Prove your AI agent can resist real prompt-injection attacks

Connect an OpenAI-compatible test endpoint, define the security objective, and invite authorized adversarial testing. When a valid attack succeeds, you receive reproducible evidence to investigate, fix, and retest the issue.

Use your existing model and endpoint. Define the scope. Control the bounty.

Your AI agent changes. Its attack surface changes with it.

A prompt update, model change, retrieval source, or tool integration can create a new path around an important rule. One-time testing cannot show how your live AI workflow responds to new adversarial techniques.

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• how it works

Turn a security objective into a live adversarial test

Set the rule that must hold. Red Sentinel gives authorized researchers a defined target and gives your team evidence you can act on.

01

Connect a test endpoint

Use your existing OpenAI-compatible endpoint, model, and test configuration.

02

Define the security objective

State what the agent must not reveal, bypass, or do. Set the permitted scope and success condition.

03

Set the bounty and launch

Fund the test to reward valid findings. Researchers attack only within your defined rules.

04

Review, fix, and retest

Receive the attack trace and result. Improve the workflow, then run the test again.

• Built for your current stack

Connect without replacing your AI system.

Red Sentinel supports OpenAI-compatible endpoints. Point a Sentinel at the test environment you already operate, validate the connection, and define the security objective for that workflow.

  • Custom endpoint URL, API key, and model name
  • Your selected system instructions and test configuration
  • Conversational and tool-using Sentinel modes
  • Defined objectives, attack rules, and bounty limits

Use a staging or sandbox endpoint. Do not connect production systems with live customer data or irreversible tool access.

• why us

Evidence your security and engineering teams can use.

Red Sentinel helps you find, reproduce, and retest AI security failures against the workflows that matter to your product.

Test the Workflow You Actually Run

Evaluate the endpoint, model, prompts, and agent behavior you choose. Do not rely only on generic benchmarks.

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Control the Scope

Choose the target, allowed actions, bounty, rate limits, and test duration for each Sentinel.

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Get Reproducible Attack Evidence

A valid break records the attack sequence, the security objective that failed, and the result your team needs to investigate.
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• Watch Demo

See a Security Sentinel in Action

See how a team defines a security objective, launches a controlled test, and reviews the evidence from a valid adversarial break.

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Mainnet Demo

Connect

Connect a test endpoint and validate the configuration

Define

Set the security objective and permitted test scope

Review

Review results, fix the issue, and retest

• For authorized AI security researchers

Find real AI security failures. Earn for validated results.

Browse public Sentinels, study the stated objective, and attempt an authorized break. Successful, verified results earn the published bounty and create a public record of your research.

Learn about AI Red Teaming

Test Public Sentinels

Review the objective and rules for an available Sentinel. Find a valid prompt-injection or jailbreak path within the authorized test scope.

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Launch a Security Test

AI teams can connect a test endpoint, define a protected objective, and reward researchers for validated security findings.

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Smart-Contract Security

Review the protocol and settlement layer

Red Sentinel uses smart contracts for bounty handling and outcome settlement. Review the available audit report and protocol documentation before participating.

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Test the AI workflow that matters most

Start with one high-risk objective: protect a system instruction, enforce a data boundary, or prevent an unauthorized tool action. Run a controlled Sentinel and see what adversaries can actually break.