Services
Insecure Lab is written by the engineers at Pashyaa Technologies. These are the engagements we take on: fixed scope, fixed price, agreed in writing before work begins.
Pentest reports don't fix themselves
Security firms deliver a findings PDF and leave. Someone still has to fix each item inside a Spring Boot codebase without breaking production — and that person is hard to find. We do the fixing, not the testing.
What we don't do: we don't run penetration tests, offensive engagements, or attack simulations. We remediate findings, review code, and design secure integrations. If you need testing, we'll tell you and you should hire a specialist for it.
Security Audit Remediation
You have a findings report from an auditor or a client security review. We fix the findings in your codebase without breaking production.
Scope: Java and Spring Boot applications, and the infrastructure configuration around them.
What you get
- Each finding triaged, with severity and exploitability assessed against your actual codebase
- Fixes implemented and delivered as reviewable pull requests
- Regression tests covering the fixed paths
- A remediation report you can return to the auditor or client
- A short handover call covering what changed and why
Java/Spring Boot Secure Code Review
Your team ships faster with AI assistance. We check what it actually does with your customer data before it reaches production.
Scope: Application code, including AI-generated and AI-assisted code, plus dependency and configuration review.
What you get
- Line-referenced findings with severity and reproduction notes
- Explicit review of AI-generated and AI-assisted code paths
- Authentication, authorisation, and data-handling review
- Dependency and secrets-handling review
- Prioritised remediation plan you can hand to your own team
LLM/RAG Integration Security Design Review
Retrieval systems leak in ways ordinary application reviews miss. We review the design before the data does something you cannot undo.
Scope: LLM and RAG features inside existing applications: retrieval boundaries, prompt handling, tool access, and data flow.
What you get
- Data-flow review covering what reaches the model and what is retained
- Retrieval boundary and tenant-isolation review
- Prompt-injection exposure assessment and mitigations
- Tool and function-calling permission review
- Written findings with design-level recommendations
AI Feature Discovery & Architecture Review
Before you build an AI feature into an existing Java system, an architecture review that tells you what it will actually cost and where it will break.
Scope: Planned or in-progress AI features in enterprise Java and Spring Boot systems.
What you get
- Feasibility assessment against your existing architecture
- Retrieval and model integration approach
- Build-versus-buy assessment for the components involved
- Delivery sequence with effort estimates
- Written architecture recommendation
Final price depends on scope. It is fixed and agreed in writing before work begins.
Start a conversation
Tell us what you're dealing with — a findings report, a codebase, or a feature you're planning. You'll speak directly to the engineer who does the work.