A Step-by-Step Guide to Building Your Own AI Security Agent

Meta description: A 148-line Python preflight check can scan a project before an AI agent touches it, testing planted secrets and hidden injection risks before changes begin.

Tags: AI agents, code security, Python, prompt injection, preflight checks

Featured image: Abstract friendly illustration: a small script icon scanning a folder before a robot reaches it; no real people, logos, or text.

A 148-line Python preflight check is aimed at developers who want a lightweight way to inspect a project before an AI agent can access it. A Towards AI post describes the idea: instead of handing a repository to an agent immediately, the script runs a short set of checks on the code and context first. The available excerpt does not give a publication date, but it says the checks were tested against planted secrets and a hidden injection, and it also refers to a further case that is not fully described in the preview. For readers, the value is not a magic firewall. It is a practical question: what can be caught cheaply before an assistant starts reading, writing, or suggesting changes?

Why a Project Preflight Check Matters for AI Agents

AI coding agents usually need context. They may read files, follow links, inspect dependencies, or accept natural-language instructions embedded in comments and data. That convenience creates risk. A secret in a config file can be copied into a log or answer. An instruction hidden in a README can be treated as a command by an agent that follows text too literally. A dependency or generated file can contain content that changes the agent’s behavior.

A preflight check is useful because it separates two decisions that are often collapsed: “Can the agent see this?” and “Is this project safe enough to automate?” The 148-line Python script described by Towards AI is small enough to fit that moment. It does not replace code review, permission controls, sandboxing, or secret scanning. It gives a first pass before an AI agent is allowed near the project.

What the 148 Lines Test

The core fact from the source is narrow: 148 lines of Python check a project before an AI agent is allowed near it. The excerpt identifies planted secrets and a hidden injection as test cases. Those are meaningful examples because they represent two different classes of failure.

Planted secrets are test credentials or tokens deliberately placed in a project. If a scanner flags them, the script is doing something similar to basic secret hygiene: looking for strings that should not be exposed. The exact rules, file types, entropy checks, or false-positive behavior are not shown in the excerpt, so readers should not assume the script catches every credential format.

A hidden injection is different. It tests whether the check can notice text that might steer an AI agent, such as instructions disguised as normal content. Prompt injection is a known risk for systems that consume untrusted text. The source does not specify whether the test used comments, markdown, code strings, or data files, so the available material only shows that the concept was exercised, not how broadly.

The preview also ends with a mention of an additional test case, but the available text does not name it. That gap matters. A security tool’s usefulness depends on the tests it includes and the ones it misses.

What Readers Can Expect From the Script

For non-experts, the main takeaway is the pattern: run a short automated scan before granting an AI agent access to a project. The script’s size suggests it is not a full enterprise platform. It is more likely a focused guardrail, perhaps looking for obvious dangerous strings, suspicious instructions, or files that should not be passed to an agent.

That can help in several real workflows. A developer may want to avoid sending a .env file to a chatbot. A team may want to block README instructions that tell an assistant to “ignore previous rules.” A contributor may want to catch test secrets before a repository is shared with an external tool. In each case, the 148-line Python preflight check is a gate, not a complete audit.

The check also raises a practical design question for agents: should the agent receive the whole project, a filtered context, or only files that passed inspection? A simple preflight script can support that model by reducing what reaches the agent in the first place.

What the Check Does Not Solve

No scanner should be treated as a guarantee. The excerpt does not show whether the script can handle encrypted files, large binaries, compressed archives, unusual encoding, or obfuscated secrets. It also does not show how it distinguishes a test secret from a real secret, or whether it can adapt to different AI providers and agent frameworks.

A hidden injection is especially hard because the boundary between “data” and “instruction” can blur. If an agent is designed to follow text in a file, the file may contain content that affects its behavior even without malicious code. A Python script can flag suspicious patterns, but it cannot know every agent’s interpretation layer.

For this reason, the script is best understood as one layer. It works alongside repository permissions, least-privilege access, secret managers, review workflows, and human oversight.

What to Watch Next

The more interesting question is whether this pattern spreads. Small preflight checks can become common because they are easy to add to a development loop. They can run before a CLI agent starts, before a code snippet is pasted into a model, or before a CI job sends context to an external assistant.

Readers should watch for details that would make the idea more useful: the exact test list, false-positive examples, support for common secret formats, behavior on large repositories, and whether the script can be extended for different agent environments. Without those details, the source gives a clear signal but not a full security specification.

Who should use it / who should skip it

Use the pattern if you work with AI coding tools and need a simple first check before a project is exposed to an agent. The 148-line Python preflight check described by Towards AI is relevant for developers, students, and small teams who want to think about secrets and injection before automation begins.

Skip or delay reliance on it if you need a compliance-grade scanning solution, if your repositories contain many sensitive formats, or if the missing details are not available. The script’s value is its simplicity: a short automated pause before an AI agent is allowed near a project. That pause can be useful, but it should not be mistaken for complete protection.