Open Source
Offensive Security

Why I Built SecurityForge —
And Why I'm Giving It Away

A deep dive into building an open-source offensive security toolkit with 4,025+ attack payloads, 25 WAF fingerprints, and AI-native workflows — and why every company that pays for a WAF should be testing it with real attack patterns.

4,025+
Attack Payloads
25
WAF Vendors Detected
100%
OWASP Coverage
120
CVE Exploits
0
Dependencies

TL;DR

We built SecurityForge — an open-source offensive security toolkit with 4,025+ curated attack payloads, automatic fingerprinting for 25 WAF vendors, and native integration with Claude Code and ChatGPT. It's free, it has zero dependencies, and you can run it in 10 seconds.

We're open-sourcing it because the attackers already have these payloads. The defenders are the ones who don't. This levels the playing field.

# Try it right now
$ git clone https://github.com/dalisecurity/securityforge.git
$ cd securityforge
$ python3 waf_tester.py -i

SecurityForge v3.0 — Interactive Mode
4,025 payloads loaded across 15 categories
Ready. Enter target URL to begin.

The Problem Nobody Talks About

Here's a dirty secret in web application security: most companies have no idea if their WAF actually works.

They pay $5,000–$50,000/year for a Web Application Firewall — Cloudflare, AWS WAF, Akamai, Imperva — slap it in front of their app, and assume they're protected. They run a quick scan with a commercial tool, see a green checkmark, and move on.

But what happens when a real attacker shows up?

I've spent years in security consulting, and the pattern is always the same:

  1. Company deploys WAF with default rules
  2. Company assumes they're safe
  3. Attacker bypasses WAF in 15 minutes using a $0 payload from a GitHub repo
  4. Company gets breached
  5. Repeat
The Real Issue

The problem isn't the WAF vendors — they build solid products. The problem is nobody stress-tests their WAF configuration with real-world attack payloads. Default rules are not enough. Custom rules need custom testing.

The few testing tools that exist are either outdated (last updated 2019), fragmented (scattered across 50 repos), expensive (commercial tools costing more than the WAF), or unusable (raw text files with no tooling). SecurityForge fixes all four.


What's Actually Inside: The Nitty Gritty

SecurityForge isn't just a dump of random payload strings scraped from the internet. Every payload is categorized by attack vector, mapped to OWASP frameworks, tagged with CVEs where applicable, tested against real WAFs, and documented with descriptions explaining what it does and why it matters.

4,025+ Payloads Across 15 Categories

XSS
779
779
WordPress CVEs
450
LLM / AI Security
300
SQL Injection
148
Modern Bypasses
138
Command Injection
125
CVE Exploits
120
SSRF
72
SSTI
62
Path Traversal
59
LDAP / XPath / CRLF
163
XXE + Others
609

Deep Dive: Why XSS Gets 779 Payloads

XSS is the most common web vulnerability (OWASP A03:2021) and WAF bypass techniques evolve constantly. Our collection isn't 779 variations of <script>alert(1)</script>. It includes:

Every payload is designed to answer one question: does your WAF block this, or does it slip through?

LLM Security: The New Frontier

This is the category I'm most excited about. As organizations deploy LLMs in production, prompt injection is the new XSS. Our 300+ AI security payloads include:

138 Modern Bypass Techniques (2025–2026)

This section alone is worth the clone. These are bypass techniques discovered in the last 18 months that most WAF rule sets haven't caught up to:


WAF Detection: 25 Vendors Fingerprinted

Before you test payloads, you need to know what you're up against. SecurityForge's WAF detector identifies 25 vendors by analyzing HTTP response headers, cookies, error pages, server signatures, and response timing patterns.

