iCompaas LLM VAPT is a dedicated security platform that continuously red-teams your AI applications — finding the prompt injections, data leaks, and jailbreaks before attackers do.
Built for teams shipping LLM-powered products who need to prove — not just assume — their AI is safe.
Traditional security tools were built to defend code, networks, and databases. They weren't built to defend conversations. Generative AI introduces a category of risk that conventional scanners simply can't see.
A user types a clever sentence — and your model leaks its own instructions.
A chatbot connected to internal tools gets talked into running a command it shouldn't.
A RAG-powered assistant surfaces a document it was never supposed to retrieve.
A "harmless" jailbreak prompt turns your AI into a liability in front of a customer.
These aren't hypothetical edge cases. They're the new front line of application security — and most organizations have no continuous, structured way to test for them. That gap is what iCompaas LLM VAPT closes.
iCompaas LLM VAPT is a purpose-built platform for the continuous security testing of Large Language Model applications.
Attacking your AI on purpose, safely, to see what actually breaks.
Translating raw vulnerabilities into business language and fixes.
You need to know, with evidence, whether your models can be manipulated — and you need that evidence in a form your team can act on immediately: reproducible attack paths, clear severity, and concrete fixes.
You need to demonstrate due diligence to regulators, auditors, and customers. iCompaas LLM VAPT translates technical testing into the structured evidence those conversations require.
You need confidence that the AI feature you're about to launch won't become the headline you didn't want. A measurable security posture means you can move fast and defensibly.
Connect the LLM applications, APIs, or models you want assessed. These become your Targets — the things under test.
Launch automated testing Engagements — structured, repeatable assessments that probe your targets the way an adversary would.
Every successful attack becomes a Finding — triaged by severity, backed by proof of how it happened, and linked to the specific risk.
Each finding comes paired with practical remediation guidance — from prompt-level hardening to architectural changes.
A continuously updated security posture score shows whether your AI surface is getting safer as your application evolves.
Conventional penetration testing looks for broken code. LLM security testing looks for broken judgment — the ways a model can be talked into doing something it shouldn't.
Getting a model to ignore its own instructions and act on an attacker's behalf.
Tricking a model into revealing what it was told to keep hidden, exposing internal logic or sensitive info.
Testing what happens when an AI with real permissions is pushed too far outside its intended boundaries.
Checking whether what a model generates can hurt the systems downstream (e.g. XSS, injection).
Verifying the data and components behind your model haven't been tampered with.
These aren't bugs you patch once. They're risks you have to keep testing for — because every prompt is a new potential attack surface.
Security claims mean little without a framework behind them. iCompaas LLM VAPT structures its testing and reporting around recognized approaches to AI risk — so your findings are organized and explainable to auditors.
That structure turns a pile of red-team output into something your compliance team can actually use.