boostsecurityio/smokedmeat — A CI/CD Red Team Framework for demonstrating Build Pipeline security risks.

Description and use

**Summary**
BoostSecurityIO’s **smokedmeat** is an open‑source CI/CD Red‑Team framework that lets security engineers inject AI‑enhanced attack simulations into build pipelines, exposing common pipeline‑security flaws. Written in Go and backed by 281 ★, recent commits, and a growing community, it offers ready‑to‑use APIs/SDKs/CLI for prototyping RAG or autonomous‑agent workflows without building a model stack from scratch.

**Value** – By bundling AI‑driven threat‑modeling primitives (e.g., prompt‑based exploit generation, automated credential‑spraying, and pipeline‑tampering scripts) into a single package, smokedmeat accelerates the creation of realistic red‑team scenarios and reduces the effort required to add generative‑AI capabilities to existing DevSecOps tooling.

**Practical adoption path** –

1. **Integrate** the CLI or Go SDK into your CI/CD runner (GitHub Actions, GitLab CI, Jenkins, etc.).
2. **Configure** a “smoke test” stage that calls the provided API to generate and execute AI‑crafted attack vectors against the build artefacts.
3. **Iterate** by customizing the supplied prompts or plugging in your own LLM endpoint to tailor the threat model to your stack.
4. **Monitor** results through the built‑in reporting hooks or export them to your SIEM for continuous improvement.

**Production readiness** – The project shows high readiness for pilot deployments: it has recent activity (last commit 2026‑05‑12), a healthy star/fork ratio, clear documentation, and a Go codebase that is easy to vendor. While no critical licensing or security red flags have been found, a final review of the open‑source license and maintainer responsiveness is recommended before full‑scale production use.

Problem

boostsecurityio/smokedmeat helps add AI capability without starting from a blank model stack.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Evidence

A CI/CD Red Team Framework for demonstrating Build Pipeline security risks.

Source