KauanCosta2000

KauanCosta2000 / Ultimate-ssrf-Framework

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Advanced SSRF discovery, validation and analysis framework for bug bounty, pentesting and security research.

github.comKauanCosta2000Ultimate-ssrf-Framework appsec bug-bounty cybersecurity experimental offensive-security
10
2
69% credibility
Found Jun 01, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

The Ultimate SSRF Framework is a security testing tool designed to help researchers and penetration testers find Server-Side Request Forgery vulnerabilities in web applications. It automatically discovers website endpoints, tests them with various attack patterns, and can access cloud provider metadata services (AWS, Azure, Google Cloud, Alibaba) to check for serious security issues. The tool supports blind vulnerability detection through callback services, AI-assisted attack generation, and produces reports in multiple formats for documentation or integration with other security tools. It is intended for authorized security testing, bug bounty hunting, and educational purposes.

How It Works

1
🔍 You discover a security testing tool

You hear about a tool that helps find security weaknesses in websites, specifically ones where servers can be tricked into fetching unexpected content.

2
🎯 You choose what to test

You enter a website address you have permission to test, or provide a list of websites from a file.

3
🤖 The tool explores automatically

The tool visits the website, finds hidden paths and parameters, and tests them for vulnerabilities—all while you watch.

4
You choose how to receive results
📡
Out-of-band callbacks

The tool sends special requests and waits for the website to 'call back' to a service you provide, proving the vulnerability exists.

💻
Direct results

The tool shows you immediately what it found, including any sensitive data the server accidentally revealed.

5
☁️ Cloud services get checked

If the target uses Amazon, Google, Azure, or Alibaba cloud services, the tool checks whether it can access secret metadata like access keys.

6
📊 You get a complete report

The tool creates easy-to-read reports showing what it found, how serious each issue is, and what happened during testing.

✅ You have actionable security findings

You now have documented evidence of security weaknesses that developers can fix, or findings to report through bug bounty programs.

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AI-Generated Review

What is Ultimate-ssrf-Framework?

A Python-based CLI tool that automates the tedious parts of SSRF testing. It crawls your target for vulnerable endpoints, fires off payloads targeting cloud metadata services (AWS, Azure, GCP, Alibaba), and logs any out-of-band callbacks to confirm blind findings. The framework wraps Playwright for browser automation and integrates with multiple AI providers to generate context-aware payloads. Beyond basic testing, it fingerprints WAFs and suggests bypass techniques, tests specialized protocols like gRPC and WebSocket for SSRF vectors, and exports findings to formats compatible with Nuclei, SIEM tools, or HTML reports.

Why is it gaining traction?

Most SSRF tools focus on a single technique. This one strings together the full workflow—discovery, testing, validation, reporting—in one command. The AI integration is the differentiator: feed it your target context and it generates payloads tailored to your WAF and cloud provider rather than relying solely on wordlists. Support for multiple export formats means findings flow directly into your existing security pipeline without manual conversion. The experimental modules targeting Kubernetes and serverless environments address real attack surface that standard scanners miss.

Who should use this?

Bug bounty hunters who want to quickly enumerate SSRF vectors across multiple targets. Penetration testers who need to document findings in formats their clients expect. Security researchers exploring SSRF in cloud-native applications. If you're doing web app testing professionally and SSRF is in scope, this reduces repetitive reconnaissance work significantly.

Verdict

The feature set is impressive for any SSRF toolkit, and the AI-assisted workflow genuinely adds value. However, the credibility score sits at 0.699999988079071% with only 10 stars—this is an early-stage project with experimental modules that the README explicitly warns may produce false positives. Use it for reconnaissance and hypothesis generation, but validate every finding manually before reporting. Worth watching as it matures.

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