant passwords based on real-world patterns and security datasets.
π Core Capabilities
β Password Strength Classification
Trace.AI scores passwords as Weak, Fairly Strong, or Strong using a combination of rule-based feature extraction and machine learning.
π― Pattern Recognition
Detects predictable and insecure patterns such as:
- Keyboard walks (
qwerty,asdf123) - Common substitutions (
p@ssw0rd) - Repeated sequences (
abcabc,123123) - Known dictionary or breached password similarities
π Policy Compliance
Checks if passwords meet enterprise-grade security policies, including: - Minimum length and entropy - Required character types (upper/lowercase, digit, special) - No whitespace, dictionary words, or reuse
π Datasets Used
Trace.AI was trained using curated, high-quality password datasets:
| Dataset | Description |
|---|---|
| cleanpasswordlist(modified) | Real-world passwords list, modified and feature engineered for better prediction and scoring |
π§ Machine Learning Models
Trace.AI supports and evaluates multiple ML models for robustness:
| Model | Strengths | Use |
|---|---|---|
| RandomForest | Non-linear classification, interpretable, fast | Production baseline |
| XGBoost | Handles imbalance, high accuracy, fast inference | Advanced detection |
| Decision Trees | Lightweight, interpretable | Edge device / fallback model |
All models are trained using engineered features like: - Length, character diversity - Entropy - Keyboard patterns - Regex-based leetspeak and substitution scoring
Project Goals
Trace.AI is engineered to support the following goals:
| Feature | Description |
|---|---|
| π Password Strength Estimator | Predict if password is Weak, Moderate, or Strong |
| π§ Pattern Analyzer | Identify insecure sequences, leetspeak, keyboard walks |
| π Policy Validator | Check adherence to defined password policies |
| π€ Exportable Reports | Download prediction logs for security audits |
| π Visual Dashboard | UI-based analysis of strength and structure (via Gradio) |
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