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.
Secure Your AI Against Prompt Injection
Protect your AI assistants from adversarial attacks and prompt injection using Argus, a comprehensive self-hosted testing platform. By simulating thousands of realistic Azerbaijani synthetic users—each equipped with a unique role, goal, language style, and knowledge level—Argus identifies critical vulnerabilities in your system's safety and compliance before they ever reach your customers. As one of the three core engines of the Argus ecosystem, this red-teaming capability is integrated into a single console alongside QA and pentest engines, allowing you to manage your entire security posture through one sign-in and a unified settings menu for judge model selection. Unlike standard testing, Argus treats adversarial personas as first-class citizens, specifically targeting the edge cases that break assistants, such as user frustration, contradiction, manipulation, and complex AZ→RU code-switching. By utilizing an Azerbaijani-native LLM judge to score accuracy, tone, formality, and safety, the platform provides a rigorous evaluation of how your AI handles the unpredictable nature of real-world interactions. This ensures that your assistant is not just functional for the polite user, but resilient against the most challenging adversarial behaviors.
Strengthen Your AI Defenses
Detect prompt injection and manipulation vulnerabilities using specialized adversarial personas.
Validate performance against realistic Azerbaijani-native user behaviors and linguistic nuances.
Ensure strict compliance with internal policy and knowledge documents through derived expectations.
Maintain total data sovereignty and security with a self-hosted, on-premise deployment.
Eliminate recurring vulnerabilities using dedicated regression suites that confirm past fixes.
Verify actual customer experience by testing through real-world connectors like REST, Dify, or browser automation.
Advanced Red-Teaming Capabilities
Adversarial Personas
Simulates users exhibiting frustration, contradiction, and AZ→RU code-switching to find edge cases that polite users would not trigger.
Native LLM Judge
An Azerbaijani-native model scores the assistant on accuracy, tone, formality, compliance, and safety.
Black-Box Testing
Connects via REST, Dify, Kommunicate, or browser automation to test exactly what the end customer can reach.
Policy-Driven Expectations
Derives expected behaviors from your uploaded knowledge and policy documents, while remaining human-overridable.
Unified Console
One sign-in provides access to the red-teaming engine alongside QA and pentest engines.
The Red-Teaming Process
Frequently Asked Questions
How does the system handle Azerbaijani language nuances?
The platform generates synthetic users with specific Azerbaijani styles and knowledge levels, and utilizes an Azerbaijani-native LLM judge to accurately score tone, formality, and compliance.
Will the testing process crash my AI assistant?
No. Per-assistant concurrency is strictly bounded to ensure that the testing process does not inadvertently become a denial-of-service attack on your system.
Is the readiness score a definitive release gate?
The readiness score is intended as a signal rather than a hard release gate, as the judge does not yet have a published agreement measurement against human reviewers.
Can I change the judge model after a test is finished?
No. Each run snapshots its evaluator configuration at launch, ensuring that a finished run is never re-scored against a model chosen after the test was completed.
How are the 'expected behaviors' determined during testing?
Expected behaviors are derived from your uploaded knowledge and policy documents. These derived expectations are treated as proposals and remain fully human-overridable.
Ready to secure your AI?
Deploy Argus on-prem and start red-teaming your assistants against prompt injection today.
Request a demo