# zakirkun/deep-eye

Deep Eye orchestrates multiple AI providers (OpenAI, Claude, Grok, Gemini, OLLAMA, Groq, Mistral, OpenRouter, LiteLLM, LM Studio) for intelligent payload generation, scans targets for 45+ vulnerability types, and produces professional reports with compliance mapping.

Repository: https://github.com/zakirkun/deep-eye
Canonical: https://ross.abutalabs.com/products/deep-eye
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-08-26T03:44:58+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 54, longevity 23
- inputs: {"age_days": 322, "days_push": 7, "days_rel": 98, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2219, forks 406 (observed 2026-08-28T04:06:27.351033+00:00)

## What it is
Deep Eye is an AI-driven penetration testing CLI that orchestrates multiple LLM providers (OpenAI, Claude, Gemini, OLLAMA, Groq, and others) to generate context-aware payloads and scan web targets for 50+ vulnerability types. It produces professional reports with compliance mapping (PCI-DSS, SOC2, ISO 27001), false-positive triage, and export to formats like HTML, PDF, SARIF, and JSON.

## Use cases
- scan a web app for sql injection and xss vulnerabilities
- generate ai-powered payloads for penetration testing
- automate bug bounty report writing
- map security findings to pci-dss and iso 27001 compliance
- run vulnerability scans with multiple llm provider failover
- retest a target and diff against a previous scan baseline
- seed a crawl from an openapi swagger spec

## When to choose
- you want LLM-assisted payload generation and false-positive triage in a single pentest tool
- you need compliance-mapped, exportable vulnerability reports
- you want flexible AI provider support including local models via OLLAMA
- you run bug bounty work and want HackerOne-style report drafts

## When to avoid
- you need a fully passive or compliance-audit-only scanner without AI dependencies
- you cannot provide any AI provider API key and have no local model
- you require a license-audited tool - the license is listed as NOASSERTION despite an MIT badge
- you need a mature enterprise scanner with long-term vendor support

## Facets
- artifact type: cli-tool
- maturity: active
- function: penetration-testing, vulnerability-scanning, llm-inference, agent-framework, security
- domain: security, penetration-testing, artificial-intelligence, developer-tools
- platform: python, windows, cli
- tags: ai-driven-pentesting, payload-generation, bug-bounty, compliance-mapping, multi-provider-llm, web-security-scanner, nuclei-templates, playwright, reporting, linux, macos

## Member repositories
- zakirkun/deep-eye (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:27.351033+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 2026-08-30T02:45:53.154810+00:00, confidence not recorded.
  - readme: https://github.com/zakirkun/deep-eye (fetched 2026-08-28T04:06:27.351033+00:00, sha 8f666378c9e6)
- Data as of 2026-08-30T08:39:29.467469+00:00.