Attack models and agents before you trust them.

Traditional application security asks whether code contains a vulnerability. AI security usually asks something different:

The risk is not simply that the model produces an unusual answer. The risk is what the surrounding system allows that answer to become.

Direct prompt injection happens when a user gives the model instructions intended to override or circumvent the application's controls. RedMaw tests whether the system can be pushed to ignore higher-priority instructions, disclose restricted information, reveal hidden behavior, bypass application-level controls, or enter states the product is not intended to expose.

The important result is not “the model responded strangely.” It is whether the attack creates a material security outcome.

Indirect injection moves the attack into content the AI consumes: a webpage, a document, an email, a support ticket, retrieved knowledge, or another untrusted source.

The human may never see an obvious attack. The AI reads the content and treats part of it as an instruction. This matters more as AI systems are connected to search, retrieval, business applications and tools.

Hidden instructions and protected context are part of the attack surface. RedMaw tests whether an AI application can be manipulated into revealing system-prompt content, hidden instructions, sensitive contextual data, or content that should remain outside the user's privilege. Disclosure is evaluated as a security behavior, not a quality issue.

A control is only useful if it holds when someone actively tries to defeat it. The objective is not to collect entertaining jailbreak examples. It is to determine which bypasses produce meaningful security consequences in the deployed system.

Fine-tuning, retraining, prompt changes, model upgrades and safety-policy changes can all alter behavior. A model that passed security testing last month can regress after an update.

RedMaw can run AI red-teaming in the CI or release pipeline and compare the changed system against the last accepted security baseline, turning adversarial testing into a release gate rather than a one-time exercise.

The highest-consequence AI risk appears when a model can do more than generate text: when it reaches APIs, support systems, developer tools, internal knowledge, production data or business actions.

That creates the canonical path this surface is built around.

Adversarial AI testing should be repeatable, scoped and tied to security outcomes. A useful program needs:

The value comes from turning red-teaming behavior into an operational security loop.

Validated findings enter the RedMaw findings-state layer. They can be pushed to Jira, GitHub Issues, Slack or email with proof and remediation guidance attached.

After the fix, RedMaw re-tests the issue. The finding closes when the exploit no longer works.