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Work · 04 · AI security
KavachRT
Security evaluation for AI systems, because shipping a model-driven product without adversarial testing is shipping an unlocked door.
Status
In development
In active development, not yet released
Domain
AI security and red teaming
Links
The problem
AI systems fail differently from ordinary software. A traditional security review will not find a prompt injection, a jailbreak path or a model that leaks its context under pressure, yet these are precisely the failures that make headlines. Teams shipping LLM-powered products largely test them the way they test CRUD apps, and attackers know it.
The approach
Kavach is Hindi for armour. KavachRT is an autonomous operator that probes a target the way an attacker would, adapts as it finds weaknesses, and produces something a team can act on.
- Adversarial testing as repeatable campaigns: prompt injection, jailbreaks, goal hijacking, data-exfiltration probes.
- Code-level risk around the model: unsafe prompt construction, unvalidated tool calls, over-broad agent permissions.
- A reproducible score and a go/no-go verdict, so the same target under the same conditions yields a comparable number, and runs can be compared over time.
The output is not a score for a slide deck. It is a finding list: what broke, how, and what to change. An evaluation you cannot act on is theatre.
Why this lab
KavachRT sits where the lab’s two deepest roots meet: a long DevSecOps career and peer-reviewed malware research (ICISSP 2024), and current hands-on work in AI red teaming.
Status
In active development. We say so plainly rather than selling it as available. Follow progress at kavachrt.com.