Test what your AI system can be manipulated into doing.
ShabuShabu Security provides authorized AI and LLM security testing for AI-powered applications, assistants, agents and tool-enabled workflows, with a focus on prompt injection, permission boundaries, sensitive-data exposure and unsafe autonomous actions.
The model is only one part of the attack surface.
Modern AI products combine language models with user data, retrieval systems, APIs, external tools, account permissions and automated actions. Security failures often emerge from the interaction between those components.
ShabuShabu Security therefore evaluates the complete AI application workflow rather than treating the model as an isolated component.
Test instruction boundaries
Review whether user-controlled or retrieved content can influence behavior beyond its intended role.
Test connected capabilities
Assess what tools, APIs, files and application functions the AI can reach or invoke.
Validate security impact
Determine whether manipulated AI behavior can cross a meaningful data, permission or action boundary.
Core security areas inside AI-powered applications.
The exact scope depends on the model architecture, available tools, data sources and level of autonomy the application grants to the AI system.
Prompt Injection
Review whether untrusted instructions can override, redirect or weaken intended application behavior.
Tool Abuse
Test whether the AI can invoke connected functions in ways that exceed the intended user or application context.
Sensitive Data Exposure
Assess whether prompts, context or model-connected resources can expose information outside permitted boundaries.
Retrieval Security
Review whether retrieved content can manipulate the model, expose restricted information or introduce unsafe instructions.
Permission Boundaries
Test whether the AI inherits or bypasses user, tenant and application-level access restrictions.
Agent Actions
Assess how autonomous or semi-autonomous workflows handle actions that can change data or trigger external systems.
Context Isolation
Review whether one user, session or workflow can influence context intended for another security boundary.
External Integrations
Assess trust relationships between the model, third-party APIs, plugins, tools and application services.
AI Business Logic
Test whether model outputs can alter product workflows, approvals or decisions in unintended ways.
Every AI capability introduces a new permission question.
The important security question is not only what the model can generate, but what the surrounding application allows that generated behavior to influence.
User Input
Can untrusted prompts influence behavior beyond the user-facing interaction they were intended for?
Retrieved Content
Can documents, websites or external data become an indirect instruction channel to the model?
Application Data
Does the AI receive only the information appropriate for the current user and task?
Tools & APIs
Can the model invoke capabilities that exceed the user’s intended level of access?
Agent Autonomy
Can a sequence of model-driven decisions create an unintended high-impact action?
Output Handling
Does the application safely interpret model output before using it in downstream workflows?
What should an AI security test determine?
We test whether model behavior can be turned into a practical security failure inside the surrounding application.
Instruction & Data Security
Tools, Agents & Actions
The more an AI can do, the more carefully its authority must be controlled.
An assistant that only generates text presents a different security model from an agent that can query customer data, send messages, modify records or call external services.
For tool-enabled AI systems, we review whether authority is limited by independent application controls rather than trusting the model to decide what it should be allowed to do.
Security testing across modern AI application architectures.
AI Assistants
Conversational products connected to customer data, business workflows or private application context.
RAG Applications
AI systems retrieving information from documents, knowledge bases or external sources.
AI Agents
Autonomous or semi-autonomous systems that can plan actions and interact with application tools.
AI-Enabled SaaS
Products embedding language models inside existing web applications, APIs and customer workflows.
From AI capability mapping to verified security findings.
The assessment begins by understanding what the AI can access, which actions it can influence and which trust boundaries must remain protected.
Map AI capabilities
Identify models, prompts, tools, data sources, user roles and application actions in scope.
MAP / MODEL / TOOLS
Identify trust boundaries
Understand which instructions, data and capabilities should remain isolated from untrusted influence.
TRUST / DATA / PERMISSIONS
Red-team the workflow
Perform controlled testing against prompt handling, tool use, agent actions and data boundaries.
TEST / MANIPULATE / VALIDATE
Report & retest
Document security impact, affected workflows and remediation priorities, then verify fixes if required.
REPORT / FIX / RETEST
Findings tied to the real AI application workflow.
The report focuses on where AI behavior crosses a meaningful security boundary and what engineering controls can reduce that risk.
Executive Summary
An overview of the AI security posture and the most important weaknesses identified during testing.
Attack Scenarios
Clear descriptions of how AI behavior can be manipulated into an unintended application outcome.
Impact Analysis
An explanation of affected data, tools, users or business workflows.
Technical Evidence
Relevant prompts, system behavior and application context required to understand validated findings.
Remediation Guidance
Recommendations focused on application controls, permission boundaries and safer AI integration patterns.
Retesting
Optional verification after security controls or AI workflows have been updated.
When an AI security assessment creates the most value.
Before AI feature launch
Test new AI functionality before exposing it to users or connecting it to sensitive application data.
Before adding tools
Review permission boundaries before allowing the AI to call APIs or perform application actions.
Before adding autonomy
Test agent workflows before expanding the number or impact of actions the AI can perform independently.
After major AI changes
Reassess the product when models, system prompts, retrieval sources or AI permissions materially change.
Controlled testing for AI systems with real capabilities.
ShabuShabu Security performs AI security testing only within an approved engagement scope. Models, applications, accounts, tools, data sources and restricted actions are defined before testing begins.
Find out what your AI can be pushed beyond its intended boundaries to do.
Tell us how your AI product works, which models, tools and data sources it can access and what actions it is allowed to perform. We will structure testing around the real trust boundaries of the application.