So I get asked fairly regularly for a starting point on this stuff, and up until now I've just been sending people individual links as they come up in conversation. This is my attempt to put all of it in one place, because most of what gets written in the security space is aimed at enterprise teams with dedicated security staff, not a solo founder shipping an AI product who just wants to understand what they're actually dealing with. That said, everything below seeks to be non technical enough to be useful to somebody without requiring in depth security knowledge, and would be a great resource to bookmark and keep on hand while building out applications. 1. The OWASP LLM Top 10 (genai.owasp.org) If there is one document worth reading before you ship anything with an AI component, this is it. OWASP has been producing security guidance for developers since 2001, and their Web Application Top 10 became the industry standard for traditional web security. In 2023 they released a version specifically for LLM applications, updated again in 2025, covering the ten most critical vulnerabilities specific to AI powered products. Prompt injection sits at number one on that list, which on its own should tell you something. It doesn't need to be read cover to cover, but even skimming the names and one line descriptions should give you a meaningful vocabulary for understanding what your product is and isn't exposed to. Also worth noting: they released an Agentic Applications Top 10 in 2026 specifically for founders building with autonomous AI agents. If this is applicable to your build, I would definitely give that document a further look into as well. 2. Georgia Tech's Vibe Security Radar (scp.cc.gatech.edu) Launched in May 2025 out of Georgia Tech's Systems Software and Security Lab, the Vibe Security Radar does something nobody else was doing at the time: actually tracking CVEs directly traceable to AI generated code. Researcher Hanqing Zhao's reasoning for building it was straightforward: "everyone is saying AI code is insecure but nobody is actually tracking it."

From what I’ve personally observed, it seems to me that though most AI founders seem to be technical enough to bring their software products to life, but not quite technical enough to know what they are actually exposing when they use AI tools both build and ship quickly. With security debt, the unfortunate reality is that this particular knowledge gap often means that when these issues come into the limelight, it's usually because something backfires publicly post launch. The worst part about this is that AI founders in specific are none the wiser, as during the build process it's often assumed that the security layer will be taken care of out of the box; though it's worth noting that this is an area that most AI code generators cannot seem to perfect, especially in 2026. That being said, here are some important fundamental baseline ideas that I think are critical for any AI founder to keep in the back of their mind, both as they build and refine their projects. This list was deducted after my many experiences talking and working with non technical founders across the board, and will go a long way in ensuring you are shipping something safe and secure! 1. Your LLM Is Not a Security Boundary To begin, this is the singular most important mindset shift that most people need to bake front and centre into their minds as soon as absolutely possible. Most assume that AI tools are built with their best interests in mind out of the box; that such tools will build whole and complete products that are functional and air tight all around. Sadly however, as mentioned above security in specific is one of the main pitfalls when it comes to building with AI code generators. Because of this, this is an imperative rule to keep in the front of your mind as you build out each and every single app element on your builds. Thus, the tools that you expose in effect define your attackers blast radius; that is, any parameter that the model in question uses MUST be treated as attacker controlled input. Often, founders will wire up their AI to databases, APIs, and critical admin functions and then assume that the model's instructions will hold throughout. However, in the event of a full fledged cyber attack (or even smaller incidents for that matter, as vulnerabilities tend to compound the more abundant they are), this is rarely the reality. Thus, prompting with security front and center should be imperative to your development process.