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A working demo proves that a feature behaved as expected in one situation; it does not prove the app protects accounts, private data, or the computer and credentials used to build it. If you use an AI coding assistant without a developer background, treat the result as a draft that needs security checks—not as production-ready software. OWASP guidance documents several ways seemingly functional AI-assisted apps and workflows can still go wrong.
The title’s first-person framing suggests personal incidents, but no author experience is established here. The risks below are practical, documented classes of failure, not claims about something I personally encountered or about every AI tool.
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Why a working demo can still be unsafe
A prompt often describes what a feature should do on screen—such as showing a login form—without spelling out the protections behind it. The result may work while omitting password safeguards, session protections, rate limits, account separation, or secure recovery. OWASP’s citizen-development guidance puts the distinction plainly: “It won’t, however, automatically include security best practices that weren’t explicitly requested.” That is a warning about what may be omitted, not proof that every generated login is vulnerable.
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A page loading and a happy-path test passing tell you little about what happens when someone uses the wrong account, changes an identifier, submits unexpected input, or tries an action they should not be allowed to perform. Security requirements need to be stated, checked, and tested; they do not follow automatically from a convincing demo.
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Authentication is not authorization
Authentication answers “Who signed in?” Authorization answers “What is this account allowed to see or change?” An app can identify a user correctly and still fail to check whether that user has permission to access a particular record or action.
For example, imagine an app whose URL includes a record number. If changing that number lets one signed-in user view another person’s record, the login screen has not protected the data. This is an illustrative scenario, not a report of a measured frequency. OWASP identifies missing authorization checks on sensitive endpoints as a risk, and its citizen-development guidance describes public endpoints and anonymous access to sensitive information as plausible failures.
Think through permissions for each important action and record: who can view, create, edit, delete, or export it? Test with separate accounts, including attempts to access another user’s data. A check performed only in the interface may not protect the underlying endpoint.
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A coding assistant may receive more than the text you type into a prompt. Depending on the service and its settings, it may use open files, project structure, terminal output, or other context to help generate code. That material can include API keys, customer information, internal business logic, or details about how a system is arranged.
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Before using a tool with sensitive material, check its current terms and settings and follow your organization’s policies. Do not paste live credentials, production data, or confidential code unless the selected service and applicable policy explicitly allow it. Do not assume that one tool’s data handling rules apply to another.
A package suggested by AI still needs verification
An assistant can suggest a package name that sounds plausible but does not correspond to a legitimate package. Someone else may register that name and publish malicious code; blindly running the suggested install command can therefore introduce a supply-chain risk. OWASP describes this kind of AI-assisted package-name deception as “slopsquatting.”
- Verify the exact package name on the relevant public registry before installing it.
- Review its publication history and maintainer signals, and use a trusted source.
- Be cautious of a name that is unfamiliar or differs subtly from a package you intended to use.
These are checks, not a guarantee that a package is safe. The guidance cited here does not establish an incidence rate, so there is no sound basis for claiming how often this happens.
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Code completion proposes text. An AI agent may also be allowed to run shell commands, edit files, install software, use network access, or interact with repositories and other tools. Its potential impact depends on the specific tool, version, settings, and permissions you grant—not merely on whether it is called an agent.
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The more authority it has, the more damage an unsafe instruction or mistaken action could cause. Material the agent reads, including repository content, may also influence what it does. OWASP recommends limiting tools and network access, using a sandbox, minimizing extensions and permissions, using short-lived scoped credentials, and requiring human approval for high-impact actions.
- Limit access to the files, commands, networks, and accounts the task actually needs.
- Use an isolated or restricted environment where feasible rather than granting unrestricted access to your everyday machine.
- Keep credentials narrowly scoped and short-lived instead of giving an agent broad, persistent access.
- Review consequential commands and changes before allowing them to run or take effect.
When comparing setups, ask what the system can actually access and do. A completion tool with no execution rights is different from an agent that can install dependencies or deploy changes. Settings such as automatic approval can change the risk substantially.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Tests and scans provide evidence, not a safety certificate
Tests can show that specified cases pass; scanners can flag patterns, exposed secrets, vulnerable dependencies, or configuration problems within their scope. Neither proves that every permission boundary, privacy expectation, or business rule is correct. OWASP cautions against relying on AI-generated tests alone and treats IDE feedback as complementary to more authoritative continuous-integration scans, not a replacement for them.
Ask what a check actually inspects: source code, dependencies, secrets, or deployed configuration? Confirm that checks run before deployment and that a person reviews important findings. A generated explanation or clean scan can support a review, but cannot certify the whole app as safe.
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The maintainability risk: not knowing what changed
A less visible practical risk for a non-developer is becoming dependent on code you cannot explain or safely modify. This is an inference rather than a quantified finding in the cited security guidance: if you cannot identify what changed, how data moves, or where secrets are stored, debugging a later problem or handing the app to someone else will be harder.
Keep a simple record of major changes, new dependencies, data flows, and where configuration and secrets are handled. If you cannot explain a change well enough to decide whether it belongs in the app, pause before accepting it.
Before you share or deploy
Use this checklist before exposing an app to other people, especially if it handles accounts, private records, payments, uploads, location, or other sensitive information:
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- Keep live secrets, customer data, and confidential code out of prompts unless the tool’s approved settings and policy permit their use.
- Verify each new package and its source before installing it.
- Restrict an agent’s file, command, network, and account permissions; use a sandbox and approve consequential actions.
- Test who can view, edit, delete, or export each important record, including attempts to access another user’s data.
- Get an experienced developer or security professional to review the app before exposing sensitive data or relying on it in production.
The higher the consequences of a mistake, the more important an independent review becomes. OWASP’s Secure Coding with AI Cheat Sheet states: “The developer who accepts and commits the code does.” In practice, that means a person must own the decision to use generated changes, rather than treating the assistant’s output as self-approving.
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