AI & model security
Review AI application boundaries, data handling, prompt pathways, model integrations and output use for the scoped system.
Models · prompts · dataAI & Web3 Security
Secure the new surfaces your systems create.
Assess AI models, agent workflows, smart contracts and decentralized applications with a focus on real trust boundaries, practical attack paths and clear engineering actions.
Authorized, scoped reviewsAI and Web3 expertiseAction-focused reporting
AI × Web3 attack surfaceModel flow · tool access · on-chain execution
Trust boundaries mappedAI application layer
Web3 execution layer
Select a component to focus the assessment view
01
Input & data boundary
Trace where prompts, retrieval results and other untrusted content enter the application.
AI systemsModels, prompts and data flows
Agent workflowsTools, APIs and permissions
Web3 applicationsContracts, wallets and integrations
Actionable findingsPrioritized technical next steps
One connected service
Review the components where new risks meet existing systems.
AI and decentralized technologies introduce distinct security concerns, especially when connected to identities, APIs, user data and financial transactions. Assess the relevant components in context.
Smart contracts & dApps
Examine contract logic, authorization, upgrade paths and application integrations based on the agreed scope and chain environment.
Contracts · dApps · protocolsCross-layer integrations
Assess how agents, tools, APIs, wallets and on-chain actions interact, including where identity and authorization cross boundaries.
Agents · APIs · transactionsThreat surface
Follow trust across every connected layer.
Component-level checks matter, but the impact often depends on how components are connected. We map important flows, permissions and assumptions to understand where a weakness could cross from one layer into another.
The assessment is limited to explicitly authorized assets and agreed test boundaries. Production actions and transaction testing are planned with care.
Input and data boundaries
Review data sources, retrieval paths, prompt handling and how untrusted content reaches models or agents.
Model and tool permissions
Examine access granted to model-connected tools, APIs and services, including validation and authorization checks.
Contract and protocol logic
Assess relevant contract behavior, privileged functions, external calls and expected state transitions within scope.
Wallets, oracles and bridges
Consider key custody, transaction authorization and dependencies that carry data or value between systems.
Assessment approach
Make each test step clear and controlled.
We define what is in scope, how testing will be performed and how findings will be validated before moving from discovery to reporting.
Agree scope & boundaries
Confirm systems, environments, accounts, test windows and any excluded or sensitive actions.
Trace components & flows
Understand architecture, trust assumptions, integrations and the behavior that matters to users.
Test relevant weaknesses
Use proportionate, authorized testing to verify issues and understand realistic impact.
Prioritize next actions
Explain evidence, affected components, risk context and practical remediation options.
Engineering-focused reporting
Turn complex attack paths into clear decisions.
Findings should help teams understand what happened, why it matters and what to do next. Reporting is tailored to the agreed scope and evidence collected during the review.
Reproducible context
Document relevant conditions and affected components without exposing unnecessary sensitive data.
Risk explained
Describe likely impact and dependencies so teams can make informed remediation decisions.
Useful next steps
Offer technical guidance and follow-up validation options appropriate to the finding.
Where to begin
Focus the review on your current build.
Start with the components that are deployed, planned or most important to your users. Scope can cover one application or connected parts of a wider system.
AI-enabled products
Review applications that use hosted or embedded models, retrieval sources and generated outputs.
Product & platform teamsAgents and automation
Assess tool-connected workflows where models can invoke APIs, retrieve data or trigger approved actions.
AI engineering teamsContracts and dApps
Review smart contract logic and application flows where on-chain state or transactions are in scope.
Web3 buildersCommon questions
Set the right boundaries for emerging technology.
AI and Web3 reviews depend on system design and deployment details. These answers cover common scoping questions.
Yes, when the components are connected and included in the agreed scope. The review can trace relevant paths across models, agent tools, APIs, wallets and contracts.
No review can prove the absence of all defects or guarantee future security. Findings reflect the code, configuration, scope and evidence available during the assessment.
Test environments and permitted actions are agreed in advance. Any production or transaction-related testing requires explicit authorization and careful safety boundaries; it is not assumed as part of a review.
Useful inputs include system architecture, components and versions, relevant repositories or endpoints, test accounts, deployment details, known concerns and any excluded actions.
Your teams can use the report to plan remediation. Follow-up validation can be scoped to confirm whether specific reported issues have been addressed.
Review your AI & Web3 security
Understand where trust crosses your technology stack.
Share what you are building, which components are in scope and the security questions you need answered. We can plan a focused review around your environment.