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Application Security Testing

Find the Vulnerabilities That Actually Matter: Across Web, Mobile, API, and AI-Powered Applications

Modern applications are not what scanners were built to test. The application surface now spans web frontends, mobile clients, public and internal APIs, microservices, AI-powered features, and increasingly the agentic AI capabilities being embedded into products. Each surface has its own attack patterns, its own OWASP framework, and its own gaps that automated scanning alone will not find.


Our Application Security Testing service combines experienced manual penetration testing with AI-augmented automated testing to find the vulnerabilities that matter, from common configuration weaknesses scanners catch easily, to complex business logic flaws and authorization bypasses that require human expertise, to AI-specific vulnerabilities (prompt injection, output handling failures, model exfiltration) that traditional AppSec tooling doesn't yet cover.


Engagements are anchored in the OWASP Top 10 (web), OWASP API Security Top 10, OWASP MASVS and MASTG (mobile), OWASP LLM Top 10, NIST AI 600-1, and MITRE ATLAS, the frameworks regulators, auditors, and buyers expect to see referenced.

Our Tailored Approach Can Include
Modern Application
Surface Testing

  • Web application testing aligned to OWASP Top 10 and OWASP ASVS.
     

  • Mobile application testing for iOS and Android, aligned to OWASP MASVS and the OWASP Mobile Application Security Testing Guide (MASTG).
     

  • API security testing aligned with the OWASP API Security Top 10, covering authentication, authorization, rate limiting, business logic, and API-specific failure modes that traditional web testing misses.
     

  • AI and LLM application testing aligned to the OWASP LLM Top 10, NIST AI 600-1, and MITRE ATLAS, covering prompt injection, insecure output handling, sensitive information disclosure, training data poisoning, model denial of service, and the emerging attack patterns against AI-powered features.
     

  • Single-page application (SPA) and modern frontend testing.
     

  • Desktop application testing for Windows, macOS, and Linux platforms.
     

  • Embedded and IoT software testing where applicable.

Testing Methodologies & Manual Expertise

  • Manual penetration testing by experienced testers.
     

  • Black box, white box, and gray box approaches matched to the testing objective and the access available.
     

  • Business logic testing: the category of vulnerabilities scanners cannot find because they require understanding what the application is supposed to do.
     

  • Authentication and authorization deep-dives, including OAuth, OIDC, SAML, and modern federated identity flows.
     

  • Session management, input validation, output encoding, and error handling testing.
     

  • Compliance-aligned testing where required: PCI DSS, HIPAA, SOC 2, FedRAMP, and sector-specific requirements.
     

  • AI-aware manual testing: prompt injection, jailbreak resistance, output handling, agent tool-use abuse, and confused-deputy attacks against AI features.


     

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Test Automation, Tooling Strategy & AI-Augmented Testing

  • Vendor-neutral evaluation of automated AppSec testing tools: SAST, DAST, SCA, IAST/RASP, and emerging AI-native testing platforms.
     

  • AI-augmented testing capability: AI-powered fuzzing, intelligent test case generation, AI-assisted vulnerability research, and machine analysis of large application surfaces.
     

  • API discovery and continuous API testing strategy.
     

  • Integration with CI/CD pipelines and the broader Secure SDLC program for continuous testing rather than point-in-time engagements.
     

  • Tool consolidation and rationalization: most teams have overlapping AppSec tooling and could reduce vendor count without losing coverage.





     

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Findings, Remediation Support & Continuous Improvement

  • Detailed findings with reproducible evidence, severity assessment grounded in business impact, and clear demonstration of exploit paths.
     

  • Risk-prioritized recommendations focused on the vulnerabilities that actually matter, not exhaustive checklists.
     

  • Remediation guidance for development teams, including secure coding patterns, configuration changes, and architectural recommendations where individual fixes are not enough.
     

  • Optional retest engagements to confirm remediation and document closure for auditors and regulators.
     

  • Connection to TBDCyber's broader Secure SDLC program: turning point-in-time findings into systemic improvements in development practice.
     

  • Trend analysis across multiple engagements: where the vulnerabilities cluster, what training would prevent the most issues, and what architectural changes would shift the risk profile.

Benefits

Coverage Across the Full Modern Application Surface

Web, mobile, API, AI-powered features, and the modern client patterns your applications actually use. Aligned to the OWASP frameworks (Top 10, API, MASVS, LLM) and AI standards (NIST AI 600-1, MITRE ATLAS) buyers and regulators now expect.

Manual Expertise Where Automation Can't Replace It

Business logic flaws, complex authorization bypasses, AI prompt injection, and multi-step attack chains. The findings that matter most are usually the ones a scanner could never produce.

AI-Augmented Where It Adds Real Value

AI-powered fuzzing, intelligent test generation, and AI-assisted vulnerability research go deeper than time-bound manual testing alone. The combination of human expertise and AI augmentation produces engagements neither approach could deliver on its own.

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Vendor-Neutral Tooling Recommendations

TBDCyber doesn't sell or resell AppSec testing tools. When recommendations on SAST, DAST, SCA, or AI-native testing platforms are needed, they reflect environmental fit and total cost.

Related Services
Application Security Testing finds vulnerabilities at a point in time.
 
To address the systemic improvements those findings point to, see Secure SDLC & DevSecOps.
 
For the methodology service that drives what to test in the first place, see Threat Modeling.
 
For deeper engagements specifically on AI-powered applications and agentic AI, see Secure AI & Agentic Development.
 
For broader infrastructure, network, social engineering, and red team scope beyond the application surface, see Penetration Testing.
 
For the cloud and AI infrastructure your applications run on, see Cloud & AI Workload Security Architecture.
Your Scanners Check the OWASP Top 10. Your Application Surface Includes APIs, AI Features, and Business Logic They Were Never Built to Find.

The vulnerabilities that drive real incidents in today increasingly live in spaces traditional AppSec testing programs don't cover well: API authorization flaws, AI feature prompt injection, complex business logic abuses, and the architectural weaknesses that span multiple components. Finding them requires a combination of experienced manual testing, AI-augmented automation, and a methodology that addresses the modern application surface.


TBDCyber's Application Security Testing service combines all three. Vendor-neutral, framework-aligned, and connected to the broader Secure SDLC program that turns findings into systemic improvement.


Talk to an Application Security Testing Expert →

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