nuguard
AI-SBOM generation, static analysis, and automated red-teaming / adversarial-attack-generation for AI agents and applications.
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What it does · See it in action · Framework coverage · Comparison · Getting started · Documentation · FAQ
NuGuard is an open source AI application safety & security toolkit. It generates an AI Software Bill of Materials (AI-SBOM) for your agentic application, statically analyzes it for structural risk in the AI Stack and the software infrastructure. It then red-teams a sandboxed instance with a catalog of 100+ adversarial scenarios — prompt injection, tool abuse, data exfiltration, and more — so you find the issues before an attacker does. An automated judge evaluates the findings based on their impact and provides actionable remediation guidance.
What It Does
See It In Action
A real scan of a live fintech agent — Pinnacle Bank Assistant — walking through all five NuGuard stages: AI-SBOM, Cognitive Policy, Static Analysis, Behavior, and Red-Team. No mocks, no slides — real findings, including a live transcript of the agent leaking another customer's flagged fraud transactions on a routine question.
→ Open the interactive demo — scroll through the full walkthrough yourself.
Framework Coverage
NuGuard uses framework-aware adapters and structured configuration parsers to map the AI application stack into one evidence-backed AI-SBOM.
| Area | High-level coverage |
|---|---|
| Languages | Python, TypeScript/JavaScript, Go, C#, and Java, plus structured configuration and infrastructure formats |
| AI and agentic stack | Major agent frameworks, model-provider SDKs, MCP servers/clients, prompts, tools, guardrails, auth, and API endpoints |
| Low-code / no-code | n8n, Langflow, Flowise, Microsoft Copilot Studio, and Sparkflows exports |
| Cloud and delivery | AWS, Azure, Google Cloud, Kubernetes, Helm, Docker, Terraform, CloudFormation, Bicep, Deployment Manager, and GitHub Actions |
| Data layer | Relational, document, key-value, vector, search, warehouse, and object-storage technologies |
See the Supported Technologies documentation for the detailed language, framework, SDK, platform, Kubernetes, and datastore matrix.
Comparison
Getting Started
Install, then generate an SBOM, statically analyze it, and red-team a live target:
pip install nuguard
nuguard init --target <your-app-url>
nuguard sbom generate --source <path-to-your-app> --output app.sbom.json
nuguard analyze --sbom app.sbom.json --format markdown
nuguard redteam --config nuguard.yaml --format markdown --output reports/redteam.md
🚀 Ready to run NuGuard?
Installation, the full CLI surface, and the configuration reference — all in one guide.
🤖 Using Claude Code?
Install the NuGuard plugin and run SBOM, analysis, behavior, and red-team scans directly from Claude Code or Claude Desktop.
🎯 Need to pentest a live target?
Nuclei-backed, bounded conventional pentest — explicit authorization required.
Hosted Version
Running NuGuard at organizational scale? The managed SaaS adds what a CISO or VP Engineering needs on top of everything in this repo — no infra to stand up or maintain.
|
NuGuard.ai — Managed SaaS for Security & Engineering Leaders
Free trial available — no credit card required. |
Contributing
🤝 Want to contribute?
Dev setup, running tests and lint, and the pull request process are covered in the Contributing guide.
Release publication is managed by repository maintainers. See Governance for ownership and the release runbook for the maintained procedure.
Repo Notes
- The repository currently contains example applications under
tests/apps/ - LLM-assisted features depend on provider credentials being available via environment variables
FAQ
I have some questions, how do I reach out to folks behind this repo? You can contact us at oss@nuguard.ai For bug reporting, use the issues page on GitHub.
Do I need a live app to do a security assessment?
No. nuguard sbom + nuguard analyze find structural and supply-chain risks based on the SBOM (no source code or running application required).
nuguard behavior and nuguard redteam need a running target typically in a sandbox.
Which LLM providers are supported for LLM-assisted features?
Configured via the llm section of nuguard.yaml; provider credentials are read from environment variables. Lite LLM is used to abstract any llm provider.
For redteam scenarios, an LLM with adversarial content tolerance is required to generate attack payloads effectively. The SaaS version of nuguard provides a pre-configured LLM for this purpose, ensuring consistent and reliable red-team assessments.
What if I don't want to run all redteam scenarios?
Filter by profile (CI, Standard, Full), and configure destructive or non-destructive scenarios. Alternatively, set enabled: false per scenario in a catalog exported with nuguard redteam catalog-export.
License
Docs: Published documentation · Getting started / Quick start · Supported technologies · CLI reference · Policy engine · Static analysis · Red-team Guide · Cloud Pentesting · Claude plugin · Troubleshooting · Security · Contributing · Governance
1 maintainer
Metadata
1 maintainer
Release files for nuguard 0.9.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nuguard-0.9.9.tar.gz | 1.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nuguard-0.9.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.9 MB
Release files / nuguard-0.9.9.tar.gz
| Download URL | nuguard-0.9.9.tar.gz |
|---|---|
| Size | 1.8 MB |
| Tags | Source |
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Yes |
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Transparency logRelease files / nuguard-0.9.9-py3-none-any.whl
| Download URL | nuguard-0.9.9-py3-none-any.whl |
|---|---|
| Size | 2.1 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 27, 2026.
Transparency log
