AI Application Security
Secure your AI integration from the ground up.
We review how your applications integrate AI -- data flows to models, output handling, agent connections, RAG pipelines -- finding the vulnerabilities that live at integration points.
Overview
AI features get added to applications faster than security review keeps pace. The integration points -- data flows to models, how outputs get consumed, agent connections to production systems, RAG pipelines pulling from internal data -- are where vulnerabilities live. We perform architectural and implementation review of your AI integrations using OWASP guidance, focusing on trust boundaries and data handling rather than theoretical model risks.
What We Test
We evaluate six areas where AI integration creates application security risk.
Trust Boundary Analysis
Where your application treats AI model output as trusted input. Every boundary between your code and an AI system is a potential injection point.
Data Flow Review
What data flows to AI models and how. Are you sending customer PII to external APIs? Are model responses cached with sensitive content?
Input & Output Handling
How user inputs are preprocessed before reaching the model, and how model outputs are sanitized before reaching downstream systems or users.
API Security
Authentication and authorization for AI service endpoints. Rate limiting, token management, and access controls on model-facing APIs.
Configuration Review
Model parameters, system prompt security, temperature settings, and guardrail configurations that affect application security posture.
RAG Pipeline Security
How retrieval-augmented generation pulls from your data sources. Access controls on the corpus, injection risks in retrieved content, and scope of what the model can access.
Our Approach
We combine architectural review with targeted testing to evaluate how securely your application integrates AI components.
Architecture Review
We map your AI integration architecture: data flows, trust boundaries, authentication between components, and how model outputs are consumed.
Implementation Assessment
Code review and configuration analysis of AI integration points -- input preprocessing, output handling, API security, and credential management.
Targeted Testing
We test specific risk areas identified in the review: injection through AI intermediaries, data leakage through model responses, and authorization bypasses.
Remediation Guidance
Specific, code-level recommendations for each finding. We work with your development team to make sure fixes are practical and don't break functionality.
Common Findings
These are issues we frequently discover during ai application security engagements:
Missing Input Sanitization
HighUser inputs passed directly to AI models without preprocessing, enabling prompt injection attacks through the application's normal interface.
Unvalidated Model Output
HighAI model responses rendered in the application or passed to downstream systems without sanitization, creating XSS and injection risks.
Overly Broad RAG Access
HighRetrieval pipelines with access to data that exceeds what the user querying the system should be able to see -- breaking application-level access controls.
Weak API Authentication
MediumAI service endpoints using shared API keys, missing rate limiting, or lacking per-user authentication for model access.
Hardcoded Credentials
CriticalAPI keys for AI services embedded in client-side code, configuration files committed to repositories, or environment variables accessible to unauthorized users.
No Logging of AI Interactions
MediumModel queries and responses not logged, making it impossible to detect abuse, investigate incidents, or audit AI system behavior.
Common Questions
How is this different from AI penetration testing?
AI penetration testing attacks your AI systems adversarially -- trying to break them. AI application security review examines how your application is built around AI -- architecture, code, configuration, data flows. Pentesting proves exploitability; application review finds the structural issues that make exploitation possible.
Do you need access to our source code?
Source code access makes the review significantly more thorough. We can examine input handling, output processing, and credential management at the implementation level. Without source code, we can still review architecture, test externally, and evaluate configurations.
Can you test applications using third-party AI APIs?
Yes. Most AI application security issues live in how your code interacts with the AI service, not in the AI service itself. We focus on your integration layer -- how you send data, handle responses, manage credentials, and enforce access controls.
Other Application Testing Options
Web Application Testing
Following OWASP methodologies, we probe your web applications for injection flaws, authentication bypasses, business logic vulnerabilities, and data exposure risks.
Mobile Application Testing
Security assessment of iOS and Android applications covering client-side vulnerabilities, backend API security, data storage, and platform-specific attack vectors.
API Security Testing
Full security assessment of REST, GraphQL, and SOAP APIs covering authentication, authorization, injection vulnerabilities, and business logic flaws.
Ready to Strengthen Your Defenses?
Schedule a free consultation with our security experts to discuss your organization's needs.
Or call us directly at (445) 273-2873