Learn to code securely while having fun through our popular open source in-editor experience, designed for developers, students, and anyone curious about security. Get started for free in under 2 minutes, playing right from your browser.
Open-source, self-hosted AI vulnerability research tool that orchestrates agents to find and validate security issues in code.
The community's most comprehensive, continuously-updated index of research on Large Language Models for software vulnerability detection — papers across function-level, repository-level, agentic, and smart-contract detection, plus datasets, benchmarks, and surveys.
Lightweight, container-free sandbox for running commands with network and filesystem restrictions
DianXing - AI-Driven End-to-End Code Security Auditing
Globstar is a fast, feature-rich, and open-source static analysis toolkit for writing and running code checkers. Based on tree-sitter.
A fast universal code security scanner, written in Rust. Batteries included: supports 14 languages, TUI for triage, secrets, post-quantum audits, diff-aware scans and more 𓃥
Prevent merging of malicious code in pull requests
Django application that performs SAST and Malware Analysis for Android APKs
Focused malicious code detection ruleset, with a high protection-to-noise ratio
AI-powered SAST scanner that finds auth bypass, IDOR, and logic bugs Semgrep/CodeQL miss. Free GitHub Action. Supports Python, JS/TS, Go, PHP, Ruby.
Open-source AI verification infrastructure for deterministic verification of LLM outputs, tool calls, code, schemas, and agent state before production execution.
OpenVul: An Open-Source Post-Training Framework for LLM-Based Vulnerability Detection
Codeaudit - Modern Python source code security analyzer based on distrust.
Detect and patch vulnerabilities in AI-generated Python code — VS Code extension
Multi-language static analysis with cross-file taint tracking. Scan your repo, triage findings in your browser, commit triage state with your code. No cloud, no account.
AI code generation and improvement
The purpose of this document is to outline the security risks and vulnerabilities that may arise when implementing ai in web applications and to provide best practices for mitigating these risks.
LLM <-- MCP --> CodeQL( AST | CFG | CLI | LSP )
Security gate for AI-generated code - blocks the build until vulnerabilities are fixed
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