Cryptocurrency hacks list

Cryptocurrency risk scoring services list

RugWatch is a real-time Solana rugpull and honeypot detection bot. It monitors new token launches across major DEX ecosystems, analyzes on-chain risk signals (authorities, liquidity, trading rules), and alerts via Telegram and Discord with a clear risk score.

A complete Web3 security toolkit combining AI-powered token auditing, ML-based deployer reputation scoring, and live Etherscan V2 data. Includes static analysis for rugpull detection, RandomForest reputation modeling, contract-fetching automation, and Solidity on-chain registries for transparent, reproducible security insights.

A deep technical article exploring how AI, feature engineering, and static smart-contract analysis uncover rugpull risks before humans detect them. Covers Solidity pattern mining, mint abuse detection, blacklist/fee manipulation signals, ML-inspired scoring models, and how to quantify ERC-20 token scam probability.

Phoneint-OSINT-Toolkit — a privacy-first phone-number OSINT toolkit (CLI + minimal GUI). Parses and normalizes numbers (E.164), enriches with deterministic metadata, runs optional async adapters (DuckDuckGo, Google, public datasets), computes explainable risk scores and owner intelligence, and exports JSON/CSV/PDF reports.

A hybrid Solidity + Python security toolkit that analyzes ERC-20 token contracts using static pattern extraction and ML-inspired scoring. Detects mint backdoors, blacklist controls, fee manipulation, trading locks, and rugpull mechanics. Outputs interpretable risk scores, labels, and structured features for deeper analysis.

AI-powered real-time smart contract scanner that connects Machine Learning with Etherscan V2 to analyze newly deployed contracts instantly. Fetches verified Solidity code, performs static risk analysis, computes ML-driven deployer trust scores, and generates full security intelligence pipelines for Web3 threat detection.

A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.

A deep technical exploration of how malicious smart-contract developers weaponize fee logic in ERC-20 tokens. Covers dynamic tax flipping, hidden sell traps, fee obfuscation, whitelist-based bypasses, liquidity-drain funnels, attack timelines, forensic analysis, mathematical modeling, and ML-powered detection strategies for tax abuse.

A customer intelligence engine that predicts subscription probability, models purchase frequency, and computes a unified loyalty risk score. Includes explainability, segment insights, and a scenario simulator, all integrated into an interactive Streamlit dashboard.

A research-grade framework for extracting, classifying, and analyzing the “genetic” behavior of smart contract tokens. Identifies economic traits, supply mutations, fee patterns, permission risks, upgradeability vectors, and scam species using a structured gene taxonomy with risk scoring, HTML reports, and token comparison tools.

IP threat detection with automatic blacklisting

Discover and audit MCP servers for security vulnerabilities across Claude Code, Cursor, VS Code, and more

Extensión de navegador (Chrome/Brave/Edge) que evalúa la “salud” de privacidad de una web con una nota A–E basada en cookies, señales de tracking y el análisis de su política de privacidad (IA opcional).

AI-powered KYC automation platform with adaptive risk scoring, multi-layer biometrics, OCR, deepfake detection, and cryptographic credentialing. Reduces verification time from 48–72 hours to 8–12 minutes while improving fraud detection to 98.5%.

Context-Aware Vulnerability Risk Scoring

A multi-agent AI system using Google AI Agent SDK and Gemini to analyze real-time route safety (crime, weather, lighting) and optimize navigation.

XRPL liquidity safety signal – TRAXR dashboard & read-only API that scores AMM pools, trustlines & issuers using a private CTS engine.

CryptGuardian is a research-level concept, created from the perspective of a developer concerned about long-term security for distributed systems. It is not a final product, but an exploration of defense strategies against modern and future threats, including AI-assisted attacks and quantum cryptanalysis.

A full-stack machine learning application that predicts loan approval and credit risk percentage using the Home Credit Default Risk dataset. It integrates a trained classification model with a Flask API and React frontend to provide real-time risk evaluation based on applicant financial and external credit bureau data.

📈 Automate trading with this arbitrage bot for passive income and explore the potential of crypto investments effortlessly.

Streamlit Health Intelligence Platform: digital habits → wellbeing risk scoring, cohort KPIs + 90-day trends, and scenario simulator (synthetic 3,500×24; high_risk_flag).

LangGraph-powered AI banking assistant with balance checks, secure money transfers, and fraud detection using risk-based transaction routing.

Phishing detection CLI built on Kali Linux that performs SSL validation, WHOIS analysis, domain heuristics, and weighted risk scoring to classify suspicious URLs.

Actuarial data science project analyzing insurance claims to identify potential fraud and assess customer risk. Includes Python preprocessing, feature engineering, and a stakeholder-ready Tableau dashboard.

OpenSiteTrust is an open, explainable, and reusable website scoring ecosystem

AI-powered corruption detection platform analyzing 3.1M Mexican federal procurement contracts (2002-2025). 16-feature per-sector calibrated risk model with 96% AUC, validated against 15 documented corruption cases worth 253B MXN.

A collection of medical calculations in Python. Example: MELD score for liver disease.

Deterministic abuse signals for domains/emails (with explain)