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product-updates • 8 min read

How Does dwaar Red Teaming Work?

By Ankush • June 29, 2026

How Does dwaar Red Teaming Work?

AI red teaming is not the same as sending a few jailbreak prompts to a chatbot.

For agentic applications, red teaming needs to test the full system: the model, prompts, context, retrieval pipeline, tools, permissions, guardrails, and workflows around the agent.

That is what dwaar red teaming is built for.

dwaar helps teams understand how their AI agents can be manipulated before those agents are deployed into real business workflows. The goal is not just to find whether an agent says something unsafe. The goal is to understand whether the agent can be influenced into revealing sensitive information, misusing tools, bypassing policy, making unsafe recommendations, or triggering risky actions.

In simple terms:

dwaar red teaming tests how an AI agent behaves under adversarial pressure.

And because agentic applications are connected to tools, data, and workflows, this testing has to go beyond traditional security checks.

What Is AI Red Teaming?

AI red teaming is the process of testing an AI system with adversarial inputs to identify where it fails.

In a basic LLM, this may involve testing whether the model can be made to produce harmful, restricted, or policy-violating outputs.

But in an agentic application, the risk is broader.

An agent may be able to:

So the red team needs to ask deeper questions:

What can this agent access? What can this agent be manipulated into believing? What can this agent be manipulated into revealing? What tools can it misuse? Where do the guardrails fail? Can the same vulnerability be reproduced?

That is the difference between casual jailbreak testing and structured agentic red teaming.

How dwaar Red Teaming Works

At dwaar, red teaming follows a structured process. The aim is to move from surface-level testing to reproducible security evidence.

1. Connect the Agent and Understand Its Context

The first step is to understand the agent being tested.

dwaar starts by connecting to the agent endpoint and gathering application context. This may include the agent’s purpose, intended users, system instructions, tools, workflows, data sources, and business domain.

This matters because no two agents have the same risk profile.

A customer support agent has different risks from a coding agent. A healthcare assistant has different risks from a sales agent. A procurement agent has different risks from an internal knowledge bot.

Good red teaming is not generic. It is context-aware.

For example, testing a finance agent should include risks around approval workflows, invoice manipulation, vendor data, payment recommendations, and sensitive financial information.

Testing a coding agent should include risks around insecure code generation, repository access, secret exposure, sandbox escape, and CI/CD misuse.

The context determines the attack plan.

2. Map the Agentic Attack Surface

Once the context is understood, the next step is to map the attack surface.

In traditional applications, the attack surface may include APIs, forms, headers, files, parameters, roles, and permissions.

In agentic applications, the attack surface is wider.

dwaar looks at:

This step is important because agentic vulnerabilities often appear between components.

The model may be safe in isolation. The tool may be secure in isolation. The workflow may look reasonable in isolation.

But when the agent, context, tools, and permissions interact, new risks appear.

That is what red teaming needs to uncover.

3. Generate a Contextual Attack Plan

dwaar then creates a red team plan based on the agent’s function, industry, data access, and tool permissions.

This is where agentic red teaming becomes different from generic prompt testing.

The attack plan may include tests for:

The point is not to run the same list of prompts against every agent.

The point is to test the risks that matter for that specific agent.

A support agent should be tested against refund manipulation, customer data exposure, and escalation abuse.

A coding agent should be tested against malicious issue descriptions, insecure patch generation, secret leakage, and unsafe tool calls.

An internal knowledge bot should be tested against document leakage, prompt injection through retrieved files, and access-boundary bypass.

This is why context matters.

4. Run Adversarial Attacks Across Multiple Delivery Methods

Real attackers do not always attack through obvious prompts.

They may hide instructions inside documents, tickets, logs, webpages, tool outputs, files, encoded text, or previous conversation context.

That is why dwaar tests across different payload delivery techniques.

A direct prompt may ask the agent to break policy.

An indirect prompt may place malicious instructions inside a document the agent later reads.

