Trivy is a comprehensive, production-grade security scanner for container images, filesystems, Git repositories, Kubernetes clusters, cloud infrastructure (IaC), and virtual machines. It detects vulnerabilities in OS packages and language-specific dependencies, scans for IaC misconfigurations (Terraform, CloudFormation, ARM, Ansible, Helm, Dockerfile), secrets, and license compliance. The architecture is modular: a CLI entry point (`cmd/trivy/main.go`) orchestrates scanning via a file analysis engine (`pkg/fanal`), dependency parsers (`pkg/dependency/parser`), vulnerability detectors (`pkg/detector`), IaC scanners with Rego-based policies (`pkg/iac`), and multiple output formats (JSON, SARIF, CycloneDX, SPDX, table, etc.). It supports client-server mode via gRPC, a plugin system, WASM modules, and VEX/SBOM attestation integration. The codebase is well-structured with clear separation of concerns, extensive test coverage, and many reusable utility packages (parallel processing, generic sets, caching, version constraints, license expression parsing). It is actively maintained with frequent releases and a large community.
Nuclei is a template-based vulnerability scanner written in Go. It allows users to define security checks using YAML or JSON templates, which can be executed against a wide range of targets via multiple protocols (HTTP, DNS, network, file, etc.). The scanner features a modular architecture with a core engine that orchestrates template execution, a template system for parsing and compiling checks, and protocol-specific implementations for sending requests and processing responses. Key features include workflow chaining for multi-step attacks, fuzzing capabilities for dynamic application security testing, JavaScript extensibility for custom logic in templates, and integrations with various issue trackers and cloud platforms for result reporting. The project emphasizes performance through optimizations like template clustering (deduplicating identical requests across templates) and connection pooling. The codebase demonstrates strong engineering practices in several areas: a self-contained template clustering system for request deduplication, a JavaScript binding generation system that automates the creation of Goja-compatible libraries, a workflow context propagation system for sharing data between template steps, and a cryptographic template signing mechanism to ensure integrity. These components are designed to be reusable and showcase non-obvious solutions to common problems in security tooling. Maintenance is active with recent commits and a vibrant community. The project keeps pace with its ecosystem, showing low technical drift through regular updates to dependencies and active maintenance of generated code. Its extensive test suite and CI/CD pipelines further indicate a healthy, well-maintained project.
Bandit is a well-established Python security linter that performs static analysis by parsing source code into ASTs and running a configurable set of plugins against AST nodes. It is primarily a CLI tool, but also exposes a plugin architecture for custom checks and formatters via setuptools entry points. Key entry points include the main CLI (bandit.cli.main), baseline comparison utility (bandit.cli.baseline), and config generator (bandit.cli.config_generator). The core engine (bandit/core/manager.py, node_visitor.py, context.py) orchestrates file discovery, AST traversal, test execution, and result aggregation. It supports multiple output formats (JSON, SARIF, CSV, HTML, XML, YAML, text, screen) and integrates with CI/CD pipelines, pre-commit hooks, and Git via .bandit files. The project is actively maintained, supports Python 3.10+, and has extensive test coverage, though some legacy config conversion code and removed test IDs (e.g., B309, B320) indicate minor drift. Bandit is a reference implementation for a plugin-based security linter with a clean separation between core, plugins, and formatters.
gosec is a production-grade static analysis security scanner for Go source code. It analyzes Go programs by traversing the AST and SSA representations to detect a wide range of security vulnerabilities, including SQL injection, command injection, path traversal, cross-site scripting (XSS), hardcoded credentials, weak cryptography, TLS misconfigurations, and more. The tool is distributed as a CLI binary, a Docker image, a GitHub Action, and a reusable Go library. The architecture is modular: a core analysis engine loads Go packages, applies AST-based rules and SSA-based analyzers, and runs a configurable taint analysis engine that tracks data flow from user-controlled sources to dangerous sinks. Results are output in multiple formats (text, JSON, SARIF, CSV, JUnit, HTML, SonarQube, golint, YAML). The tool also integrates with AI providers (OpenAI, Claude, Gemini, Atlas) to suggest automated fixes for detected vulnerabilities. Notable design highlights include a generic LRU cache for regex matching, a path-based rule exclusion filter, a comprehensive CWE knowledge base, and a well-abstracted taint analysis configuration system. The codebase demonstrates strong engineering practices with extensive test coverage, performance benchmarks, and CI/CD pipelines for release and scanning.
sqlmap is an automated penetration testing tool that detects and exploits SQL injection vulnerabilities in web applications. Its core functionality includes database fingerprinting, data extraction (tables, columns, users, passwords), file system read/write, OS command execution, and out-of-band exfiltration via DNS or ICMP. The architecture is a modular monolith: a central controller orchestrates DBMS-specific plugins (30+ databases), injection technique engines (boolean blind, error-based, UNION, time-based, stacked queries, DNS, OData, SPARQL, JWT, NoSQL, etc.), and tamper scripts for WAF bypass. The tool exposes both a CLI (sqlmap.py) and a REST API (sqlmapapi.py). External dependencies are minimal—most third-party libraries are vendored (chardet, colorama, beautifulsoup, pydes, fcrypt). The codebase demonstrates deep knowledge of SQL dialects, HTTP protocol handling, and security research, with extensive offline test suites and deterministic test harnesses. Notable technical strengths include pure-Python protocol clients (TDS, Kerberos, gRPC-Web), disk-spilling data structures (BigArray), and a safe JSON serializer that replaces pickle. Drift risks include remnants of Python 2 compatibility shims, bundled crypto implementations, and static fingerprinting data that may lag behind new DBMS versions.
