Agentic Security Testing for Web Apps and APIs

• CTO or VP Engineering
• Head of Security
• AppSec Lead
• Penetration Testing Service Provider

SaaS teams ship web and API changes every week, but a full manual pentest happens once or twice a year. Scanners run more often but produce long lists of unverified issues and miss business-logic flaws, such as a user approving their own expense claim or reading another account's invoice by changing an ID.

We build and run an agentic testing workflow for your web application and APIs. A supervisor agent plans the engagement and directs specialist agents: one discovers endpoints from crawling, OpenAPI or GraphQL schemas and traffic captures; another maps roles and the attack surface; others test for OWASP Top 10 and OWASP API Security Top 10 risks, such as broken object-level authorisation, and work through business-logic flows. Every candidate issue is confirmed live in a controlled way, so only real issues are reported, with the requests and responses that prove them. A human tester reviews findings, looks for chains across issues and rates severity. Findings can become draft fix pull requests for developer review.

• Endpoint discovery from crawling, OpenAPI or GraphQL schemas and traffic captures
• Role and permission matrix testing across user types and tenants
• OWASP Top 10 and OWASP API Security Top 10 coverage
• Business-logic testing of workflows such as approvals, refunds and invitations
• Live confirmation of each candidate issue with request and response evidence
• Tester-reviewed findings, with optional draft fix pull requests

1. 01Agree scope and accountsWe agree targets, environments, test accounts per role, rate limits and excluded actions.
2. 02Discover and mapAgents discover endpoints and build a map of roles, objects and workflows.
3. 03Plan and testThe supervisor assigns specialist agents to test each area against OWASP and business-logic cases.
4. 04Confirm liveEach candidate issue is reproduced safely, and unconfirmed issues are dropped.
5. 05Tester reviewA human tester reviews confirmed issues, looks for chains and rates severity.
6. 06Report and fixFindings go to your tracker with evidence, and draft fix pull requests are prepared where useful.

1. Discovery
   • Crawler and schema importer
   • Traffic capture import
   • Role and object map
2. Orchestration
   • Supervisor agent with test plan
   • Specialist agents for authentication, access control, injection and logic
   • Tool layer for HTTP requests and browser automation
3. Confirmation
   • Live reproduction under a safe-testing policy
   • Evidence capture of requests and responses
4. Review
   • Tester console
   • Severity and chain analysis
5. Delivery
   • Issue tracker integration
   • Draft fix pull requests
   • Retest scheduler

A human tester reviews every confirmed finding, looks for chains and sets severity. Developers review and merge any draft fix pull request; nothing changes in your code without their approval.

• Staging environment and test accounts
• OpenAPI or GraphQL schemas
• Issue trackers such as Jira, Linear or GitHub Issues
• Source repository for draft fix pull requests
• CI for scheduled retests

• Agreed scope, exclusions and rate limits enforced by the tool layer
• No destructive actions; state-changing tests only on test accounts
• Every reported issue confirmed live before it is reported
• All agent actions logged for audit
• Stop control available to your team throughout

• Testing records support customer due diligence and audits against frameworks such as ISO/IEC 27001 and SOC 2, where regular pentesting is expected.
• Findings map to the OWASP Top 10 and OWASP API Security Top 10.

• SaaS & AI Product Companies

  LLM Application Penetration Test (OWASP LLM Top 10)

  A scoped pentest of your LLM feature and the app around it: prompt injection, data disclosure, output handling, excessive agency and system-prompt leakage, with reproducible evidence.

• SaaS & AI Product Companies

  AI Agent Red-Teaming (Tool Misuse and Goal Hijack)

  Red-team your AI agents for goal hijacking, tool misuse, excessive agency, privilege abuse and memory poisoning, in a sandbox that records every tool call and decision.

• SaaS & AI Product Companies

  Continuous AI Security Regression in CI

  Run versioned prompt-injection, jailbreak and data-leakage suites in CI on every prompt, model, tool or index change, and block releases that regress past agreed severity thresholds.

• Fintech & Payments

  Pre-Launch Security Assessment for LLM Features

  Test a new chatbot, copilot or LLM feature for prompt injection, cross-customer data leakage, off-policy answers and unsafe output handling before release, with evidence and a retest.