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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteSentinel RED is a self-hosted suite that runs automated security and quality tests against an LLM application. You point it at an API or local model target, choose test modules and depth, and it reports results in a web dashboard and as a PDF. It covers prompt injection, hallucination, data leakage, adversarial robustness, data poisoning, and policy compliance, according to its own published scope. It is not the AWS sample harness or an unrelated agent action-gate that also use the Sentinel name.
Two questions come up most often. To red-team an LLM app with Sentinel RED, you configure a target and run the modules against it. To run these tests in CI, the public materials describe a REST API that creates runs, lets you poll their status, and downloads reports, so a pipeline job is buildable. The sources do not document a ready-made CI integration, so your team would write that glue itself.
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What Sentinel RED tests
The product homepage at https://sentinelred.dev/ lists six areas. The linked repository describes a similar set. The table below pairs each area with the example tests the vendor names. These describe the vendor’s stated capabilities, not a measured detection rate.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Area | Examples the vendor names |
|---|---|
| Prompt injection | Direct and indirect injection, multi-turn escalation, encoding tricks |
| Hallucinations | Known-answer QA, citation checks |
| Data leakage | PII recall probes, credential leakage |
| Adversarial testing | Jailbreak fuzzing, tool-use abuse |
| Poisoning | Trigger probes |
| Compliance | Policy validation |
The homepage states a count of 6 modules and 85+ attack patterns, labelled as 2026 site claims. Its example live-console display refers to an 86-pattern attack library and shows sample module scores. Those scores illustrate the interface. They are not results from a run you can reproduce, and they are not an independent inventory.
#1 Best Overall
How a test run works
The published workflow has five stages. The sources describe them in this order, but they do not publish exact menu labels, so confirm the current wording in the live dashboard.
- Configure a target: an API endpoint or a local model. Model provider credentials can be supplied through environment variables or dashboard settings.
- Select test modules and a depth setting.
- Run the suite.
- Inspect results as they stream in.
- Generate the PDF report.
Installing Sentinel RED locally
The installation guide at https://sentinelred.dev/install recommends cloning the GitHub repository and starting the stack with Docker Compose. Clone from the exact repository URL given there, https://github.com/NenXMaster-AB/sentinel. The README’s clone example uses a generic organization name and should not be copied as-is. After startup, the dashboard is available at localhost:3000. The install page also describes a smoke-test sequence; run it before launching a full suite against a production-like target.
Rank #2
The repository README lists the components the stack is built from:
- Backend: Python 3.12+, FastAPI, SQLAlchemy, Celery
- Data: PostgreSQL 16 with TimescaleDB, Redis 7
- Frontend: React 18, TypeScript, Vite, Tailwind
Those versions are as documented in the repository. Check the current branch before following version-sensitive steps.
Using the REST API for scheduled or pipeline runs
The install page documents a REST API with three capabilities that matter for automation:
- Create a run against a configured target.
- Poll a run for its status until it finishes.
- Download the PDF report for a completed run.
The sources reviewed do not describe the API’s authentication method. Confirm how access is controlled before exposing the service beyond localhost, particularly if the same instance holds provider credentials for production models.
How Sentinel RED compares with Promptfoo and PyRIT
The Sentinel RED comparison page at https://sentinelred.dev/compare states: “There’s no single ‘best’ tool.” The page is written by the Sentinel RED publisher and describes its own product against two alternatives. It is not an independent head-to-head test.
| Tool | Positioning on the Sentinel RED comparison page | Interface style | License |
|---|---|---|---|
| Sentinel RED | Unified suite with opinionated modules, common scoring, and reporting | Dashboard and REST API, self-hosted via Docker Compose | AGPL-3.0, per the repository README |
| Promptfoo | Repeatable prompt, model, and RAG evaluation; CI regression | Not stated in the reviewed sources | Not stated in the reviewed sources |
| PyRIT | Programmable framework for custom security-research workflows | Programmable framework | Not stated in the reviewed sources |
Choosing among these tools comes down to five questions:
- Is the goal ongoing CI regression testing, or a broader red-team campaign?
- Does the team want an opinionated UI and reports, or a programmable framework?
- How easily can you add custom attacks and adapters for your application?
- Is deployment local or hosted, and how are secrets handled?
- How do the license, maintenance activity, and evidence quality fit your organization?
Licensing
The repository is licensed under AGPL-3.0. Using Sentinel RED internally for your own testing is a different situation from offering a modified version to others over a network. The AGPL requires that users interacting with a modified version over a network be offered its source code. Have counsel review the terms against your intended use, especially if you plan to modify the code or embed it in a customer-facing service.
What the available evidence establishes
The official changelog at https://sentinelred.dev/changelog lists two dated entries: an “Internal JSX prototype” on 2026-02-01, and “Landing page + product positioning” on 2026-02-13. Those entries do not establish a release cadence, external customers, production deployments, or independently verified detection performance.
No third-party evaluation of the product’s scores, coverage, attack-library size, or production readiness was found. The public GitHub repository was small at the time of review, with one star. Star counts change and do not indicate quality in either direction. Treat the vendor’s counts and sample dashboards as product claims, and validate detection against your own application before relying on any result.
Security context
Sentinel RED links to the OWASP Top 10 for Large Language Model Applications at https://owasp.org/projects/top-10-for-large-language-model-applications. The project page explains that the work now sits within the broader OWASP GenAI Security Project and points to the latest Top 10. Use OWASP as a risk taxonomy when planning coverage, and check the current list before mapping test modules to categories. Sentinel RED is not described as OWASP-certified, and the sources do not show that it implements the full Top 10.
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