geeknik/the-nuclei-templates: AI-Assisted Security Templates for Bug Bounty Hunters

Hook

What happens when a bug bounty hunter pairs with an AI to systematically codify every weird edge case and OAuth misconfiguration they've ever found? You get a repository of security templates that feel less like corporate checklist items and more like field notes from the trenches.

Context

Nuclei, ProjectDiscovery's open-source vulnerability scanner, has revolutionized automated security testing by replacing heavyweight scanners with fast, YAML-based templates. Think of it as infrastructure-as-code, but for vulnerability detection. The official nuclei-templates repository contains thousands of curated, enterprise-ready detection rules covering everything from CVEs to misconfigurations.

But official repositories have a problem: they optimize for reliability and breadth, not for bleeding-edge discoveries or niche attack vectors. Security researchers hunting bugs in OAuth flows, cloud-native architectures, or AI/ML pipelines often find themselves writing custom templates that never make it upstream. geeknik's collection fills this gap—a community-driven template library where experimental detections, AI-assisted template generation, and real-world bug bounty findings coexist. It's explicitly maintained as a living research artifact, not a production-hardened toolkit, making it valuable precisely because it doesn't promise stability.

Technical Insight

Nuclei templates are YAML files that define HTTP requests, match conditions, and extraction logic. The architecture is deceptively simple: each template is a standalone detection rule that can run independently or as part of a batch scan. Here's a representative template structure from the repository:

id: oauth-redirect-leak

info:
  name: OAuth Redirect URI Bypass
  author: geeknik
  severity: medium
  description: Detects OAuth implementations that fail to validate redirect_uri parameter strictly
  reference:
    - https://portswigger.net/research/oauth
  tags: oauth,redirect,misconfig

requests:
  - method: GET
    path:
      - "{{BaseURL}}/oauth/authorize?client_id={{client_id}}&redirect_uri=https://attacker.com&response_type=code"
    
    matchers-condition: and
    matchers:
      - type: status
        status:
          - 302
      
      - type: regex
        part: header
        regex:
          - 'Location: https://attacker\.com\?code='
    
    extractors:
      - type: regex
        name: leaked_code
        part: header
        regex:
          - 'code=([a-zA-Z0-9_-]+)'

What makes geeknik's templates distinctive is their focus on edge cases that mainstream scanners miss. Rather than detecting known CVEs with rigid signatures, these templates probe behavioral vulnerabilities—OAuth implementations that accept partial domain matches in redirect_uri validation, cloud metadata endpoints exposed through SSRF chains, or GraphQL introspection leaking sensitive schema information. The AI-assisted development model (Claude as co-pilot) enables rapid hypothesis testing: "What if an API accepts Unicode normalization attacks in user IDs? Let's template that."

The repository demonstrates sophisticated matcher composition. Instead of simple string matching, templates chain multiple conditions with boolean logic. A template might verify that a response contains a specific header AND returns status 200 AND the body matches a regex pattern AND the Content-Type is JSON. This reduces false positives while maintaining flexibility:

matchers-condition: and
matchers:
  - type: word
    part: body
    words:
      - '"admin":true'
      - '"privileges"'
    condition: and
  
  - type: status
    status:
      - 200
  
  - type: dsl
    dsl:
      - 'len(body) > 100'
      - 'contains(content_type, "json")'
    condition: and

The DSL (Domain Specific Language) matcher is particularly powerful—it allows arbitrary logic using Go-like expressions to validate response characteristics beyond simple string matching. You can compare content lengths, perform arithmetic on extracted values, or validate cryptographic signatures.

Another architectural strength is the template tagging system. Each template includes metadata tags like cloud, api, oauth, or graphql enabling targeted scans. If you're testing a cloud-native application, you can run only templates tagged cloud and kubernetes rather than executing the entire collection. This modularity means you can integrate specific subsets into CI/CD pipelines without performance penalties.

The fuzzing-adjacent templates show the repository's experimental nature. Some templates include payload lists that test for parser differentials—sending requests with malformed JSON, unusual Unicode characters, or boundary-condition integers to see how different layers of the application stack handle them. This approach surfaces vulnerabilities in custom parsers, WAF bypasses, or backend validation logic that strict signature-based scanners would never trigger.

Gotcha

The repository's greatest strength—its experimental, research-oriented nature—is also its primary limitation. There's no quality assurance process, no false-positive rate testing, and minimal documentation per template. You'll find templates ranging from proven bug bounty winners to speculative probes that may never trigger on real targets. Some templates assume specific endpoint structures that only exist in particular tech stacks, making them useless for general scanning.

More critically, the lack of severity calibration and impact documentation means you can't blindly trust findings. A template might flag behavior that's technically interesting but not actually exploitable in most contexts. For example, a template detecting verbose error messages might trigger on debug endpoints that are intentionally exposed in staging environments but never production. Without per-template documentation explaining expected false positive rates and real-world impact, you need to manually validate every finding before reporting it. This makes the repository unsuitable for automated security pipelines where you need high-confidence detections with minimal analyst overhead. It's a research collection, not a turnkey security solution.

Verdict

Use if: You're a bug bounty hunter, red teamer, or security researcher who needs cutting-edge detection logic for modern attack surfaces—OAuth flows, cloud-native architectures, GraphQL APIs, or AI/ML pipelines. This repository shines when you're hunting zero-days or exploring novel vulnerability classes where mainstream scanners have no coverage. It's also valuable if you're learning Nuclei template development and want real-world examples of complex matcher logic and DSL usage. Skip if: You need enterprise-grade stability, documented false-positive rates, or templates you can deploy in production security scanning without manual validation. If your threat model involves compliance scanning or automated vulnerability management, stick with the official ProjectDiscovery templates. This collection requires security expertise to interpret findings and tune templates—it's a power tool for specialists, not a beginner-friendly security solution.