A multi-turn attack may slowly move the agent toward an unsafe action over several messages.

An encoded prompt may test whether guardrails only catch surface-level keywords.

A tool-output attack may test whether the agent blindly trusts information returned by a connected system.

This matters because many AI defenses look strong against obvious prompts but fail against layered attacks.

The question is not only:

Can the agent refuse a bad request?

The better question is:

Can the agent stay safe when the bad request is hidden inside the workflow?

5. Evaluate the Agent’s Response and Tool Behavior

dwaar does not only look at whether the agent gave a bad answer.

It evaluates the full behavior of the agent.

That includes:

This is important because agentic failures are often subtle.

An agent may not directly leak a database.

But it may summarize restricted information.

It may not directly run a dangerous action.

But it may recommend that a human or tool performs one.

It may not reveal a full system prompt.

But it may expose enough internal policy detail to help an attacker adapt.

dwaar’s red teaming looks at these grey areas because that is where many real agentic risks live.

6. Score Findings by Risk and Impact

Not every failed test has the same severity.

A harmless topic deviation is different from customer data leakage.

A single weak refusal is different from an agent misusing a high-risk tool.

A good red team report should help teams prioritize what to fix first.

dwaar scores findings based on factors such as:

This turns red teaming from a collection of interesting prompts into a security decision-making process.

The outcome should help product, engineering, security, and compliance teams understand where the real risk sits.

7. Map Findings to Security and Governance Frameworks

AI security findings should not exist in isolation.

Teams often need to understand how vulnerabilities map to known frameworks and standards.

dwaar helps connect red team findings to frameworks such as OWASP, MITRE ATLAS, NIST AI RMF, the EU AI Act, GDPR, and other relevant governance expectations.

This is useful for three reasons.

First, it gives security teams a common language to discuss AI risks.

Second, it helps compliance and governance teams understand why a finding matters.

Third, it gives product teams clearer remediation priorities.

For example, a prompt injection issue is not just “the model failed.” It may connect to broader concerns around unauthorized instruction following, data leakage, tool misuse, or insufficient human oversight.

Framework mapping makes the risk easier to communicate.

8. Provide Reproducible Evidence and Remediation Guidance

The most useful red team finding is not vague.

It should show:

dwaar’s goal is to make AI security findings reproducible and actionable.

This is especially important because AI failures can be probabilistic and context-dependent. If a team cannot reproduce the issue, it becomes difficult to fix.

A strong red team report should create engineering evidence, not just concern.

9. Convert Findings Into Stronger Policies

Red teaming should not stop at detection.

Once a weakness is identified, the next step is to create stronger controls.

A finding may lead to:

This is where dwaar’s approach connects red teaming to runtime protection.

The goal is not only to know how the agent breaks.

The goal is to reduce the chance that it breaks the same way again.

Why dwaar Red Teaming Is Different

dwaar red teaming is built specifically for agentic applications.

That means it does not only test the model.

It tests the full system around the model.

dwaar evaluates prompts, context, retrieval systems, tools, MCP servers, guardrails, policies, access boundaries, and workflows.

This matters because agentic failures rarely happen in one place.

They happen when the agent reads the wrong context, trusts the wrong input, calls the wrong tool, bypasses the wrong guardrail, or recommends the wrong action inside a trusted workflow.

That is why AI red teaming needs to be contextual, adversarial, reproducible, and connected to remediation.

As AI agents move into software delivery, customer support, finance, healthcare, sales, HR, procurement, and internal operations, red teaming becomes essential.

Not because every AI agent is unsafe.

But because every AI agent creates a new decision boundary.

dwaar red teaming helps teams understand that boundary before attackers do.

It answers the questions that matter:

What can this agent access? What can it be manipulated into doing? Where do the guardrails fail? Which workflows are exposed? Can the issue be reproduced and fixed?

That is how AI security moves from guesswork to evidence.

And that is how teams can ship agentic applications with more confidence.