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OpenTaint

The open source taint analysis engine for the AI era

Formal taint analysis for application security — finds what AST-pattern matchers miss, lets LLM agents enact vulnerabilities as rules, scales where neither can alone.

GitHub release Go Report Card License: Apache 2.0 Go Version Discord

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OpenTaint taint analysis demo

Supported technologies and integrations

Java     Kotlin     Spring     GitHub      GitLab

The most thorough taint analysis engine for Spring apps

Roadmap

Python     Go     C#     JavaScript     TypeScript

More screenshots

OpenTaint scan output

OpenTaint summary output

OpenTaint summary output

OpenTaint summary output

OpenTaint summary output


Why OpenTaint?

OpenTaint is an open-source alternative to Semgrep Pro and CodeQL — a formal inter-procedural taint engine you can customize and self-host, built so AI agents drive your security analysis without burning tokens on every scan.

AI generates production code faster than security teams can keep up with, and the two kinds of tooling built to catch what it gets wrong each force a bad trade-off:

  • AST-pattern matchers (Semgrep OSS, ast-grep, linters) are free and fast, but they match syntax, not data flow — untrusted input that crosses a function boundary or a persistence layer slips right past. The deeper, inter-procedural analysis that does catch it has long been locked inside proprietary tools.
  • LLM security agents find what pattern matchers miss, but they re-read your code on every run. The tokens add up with every file, every commit, every CI build — and a probabilistic model still can't promise it caught everything.

OpenTaint gives you the depth of an LLM agent at the cost of a static analyzer:

  • Find what AST-pattern matchers miss. A formal inter-procedural dataflow engine tracks untrusted data across function boundaries, persistence layers, aliases, and async code.
  • Pay the model once, not on every scan. Let an agent distill a single finding into a taint rule. The deterministic engine then replays that rule across the entire codebase — and every commit after it — in minutes of CPU, at zero token cost.
  • Open source, batteries included. Engine, rules, and CI integrations come as one stack under Apache 2.0 and MIT.

Quick Start

Install script (Linux/macOS)

curl -fsSL https://opentaint.org/install.sh | bash

Install via Homebrew (Linux/macOS):

brew install --cask seqra/tap/opentaint

Install script (Windows PowerShell)

irm https://opentaint.org/install.ps1 | iex

Install via npm (Linux/macOS/Windows):

npm install -g @seqra/opentaint

Or run instantly with npx — no install required (needs Node.js):

npx @seqra/opentaint scan

Scan your project:

opentaint scan

Or use Docker:

docker run --rm -v $(pwd):/project -v $(pwd):/output \
  ghcr.io/seqra/opentaint:latest \
  opentaint scan --output /output/results.sarif /project

For more options, see Installation and Usage.


AI Agent Workflows

OpenTaint includes agent skills that turn static analysis into an end-to-end application-security workflow. Install them with:

npx skills add https://github.com/seqra/opentaint

The appsec-agent skill orchestrates a full project assessment: build the project, run OpenTaint, discover the attack surface, add targeted rules, model missing library data flows, triage findings, and optionally generate dynamic proof-of-concept checks for confirmed vulnerabilities.

Included skills cover the common security-analysis loop:

  • Scan and triage: build-project, run-scan, analyze-findings, generate-poc
  • Coverage expansion: triage-dependencies, discover-attack-surface, create-test-project, create-rule, assemble-lib-rules
  • Dataflow modeling: analyze-external-methods, create-pass-through-approximation, create-dataflow-approximation, debug-rule, report-analyzer-issue

Documentation

Full guides — installation, usage, configuration, CI/CD integration: Documentation.

Support

Star History

Star History Chart

License

The core analysis engine is released under the Apache 2.0 License. The CLI, GitHub Action, GitLab CI template, and rules are released under the MIT License.