# Detect WAF on a target
$ python3 waf_detector.py -t https://example.com

WAF Detected: Cloudflare
Confidence: 94%
Signatures matched:
  Header: cf-ray = 8a2b3c4d5e6f
  Cookie: __cfduid present
  Server: cloudflare
  Error page: Ray ID pattern matched
# WAF Vendor Detection Method Key Signatures
1CloudflareHeaders, Cookies, Error Pagecf-ray, __cfduid, Ray ID
2AkamaiHeaders, Cookies, Bot Managerakamai-grn, ak_bmsc
3AWS WAFHeaders, Cookies, Responsex-amzn-waf-action, awsalb
4ImpervaHeaders, Cookies, Challengex-iinfo, incap_ses
5F5 BIG-IPHeaders, Cookies, Error Pagex-wa-info, bigipserver
6FastlyHeaders, Cookies, Serverx-sigsci-requestid
7Azure WAFHeaders, Cookies, Front Doorx-azure-fdid, arr_affinity
8Google Cloud ArmorHeaders, Server, reCAPTCHAx-cloud-trace-context
9BarracudaHeaders, Cookies, Serverx-barracuda-url
10Citrix NetScalerHeaders, Cookies, Servercitrix-transactionid, nsc_
11RadwareHeaders, Response Textx-protected-by, AppWall
12Palo AltoHeaders, Response Textx-pan-, x-prisma
13Check PointHeaders, Response Textx-checkpoint
14ModSecurityHeaders, Server, Responsex-mod-security
15Qualys WAFHeaders, Response Textx-qualys
16Penta SecurityHeaders, Cookiesx-wapples
17StackPathHeaders, Serverx-stackpath-shield
18SophosHeaders, Response Textx-sophos
19ScutumHeaders, Response Textx-scutum
20Rohde & SchwarzHeaders, Response Textx-rs-
21SucuriHeaders, Cookies, Serverx-sucuri-id
22Fortinet FortiWebHeaders, Cookies, Serverx-fortiweb, fortiwafsid
23WallarmHeaders, Serverx-wallarm-waf-check
24ReblazeHeaders, Cookies, Serverx-reblaze-protection, rbzid
25VercelHeaders, Serverx-vercel-id

Each detection includes a confidence score (0–100%) based on how many signatures matched. High confidence (70–100%) means multiple indicators confirmed. Low confidence means we found hints but can't be certain.


100% OWASP Coverage — All Four Frameworks

Most tools cover parts of OWASP Top 10. SecurityForge covers all four major OWASP frameworks with 100% category completeness:

1,690+
OWASP Top 10:2021
575+
Mobile Top 10:2024
300+
LLM Top 10
520+
API Security Top 10

This means if you're doing a security assessment and need payloads for any OWASP category — web, mobile, AI, or API — SecurityForge has you covered. No more stitching together payloads from five different repos.


Why AI-Native Matters

This is what separates SecurityForge from SecLists, PayloadsAllTheThings, and every other payload collection.

SecurityForge was designed from day one to work with AI assistants. Every payload is categorized, tagged, and documented in structured JSON — not dumped as raw text. This means AI assistants can understand the data and make intelligent decisions.

With Claude Code (in your IDE)

You: "Detect the WAF on staging.example.com and test for XSS bypasses"

Claude Code:
   Running waf_detector.py → Cloudflare detected (92%)
   Selecting Cloudflare-specific XSS bypass payloads
   Running waf_tester.py — 779 payloads tested
   12 payloads bypassed WAF rules
   Generating HTML report with findings
   Report saved to security_report.html

With ChatGPT

You: "I'm testing a WordPress site behind AWS WAF.
      What payloads should I try for auth bypass?"

ChatGPT:
   References CVE-2026-28515 (REST API bypass)
   Pulls 150+ relevant payloads from SecurityForge
   Explains each attack vector in plain English
   Suggests testing methodology and order
   Provides WAF rule recommendations

The key insight: AI assistants are only as good as the data they work with. A structured, documented payload database turns ChatGPT from a generic security chatbot into an actual penetration testing copilot.


Why Open Source? Why Free?

