Red Teaming

Our red teaming process subjects AI models and AI-powered applications to structured adversarial testing. We investigate how systems respond when exposed to manipulated instructions, unexpected inputs, conflicting objectives, and attempts to circumvent their intended controls.

Our testing research includes:

• Jailbreak Testing — evaluating resistance to attempts to circumvent model safeguards.

• Prompt Injection — assessing the influence of malicious or conflicting instructions.

• Instruction Manipulation — testing how changes in context and instruction hierarchy affect model behavior.

• Guardrail Evaluation — examining the effectiveness and consistency of safety and security controls.

• Adversarial Inputs — testing model responses against deliberately constructed inputs.

• Information Exposure — evaluating potential unintended disclosure of information.

• Behavioral Testing — identifying unexpected or inconsistent model behavior.

Our objective is to establish how a system behaves under adversarial pressure and identify conditions that may produce security-relevant outcomes.

AI red teaming extends beyond attempting to bypass safeguards. Understanding the capabilities and behavioral boundaries of an AI system is essential to evaluating its overall security.

We examine what systems can perform under different contexts and how their capabilities interact with their safeguards, instructions, tools, integrations, and surrounding applications.

Our proprietary AI modules assist in exploring diverse scenarios and analyzing resulting behaviors. This allows researchers to investigate:

• Capability boundaries

• Unexpected model behaviors

• Safety-control effectiveness

• Agent and tool interactions

• AI application behavior

• Model and system-level dependencies

• Security-relevant failure modes

This evaluation provides a deeper understanding of how an AI system behaves beyond its intended or documented use cases.

AI systems are dynamic. Models are updated, prompts change, new capabilities are introduced, and applications increasingly connect AI with external tools and services. Each change can alter system behavior and introduce new areas requiring evaluation.

For this reason, RudraTech approaches AI red teaming as a continuous testing process rather than a one-time assessment.

Our methodology follows a structured cycle:

Challenge → Discover → Analyze → Validate → Strengthen → Re-test

Vapor, RudraTech's AI red teaming platform, supports this process through proprietary AI-driven testing and analysis capabilities. It enables researchers to conduct repeatable adversarial evaluations, investigate findings, and re-test systems after security improvements.

The goal is to establish an ongoing feedback loop between AI development, adversarial research, and security improvement.

Our Approach

RudraTech's AI red teaming methodology is built around three principles:

Explore broadly.

Examine AI systems across diverse inputs, contexts, interactions, and operating conditions.

Analyze deeply.

Investigate the behavior behind significant findings rather than treating individual outputs as isolated results.

Improve continuously.

Use validated findings to strengthen AI systems and re-evaluate them as they evolve.

We use AI-driven research to expand the depth and scale of adversarial testing while maintaining a structured process for analyzing and validating meaningful findings.

Built to Challenge Intelligent Systems

As artificial intelligence becomes increasingly capable and integrated into real-world applications, understanding its behavior under adversarial conditions becomes increasingly important.

RudraTech's AI red teaming research is designed to identify weaknesses before they become operational risks, providing organizations with a clearer understanding of how their AI systems behave when deliberately challenged.

Our work focuses on one fundamental principle:

An AI system should be tested not only for what it is designed to do, but also for how it behaves when pushed beyond its intended boundaries