# Kritt-ai/open-kritt

Open-source, self-hosted AI vulnerability research tool that orchestrates agents to find and validate security issues in code.

Repository: https://github.com/Kritt-ai/open-kritt
Canonical: https://ross.abutalabs.com/products/open-kritt
Homepage: https://kritt.ai/
Language: JavaScript
License: AGPL-3.0
License Family: copyleft
Topics: ai, bug-bounty, bugbounty-tools, security-research, ai-agents, ai-security, code-security, hackenproof, hackerone, immunefi, security-automation, security-tools, self-hosted, source-code-analysis, vulnerability-scanner
Last push: 2026-08-24T07:14:50+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 3
- inputs: {"age_days": 44, "days_push": 9, "days_rel": 17, "gap_med": 6.0, "n_releases_24m": 5}
- flags: young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2011, forks 341 (observed 2026-08-28T04:06:05.129869+00:00)

## What it is
open·kritt is an open-source, self-hosted AI vulnerability research platform that decomposes a codebase into focused security tasks, runs AI agents (Codex or Claude Code) in parallel, and combines results into de-duplicated, ranked findings. It includes a workflow builder, per-finding verification post-scripts, PoC generation, and ZIP export of scan results.

## Use cases
- find vulnerabilities in a codebase with ai agents
- automate bug bounty research on smart contracts or web apps
- audit my repository for security issues before release
- build reusable security research workflows with llm prompts
- generate proofs of concept for discovered vulnerabilities
- self-host an ai-powered vulnerability scanner
- triage and de-duplicate security findings with custom severity rules

## When to choose
- you are a security researcher or bug bounty hunter wanting AI-assisted code review with full control over prompts, models, and data
- you want self-hosted infrastructure so code never leaves your environment
- you need structured, de-duplicated, ranked findings rather than raw LLM output
- you want to validate findings with automated post-scripts and PoC generation

## When to avoid
- you need a fully automated, hands-off scanner with zero configuration
- you lack access to paid LLM providers (OpenAI, Anthropic, OpenRouter, xAI) or Codex/Claude Code
- you need a compliance-oriented SAST tool with established CVE databases rather than AI-driven research
- your codebase is very large and you cannot afford extensive LLM token usage

## Facets
- artifact type: application
- maturity: active
- function: security, vulnerability-scanning, agent-framework, llm-inference, workflow-automation, developer-tools
- domain: security, artificial-intelligence, developer-tools, penetration-testing
- platform: self-hosted, cli
- tags: bug-bounty, code-security, ai-security-research, vulnerability-research, source-code-analysis, security-automation, agpl, ai-agents, web-server, nodejs, docker

## Member repositories
- Kritt-ai/open-kritt (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:05.129869+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:01:04.358006+00:00, confidence not recorded.
  - readme: https://github.com/Kritt-ai/open-kritt (fetched 2026-08-28T04:06:05.129869+00:00, sha 7413ea870abd)
  - homepage: https://kritt.ai/ (fetched 2026-08-29T10:41:18.745365+00:00, sha e0be2bc5a26b)
  - site_page: https://docs.kritt.ai (fetched 2026-08-29T10:41:18.754468+00:00, sha 53c17dcc8238)
  - site_page: https://kritt.ai/pricing (fetched 2026-08-29T10:41:18.756589+00:00, sha ad8493ed31c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.