Test the intelligence.
Protect the application.
Understand the security risks in your AI features, from user input and retrieved data to tool permissions and real-world actions.
Let’s scope your testAn AI feature is more than a model.
The meaningful risk is what an adversarial input can cause your application to reveal or do. We test the complete system: its context, data sources, permissions, connected tools, and output handling.
We use OWASP guidance for LLM applications alongside WSTG techniques for the surrounding web application. Each finding connects observed behavior to a demonstrated security risk.
Follow the data. Test the authority.
A focused assessment, shaped around your architecture and the risks that matter.
Direct & indirect prompt injection
Adversarial instructions in user messages, retrieved documents, and other content the application treats as context.
Sensitive information exposure
Whether an AI feature can disclose protected data, credentials, or information outside the caller’s authorized access.
Retrieval & tenant boundaries
Access controls on RAG sources, tenant-aware retrieval, permission changes, and isolation of contextual data.
Tools & excessive permissions
The authority of connected agents, argument validation, approval steps, and limits on consequential actions.
Unsafe output handling
How generated content is rendered, passed to downstream systems, or used to construct application operations.
Resource & workflow abuse
Scoped scenarios around repeated operations, chained tool use, and limits intended to prevent uncontrolled consumption.
Each engagement defines the AI features, model configuration, data sources, tools, and failure conditions to test. The report records the tested configuration, scenarios, and observed results.
Every connection is
a boundary to test.
We follow an input through the application to understand where it can become an unauthorized disclosure or action.
Inputs & retrieval
Messages, documents, context
Model & orchestration
Instructions, decisions, output
Tools & permissions
Access, approvals, real actions
Validated evidence connects observed behavior to demonstrated application impact.
Adversarial scenarios. Application context.
We agree on scope, map the attack surface, test the relevant boundaries, and deliver validated findings with practical remediation guidance.
Explore our testing processA few useful
questions.
Do you test the model or the whole application?
We assess the application within the agreed scope. That includes the model’s interactions with retrieval, permissions, tools, and output processing. We can focus on particular features or boundaries where the risk is greatest.
How do you decide whether a scenario is a finding?
We agree on security failure conditions up front, such as unauthorized data access or a tool action that bypasses approval. Findings include the observed behavior, relevant context, and its demonstrated impact.
Can we test without exposing customer data?
We prefer synthetic test data and controlled accounts. During scoping, we agree on data access and handling, evidence collection, and what to do if unexpected sensitive information appears.
What happens after we change the model or prompts?
Changes can affect behavior, so important scenarios should be revisited. Retesting or a later assessment can be scoped around changed prompts, models, retrieval sources, tool permissions, and application controls.
Let’s put your product to the test.
Tell us what you’re building. We’ll help you understand what to test, where to focus, and what comes next.