I could have kept this proprietary. A curated payload database with WAF detection and AI integration has real commercial value — security consulting firms would pay for it. But I'm open-sourcing it for four reasons:

1. Security Through Transparency

The attackers already have these payloads. They share them in private Telegram channels, dark web forums, and closed bug bounty communities. The defenders are the ones who don't have access. Open-sourcing this levels the playing field.

2. WAF Vendors Need Pressure

When WAF vendors know their customers can easily test bypass techniques, they're incentivized to improve their rule sets. SecurityForge creates a public benchmark — if your WAF can't block these payloads, the community will know.

3. Community Makes It Better

No single person can track every new bypass technique across every WAF vendor. Open source means bug bounty hunters contribute new bypasses, researchers add published payloads, and WAF vendors can test against a standardized set.

4. AI Needs Good Data

AI assistants are increasingly used for security work. But they need structured, categorized, well-documented data to be useful. SecurityForge is built to be AI-readable, not just human-readable.


How to Actually Use This

Scenario 1: "Is my WAF actually protecting me?"

# Step 1: Detect your WAF
$ python3 waf_detector.py -t https://yoursite.com
WAF Detected: AWS WAF (87% confidence)

# Step 2: Run category-specific tests
$ python3 waf_tester.py -t https://yoursite.com -p payloads/xss/basic.json
$ python3 waf_tester.py -t https://yoursite.com -p payloads/sqli/basic.json

# Step 3: Generate a report
$ python3 report_generator.py --html-report security_report.html
Report generated: security_report.html

Scenario 2: "We just deployed a WAF — what do we test first?"

Start with the OWASP Top 10 payloads. If your WAF can't block these, nothing else matters:

  1. A03: Injection — Run all XSS and SQLi payloads. This is where most breaches start.
  2. A01: Broken Access Control — Test path traversal and SSRF payloads.
  3. A10: SSRF — Critical for cloud deployments. Test metadata endpoint access.

Scenario 3: "I'm doing a bug bounty and found a WAF"

  1. Run waf_detector.py to identify the vendor
  2. Check vendor-specific bypass payloads in the database
  3. Try the 138 modern bypass techniques (2025–2026)
  4. Use encoding variations and polyglots

Scenario 4: "Test our LLM deployment for prompt injection"

$ python3 waf_tester.py -i
SecurityForge v3.0 — Interactive Mode

Select category:
  [1] XSS   [2] SQLi   [3] SSRF   [4] SSTI
  [5] Command Injection   [6] XXE
  [7] AI/LLM Security ←

300+ LLM payloads loaded
Categories: prompt injection, jailbreaks, RAG poisoning, data exfiltration

Custom Payloads: The Real Power

The pre-built database is just the starting point. The real power of SecurityForge is building custom payloads for your specific environment.

Every organization's WAF configuration is different. The rules that Cloudflare applies to an e-commerce site are different from what AWS WAF applies to a financial API. Generic testing misses the gaps. Custom testing finds them.

# Generate custom payloads with the built-in creator
$ python3 payload_creator.py

What do you want to test?
> XSS bypass for Cloudflare with React frontend

Generated 15 custom payloads:
  1. React dangerouslySetInnerHTML exploitation
  2. JSX expression injection via template literals
  3. Cloudflare encoding bypass (double URL + Unicode)
  4. DOM clobbering targeting React hydration
  ...

With AI assistants, you can go even further: describe your tech stack in plain English and let Claude or ChatGPT generate targeted payloads using SecurityForge's database as a foundation.

The Goal

We want every company — from a 2-person startup to a Fortune 500 enterprise — to be able to answer one question with confidence: “Is my WAF bulletproof, or is it security theater?”


What's Next

SecurityForge is just getting started. Here's what we're building:


Try SecurityForge

4,025+ payloads. 25 WAF fingerprints. Zero dependencies. Zero configuration.
Clone and run in 10 seconds.

View on GitHub
$ git clone https://github.com/dalisecurity/securityforge.git
$ cd securityforge
$ python3 waf_tester.py -i

# That's it. No pip install. No config. No API keys.