WPScan is a Ruby-based CLI security scanner for WordPress sites. It detects vulnerabilities in WordPress core, plugins, and themes, enumerates users and misconfigurations, and supports password brute-force attacks. The architecture follows a controller/finder pattern: controllers orchestrate scan phases (version detection, enumeration, password attack), and finders implement passive/aggressive detection strategies for specific items (plugins, themes, users, backups, etc.). HTTP client is a singleton wrapping Typhoeus with caching, throttling, and proxy support. Vulnerability data is retrieved from the WPScan API or local enterprise database dumps. The codebase is well-structured with clear separation of concerns, extensive test coverage, and support for multiple output formats (CLI, JSON, SARIF). It is actively maintained and regularly updated to keep pace with WordPress ecosystem changes.
Keydd is an HTTP(s) proxy tool that performs sensitive information detection and AI-driven API business logic analysis. It acts as a MITM proxy to intercept traffic, matches content against 30+ regex patterns for credentials/API keys, and optionally uses LLMs to analyze API endpoints for business purpose, sensitivity, and penetration priority. The architecture features separate layers for proxy handling, rule-based detection, AI analysis (with rate limiting, token tracking, and metrics), database persistence (SQLite/MySQL/PostgreSQL), and notifications. Key entry points include main.go for startup, proxy/handler.go for traffic processing, and cmd/match_rules.go for sensitive pattern matching.
Garden is a one-URL automated asset discovery and passive security scanning tool designed for authorized targets. It performs passive, read-only scans (bounded GET requests within same-origin boundaries) without active exploitation. The tool supports two primary modes: quick (anonymous) scans and authenticated coverage analysis across three fixed contexts (anonymous, user, admin). Outputs include structured Markdown reports with asset inventory, evidence, security control observations, and explicit coverage gap explanations, emphasizing that zero findings do not imply safety unless coverage is fully qualified. The architecture is a Python 3.10+ application using FastAPI for the web API, Typer for the CLI, SQLAlchemy with Alembic for database management, and Playwright for browser automation during login and inventory collection. The system follows a service layer pattern shared by CLI, Web, and API entry points, with a multi-stage pipeline (validate → collect → normalize → compare coverage → replay authorization → analyze → report) orchestrated by ScanApplicationService. External dependencies include httpx for HTTP requests, Pydantic for data validation, and Jinja2 for templating. Garden is positioned as an internal AppSec workflow for post-authentication verification of authorized application surfaces, not as a general-purpose vulnerability scanner like Burp or ZAP. It features centralized redaction of sensitive data across all outputs, an ephemeral secret store for temporary credential handling, and a plugin-based security check system with six built-in low-risk checks. The codebase includes extensive test coverage (unit, integration, e2e), a comprehensive threat model, and detailed implementation plans for versioned releases (v0.2 trusted report quality, v0.3 passive authenticated coverage).
OpenDoor is an advanced CLI security reconnaissance platform for directory discovery, subdomain enumeration, WAF detection, and content fingerprinting. It employs a modular architecture with a plugin-based analysis pipeline (sniffers), network transport abstraction (direct, proxy, VPN), and extensive output formatting (JSON, CSV, SQLite, SARIF). Its core scanning engine, state machine, and plugin systems demonstrate sophisticated engineering, featuring auto-calibration, semantic soft-404 diffing, and a clean exception hierarchy.
rnsec is a zero-configuration static analysis security scanner for React Native and Expo applications. It scans JavaScript/TypeScript, XML, plist, and JSON files for over 85 security rules covering storage, network, authentication, cryptography, logging, Android/iOS platform issues, and supply chain risks. The tool uses Babel AST parsing and regex pattern matching to detect hardcoded secrets, insecure configurations, and common vulnerabilities. It generates HTML, JSON, and Markdown reports, supports CI/CD integration with changed-files scanning, and can post results as GitHub PR comments. The architecture is a monolith CLI application built with Commander, with a modular rule engine that registers scanner groups. The codebase is well-structured, TypeScript strict, and includes comprehensive tests for all rules.
ClawScan is a Go-based CLI tool for composable security scanning of AI agent skills and plugins. It orchestrates multiple external scanners (e.g., Snyk, VirusTotal, Socket, AIG, Cisco, SkillSpector) against skill directories, collects raw evidence, and optionally feeds results to an LLM judge for evaluation. The tool supports profile-based configuration (built-in profiles like `clawhub` and `openclaw-install-policy`), Docker sandboxing, declarative gate rules for automated policy enforcement, and benchmark execution against datasets like SkillTrustBench and clawhub-security-signals. It is distributed via npm (`@openclaw/clawscan`) and GitHub Releases, with a rich CI/CD pipeline including automated runtime image builds, dependency updates, and a Hugging Face leaderboard for security signals research. While primarily an application, it has strong data/research elements through its benchmark infrastructure and leaderboard submission validation.
