Building XploitScan: How Focusing on Old Security Risks in AI Code Creates Market Opportunity

What really pushed me to build this was watching developers spin up full apps in just a few days using LLMs, even though they had basically zero software engineering or security experience. I have been in cybersecurity for years, so I could see exactly where that was heading. All the classic problems we have been fighting for decades, like SQL injections, exposed API keys and secrets, weak authentication, and missing validation, were suddenly getting baked into production code at a massive scale by people who did not even know those risks existed. It was honestly kind of scary. That gap between how fast people could build and how little they knew about secure coding is what lit the fire for me. So I started XploitScan as a static analysis tool specifically tuned for the kinds of issues that keep showing up in AI-generated code. The goal is simple. Help builders catch these problems early, even if they do not know exactly what to look for yet. It is not about inventing new threats. It is about making the old ones visible before they bite.

Both of my early launches pretty much flopped. I tried Product Hunt and Hacker News, thinking I would get a nice burst of users, but honestly, it was mostly just traffic with almost zero real sign-ups or engagement. People would check it out and then disappear. What actually started working, even though it was slow, was just consistently putting out useful content. Reddit posts in relevant communities, writing on my own blog, and sharing lessons from the space. It does not give you that big, glamorous spike everyone hopes for, but it brings in the right people. Indie devs and small teams who are genuinely dealing with AI code and looking for exactly this kind of tool. That organic, slower growth feels way more sustainable. I am doubling down hard on it now because the quality of users is so much better. I would rather have a smaller group of people who actually stick around and use the product than a bunch of one-time visitors from big launch sites.

Early feedback mainly flagged two big things. Coverage and trust. On the coverage side, they wanted the tool to catch more of the specific issues they were seeing in their AI-generated code, so I expanded the rule set pretty quickly based on their feedback. The trust part was more interesting. Feedback for Teams told me the users needed to feel confident actually handing their codebase over and sharing results internally. So I built out a Trust Page explaining our approach, added an MCP server option, rolled out proper team accounts, and improved the reporting so everything was easier to review and export. The real lesson for me was that early users did not just want a lot of findings. They needed the whole experience to fit smoothly into their workflow and feel safe. Security tools are sensitive, especially when you are scanning code that might contain real secrets. Making people comfortable with the tool turned out to be just as important as making the detection itself better.

Most people assume AI-generated code brings some scary new type of vulnerability we have never seen before. It really does not. It is the same old problems we have been dealing with forever. SQL injection, hard-coded secrets, broken authentication, missing input validation, that kind of stuff. What changed is who is writing the code and how fast they are shipping. People with literally no security background are throwing together real apps over a weekend. So all those mistakes are now happening at a much bigger scale and much faster than before, and users of these platforms are none the wiser to it. The market opportunity is not about catching exotic new threats that only AI could create. It is about catching the well-known ones in code written by folks who do not even know those problems exist. That is the gap XploitScan is trying to fill. It's giving builders visibility into risks they would not spot on their own.

For the next 6 to 12 months, it is really about getting some actual traction and expanding coverage. I am going to keep adding more rules for all the common issues I keep seeing pop up in AI-generated code. At the same time, I want to lean harder into content because that has been the main growth driver so far. I also plan to spend more time talking directly to indie devs and small teams to understand what they really need and build exactly that. Less hype and flashy stuff, more just becoming the tool that people naturally turn to by default when they are working with AI code.

I came into this with a pretty deep cybersecurity background, so the core problem space felt very natural to me. I already understood the threat models and the kinds of vulnerability classes that XploitScan needed to focus on. That part was comfortable. What was completely new territory was the founder's side of things. Launching an actual product, trying to get initial traction, figuring out marketing, writing content, and growing real users. I knew the security problems cold, but I had to learn everything else on the fly. How to talk to users, how to prioritize features, even basic stuff like positioning. It has been a steep curve, but honestly, pretty rewarding to watch the pieces slowly come together.

The biggest mistake so far was building too much before I really understood what my actual users needed. I knew the security problems inside and out from my own experience, so I made a lot of assumptions about what the tool should do. But I did not spend enough time early on talking to indie devs and small teams to learn what they actually wanted in something like this. I ended up shipping features based on those assumptions and then had to rework quite a bit once real feedback started coming in. It taught me that domain expertise is super useful, but it is not the same as real customer understanding. You still have to get out and talk to people who will actually use the product.