In 2022, I embarked on a journey with jswzl, believing that a single developer could deliver immense value without a team by focusing on high-value outputs and minimizing low-leverage work. As a solo developer, efficiency is critical, and 2023 brought the widespread availability of large language models (LLMs) and AI, which greatly benefit individuals like myself. To explore their potential, I tackled a weekend project addressing common bug bounty-hunting issues using ChatGPT and Replit. I also wrote this blog post with the help of GPT-4.
Wanna just get straight to the code? Head over to Github.
Problem Statement
Bug bounty hunters often face challenges when identifying vulnerabilities in web applications:
- Getting rate-limited
- Getting Akamai banned
- Location restrictions
- Getting blocked by sending too many requests from one IP to a host
- Difficulty in rotating IP addresses
These issues can hinder progress and decrease the chances of finding vulnerabilities. Enter Socksprox.
Building it with ChatGPT and Replit
Socksprox was developed with ChatGPT, a powerful AI language model, and Replit, an online coding platform. ChatGPT provided an enjoyable and reliable experience, generating human-like text and assisting with code creation, configuration, and troubleshooting.
Unfortunately, Replit’s ghostwriter was problematic, returning errors 50% of the time. Due to these issues, I stopped using Replit after just one day. Despite its potential, Replit’s buggy performance was a letdown.
Accelerated Learning with LLMs
LLMs like ChatGPT, in my opinion, have revolutionized the learning process for developers, significantly enhancing the speed of the trial-and-error feedback loop. LLMs provide real-time feedback, suggestions, and potential fixes, streamlining the trial-and-error process and enabling developers to progress more rapidly.
Furthermore, LLMs help developers understand the “why” behind solutions by offering explanations and insights into the underlying logic and principles. By asking LLMs how things work, developers can gain a deeper understanding of technologies, design patterns, and best practices. This deeper understanding reinforces learning in much less time, empowering developers to reach new heights in their skillsets and maximizing their potential in the world of technology.
Some people express concerns that relying on LLMs might result in developers who lack a deep understanding of their work. However, I believe this worry is overstated. Utilizing LLMs offers several key benefits:
- Collaborating with LLMs is akin to pair programming with a senior developer, providing valuable guidance and expertise.
- LLMs serve as a unified resource, combining the best of documentation and platforms like Stack Overflow, without the need to search extensively and filter out noise.
- LLMs enable developers to ask highly specific, novel questions and engage in follow-up queries based on the provided answers. This interactive process reinforces learning and promotes a deeper understanding of the subject matter.
Overall, LLMs have the potential to greatly enhance developers’ learning experiences and help them build a solid foundation in their respective fields.
Solution
Socksprox addresses bug bounty hunting challenges using a Kubernetes cluster with multiple Dante Socks Proxy nodes. A load balancer distributes traffic evenly, allowing requests to be sent through as many IPs as the cluster scales to. This setup bypasses rate-limiting, decreases the chance of Akamai bans, overcomes location restrictions, limits the impact of IP blocking, and simplifies IP address rotation.
And it’s fairly inexpensive. When using DigitalOcean, each node costs $5/month, with the load balancer priced at $12/month, handling 10,000 requests per second and 10,000 simultaneous connections. Ten 24/7 IPs cost $62/month, while 100 IPs cost $512/month. Kubernetes autoscaling allows for automatic adjustments, even downscaling at night to save money.
Conclusion
Socksprox offers a practical, scalable, and efficient solution for overcoming bug bounty hunting challenges. The development process demonstrated the transformative potential of LLMs like ChatGPT for solo developers. These models enable developers to bring ideas to life faster than ever before, with creativity being the only limitation.
Will I be using LLMs going forward? You better believe it. I was surprised myself. I don’t like things I don’t understand, and I suffer hugely from Not Invented Here Syndrome. But I’m a believer now.
You can find the code and try out Socksprox for yourself from Github.
Charlie's blog 