AI Security Testing
Catch what's exploitable before your enterprise buyer's security team does.
AI-native applications introduce a new attack surface. Standard QA can validate user flows, but it often misses prompt injection, cross-user data leakage, vector database access flaws, LLM abuse paths, and AI-specific security gaps. AI Security Testing validates both the traditional application layer and the AI-specific layer enterprise buyers increasingly ask about.
Overview
Security testing for AI-native applications is not just API testing. The attack surface has changed. A traditional QA engagement may validate authentication, error handling, and regression flows, but it may not test whether the LLM can be manipulated to leak data from another user's session.
AI-native systems require testing across the full interaction layer: APIs, prompts, model outputs, vector database access, session isolation, rate limits, fallback behavior, training pipeline exposure, and cost-control failure modes.
AI Security Testing covers both layers: the standard API and application security surface your engineering team expects, and the AI-specific attack vectors your enterprise buyers are now starting to ask about directly.
The engagement is designed for teams preparing to launch, pass vendor security review, onboard enterprise customers, or validate production systems before security findings become procurement blockers.
Ideal For
- Teams launching new AI products or major AI features to production
- Companies with a vendor security questionnaire that asks about application security testing
- Applications handling sensitive user or enterprise data at scale
- APIs and backend services being integrated into enterprise customer environments
- AI-native companies that need OWASP-aligned evidence for security review
What's Included
Comprehensive services tailored to your needs.
- Authentication and authorization testing
- Input validation and injection testing
- SQL, NoSQL, and command injection checks
- Rate limiting and abuse prevention validation
- Error handling and information disclosure assessment
- OWASP Top 10 alignment across tested endpoints
- Cross-endpoint access control validation
- Direct and indirect prompt injection validation
- Output filtering and content moderation verification
- Session isolation testing for cross-user data leakage
- Rate limit and cost control validation
- Model output consistency review
- Hallucination boundary testing
- LLM timeout and fallback behavior testing
- Automated test suite development in TypeScript, Python, or Bash
- Functional regression coverage for critical user flows
- API contract testing
- CI/CD integration for automated execution on pull requests
- Coverage target of 70%+ on in-scope endpoints
- Handover of test assets and documentation
- OWASP Top 10 for Web Applications
- OWASP Top 10 for LLM Applications
- Authentication bypass attempts
- Privilege escalation testing
- Sensitive data exposure assessment
- Security misconfiguration review
- Documented findings with reproduction steps and remediation guidance
- Baseline load behavior documentation
- Performance under production-equivalent traffic
- LLM API timeout and fallback behavior under load
- Cost anomaly identification under stress conditions
- Load testing recommendations for scale-readiness
The Process
A structured approach to deliver results with clear communication at every step.
Test Planning
We review your application architecture, confirm the testing scope, establish access and authorization, define test environments, and document success criteria.
Test Design
Test cases are designed across functional flows, security scenarios, AI-specific attack vectors, prompt behavior, edge cases, and regression priorities.
Test Development
Automated test suites are built for functional and regression coverage. CI/CD integration is prepared where applicable, and manual procedures are created for security-specific scenarios.
Test Execution
Automated and manual tests are executed. Findings are documented in real time, and critical issues are communicated immediately rather than waiting for the final report.
Reporting & Handover
You receive a comprehensive findings report, remediation guidance, and all test assets including automated suites, test case documentation, and evidence artifacts.
Tools & Technologies
Industry-leading tools and frameworks I use for this service.
Pricing
Transparent pricing options to fit your needs and budget.
- Application security assessment
- Test strategy development
- Priority vulnerability identification
- Recommendations report
Best for: Teams needing a rapid security read before a launch or procurement review
- OWASP Top 10 for Web and LLM Applications
- API security testing with full endpoint coverage
- LLM interaction and prompt injection testing
- Automated test suite for core regression coverage
- CI/CD integration
- Security findings report with remediation guidance
Best for: AI-native applications preparing for enterprise customer onboarding or security review
- Everything in AI Security Package
- Full automated regression suite with 70%+ coverage target
- Performance and load testing
- Retesting after remediation
- Ongoing QA advisory for 30 days post-delivery
Best for: Companies needing complete security and QA coverage with ongoing support
All prices are starting points. Final pricing depends on specific requirements. Schedule a consultation for a custom quote.
Frequently Asked Questions
Common questions about ai security testing.
How is AI Security Testing different from the Compliance Readiness Assessment?
Can you test code or systems you did not build?
How much automation coverage is realistic?
What happens after findings are reported?
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Let's Discuss Your Build Scope
Let's discuss your requirements and find the right solution for your business.