Red-team your AI against prompt injection
Red-team your AI against prompt injection with Argus AI: a practical, on-prem approach built for Azerbaijani teams.
Red-Team Your AI Against Prompt Injection
Secure your AI assistants by simulating sophisticated adversarial attacks and prompt injections. As a core component of the Argus self-hosted AI testing platform, this engine shares its runtime, model layer, credential store, and cost ledger with dedicated QA and pentest engines to provide a unified security framework. By generating thousands of realistic synthetic Azerbaijani users—each with distinct roles, goals, and behavioral patterns—the platform stress-tests your models to ensure they remain compliant and safe before they ever reach your customers. Unlike standard testing, this engine prioritizes adversarial personas as first-class citizens. It simulates high-risk interactions including frustration, contradiction, manipulation, and complex AZ↔RU code-switching, recognizing that polite users are the least likely to expose vulnerabilities. By treating the assistant as a black box reached through actual customer touchpoints, the platform identifies real-world weaknesses, producing a readiness score and write-once assurance records that provide a transparent audit trail of your AI's resilience.
Strengthen Your AI Resilience
Identify critical vulnerabilities through realistic adversarial personas that simulate prompt injection and manipulation.
Maintain total data privacy and sovereignty using a self-hosted, on-premise deployment approach.
Validate linguistic accuracy and safety using a specialized Azerbaijani-native LLM judge for tone and formality.
Prevent the re-emergence of vulnerabilities with dedicated regression suites that confirm past issues stay fixed.
Test actual customer-facing endpoints via black-box connectivity, including REST, Dify, and browser automation.
Establish a verifiable audit trail with write-once assurance records and detailed findings for every run.
Comprehensive Adversarial Testing
Synthetic User Generation
Creates thousands of realistic Azerbaijani users, each defined by a specific role, goal, language style, knowledge level, and behavior.
Adversarial Personas
Simulates high-risk behaviors including prompt injection, manipulation, contradiction, frustration, and AZ-RU code-switching.
Native LLM Judging
An Azerbaijani-native judge scores the assistant on accuracy, tone, formality, compliance, and safety.
Black-Box Connectivity
Tests what customers actually reach via connectors for REST, Dify, Kommunicate, or browser automation.
Policy-Driven Expectations
Derives expected behaviors from your uploaded knowledge and policy documents, remaining human-overridable.
The Red-Teaming Process
Frequently Asked Questions
How does the platform prevent testing from disrupting my AI service?
Per-assistant concurrency is strictly bounded to ensure that the red-teaming process does not inadvertently become a denial-of-service attack on the assistant under test.
Can the scoring of a completed test run be modified later?
No. Each run snapshots its evaluator configuration at launch. This ensures a finished run is never re-scored against a model chosen after the test was completed, maintaining the integrity of the results.
Is the readiness score a definitive gate for production release?
The readiness score is intended as a signal rather than a hard release gate, as the LLM judge does not yet have a published agreement measurement against human reviewers.
How is the engine integrated into the broader Argus ecosystem?
It is one of three specialized engines within Argus, sharing a common runtime, model layer, credential store, and cost ledger with the QA and pentest engines for operational efficiency.
How are the 'expected behaviors' for the AI determined?
Expected behaviors are derived from your uploaded knowledge and policy documents. These derived expectations act as proposals and remain fully human-overridable rather than serving as final verdicts.
Secure Your AI Deployment
Start red-teaming your assistants today to identify vulnerabilities before your users do.
Request a demo