KavachRT is the open-source AI red-team framework. An autonomous operator attacks your LLM or AI system, adapts as it finds weaknesses, and returns a reproducible security score and go/no-go — before you ship.
Launching soon AI Red Teaming, automated. · pip install kavachrt
Anyone can run a probe once. KavachRT reasons over the results and escalates — the way a human red-teamer would, at machine speed.
An LLM orchestrator plans attacks, detects weaknesses, mutates its strategy, and re-attacks in a governed feedback loop — not a fixed pipeline.
A genuinely powerful free Framework — console, modules, and the full agentic loop. Pro adds scale, collaboration, and compliance.
A LiteLLM-backed target layer attacks hosted APIs and self-hosted models (Ollama, vLLM) alike. Bring your own model.
The 0–100 security score and go/no-go are computed deterministically — defensible enough to gate a CI pipeline.
Every finding maps to the OWASP LLM Top 10 and MITRE ATLAS — comparable, auditable, and ready for review.
Authorization gating, budget caps, redaction, and sandboxing are built in. For authorized testing only.
The differentiator a static pipeline structurally can't match: it learns from each result and decides what to try next.
Baseline sweep across OWASP-LLM categories on your target.
The orchestrator reads findings and spots what's exploitable.
It picks targeted, adaptive attacks and adjacent categories to probe.
Multi-turn attacks escalate against the weak spots.
Deterministic re-scoring, then loop or decide go/no-go.
Governed by an explicit budget — max iterations · cost cap · convergence — so it always terminates.
Start with the open-source Framework. Upgrade to Pro when you need scale, teams, and compliance.
Get the open-source framework today, or talk to us about Pro for your team.