Globstar is a static analysis toolkit that enables developers to write custom checkers in YAML or Go and run them via a single portable binary. It uses tree-sitter for AST-based analysis, supporting multiple languages (Go, Java, JavaScript, Python, Ruby, etc.). The tool is designed to be fast, dependency-free, and CI-friendly, with a focus on enforcing learned patterns such as security vulnerabilities and code quality issues. The architecture consists of a CLI layer, an analysis engine for parsing and scope resolution, and a checker system with built-in and custom checker support.
OASIS is a CLI-first security analysis tool written in Python. It orchestrates a multi-phase LangGraph pipeline (Discover → Scan → Expand → Deep → Verify → Report) to detect vulnerabilities in source code using local LLMs via Ollama or OpenAI-compatible servers. The tool supports 25+ vulnerability types across multiple languages, produces deterministic verdicts without LLM reliance, and integrates with CI via severity-gated exit codes, suppression registries, and report diffing. It is installed via pipx or Docker and run as `oasis -i /path --models ...`.
Nova Proximity is a modular Python security scanner designed specifically for MCP (Model Context Protocol) servers and Agent Skills. The application provides comprehensive discovery and analysis capabilities, including tool/prompt/resource enumeration for MCP servers and structural/permission analysis for Agent Skills. Its architecture separates concerns into distinct libraries: mcp_scanner_lib for MCP protocol handling, skill_scanner_lib for Agent Skills analysis, and nova_evaluator_lib for NOVA-powered security evaluation. The tool supports multiple transport mechanisms (stdio, SSE, Streamable HTTP) with automatic fallback, provides detailed function signature analysis, and generates actionable security reports with pattern-specific remediation guidance.
reNgine is a Django-based automated security reconnaissance and attack-surface management platform. It orchestrates 20+ external security tools (Subfinder, Amass, Naabu, Nuclei, FFUF, GoSpider, Dalfox, WAFW00f, EyeWitness, theHarvester, h8mail) via Celery task chains to perform subdomain discovery, port scanning, HTTP crawling, directory fuzzing, OSINT gathering, technology fingerprinting, WAF detection, and vulnerability scanning. Results are parsed from tool outputs (nmap XML, nuclei JSON, dalfox JSON, ffuf JSON), persisted in PostgreSQL, and exposed through a Django REST Framework API plus a jQuery/Bootstrap frontend with server-side DataTables, dashboards, and WeasyPrint PDF reports. The system supports multi-project tenancy, role-based access control (sys_admin, penetration_tester, auditor), scheduled scans via Celery Beat, and deep HackerOne bug-bounty integration for program import, bookmarking, and vulnerability report submission. It also integrates LLMs (OpenAI and local Ollama) for vulnerability description enrichment and attack-surface suggestions. Architecturally it is a monolithic Django application with four feature apps (dashboard, scanEngine, startScan, targetApp, recon_note) and a Docker deployment composed of web, celery, celery-beat, redis, postgres, nginx, and ollama services. The orchestration core, web/reNgine/tasks.py (~3000 lines), composes Celery groups/chords/chains, executes shell commands against external tools, parses their structured output, and persists scan results. The frontend is server-rendered Django templates with heavy inline JavaScript and a supplementary DRF API layer in web/api/views.py. Although it collects and aggregates large volumes of scan-derived data, this is fundamentally an operational security application, not a data-research project. The codebase is actively patched (recent CVE fixes for command injection and XSS) and feature-rich, but carries notable technical debt: an end-of-life Django 3.2 base, old vendored JS plugins (DataTables 1.10.16, Lightbox from 2018), hardcoded container filesystem paths, a monolithic task module mixing orchestration/parsing/persistence, and dead or commented code. The strongest reusable pieces are the RengineTask Celery base class (standardized task logging, caching, and notifications), the dual-provider LLM abstraction with OpenAI/Ollama backends, and a custom server-side datatable filtering language applying operators (=, >, <, !, &, |) across subdomain, endpoint, and vulnerability views. It is best characterized as effective, pragmatic, security-tool engineering rather than clean, academically elegant architecture.
APKDeepLens is a Python-based CLI static analysis tool for Android APK security assessment. It decompiles APKs using bundled JADX, analyzes AndroidManifest.xml for misconfigurations, scans decompiled source code for OWASP Mobile Top 10 vulnerabilities, detects hardcoded secrets and insecure URLs, and generates reports in JSON/HTML/PDF/TXT formats. The architecture is monolithic with modular components for manifest analysis, code scanning, secret extraction, and report generation. Entry point is APKDeepLens.py with argparse CLI interface. External dependencies include JADX (bundled), xhtml2pdf for PDF generation, and python-bidi for PDF rendering.