{"slug": "ai-built-websites-and-seo-hidden-risks-security-issues-technical-problems", "title": "AI-Built Websites and SEO: Hidden Risks, Security Issues & Technical Problems", "summary": "AI-generated websites can look correct in a browser but often contain hidden technical, security, and SEO problems, according to an analysis of AI website builders. The article emphasizes that AI-generated code is not automatically production-ready, and issues such as JavaScript blocking search engines, incorrect canonical URLs, broken links, invalid structured data, poor Core Web Vitals, and security vulnerabilities can arise. Google does not penalize AI use per se, but scaled content abuse and unreviewed output can lead to problems.", "body_md": "# AI-Built Websites and SEO: Hidden Risks, Security Issues & Technical Problems\n\n**AI can build a website in minutes. But can it build one that is technically sound, secure, fast, crawlable, accessible, and genuinely optimized for search?**\n\nThat is a much harder question.\n\nAI website builders and AI coding assistants have changed how websites are created. A business owner can describe a website in natural language, and an AI system can generate the HTML, CSS, JavaScript, React components, API integrations, database queries, metadata, structured data, and even deployment configuration.\n\nThis is extremely powerful.\n\nBut there is an important problem that is often overlooked:\n\nA website that looks correct in a browser is not necessarily a technically correct website.\n\nSearch engines do not evaluate a website exactly like a human visitor does. Google has to crawl URLs, process HTTP responses, render JavaScript, discover links, understand content, evaluate page experience, and determine whether pages are eligible to appear in search results. Google also monitors websites for security problems such as malware, phishing, hacked content, and deceptive behavior.\n\nThat means an AI-generated website can look impressive while quietly containing problems such as:\n\n- JavaScript that prevents search engines from seeing important content\n- incorrect canonical URLs\n- broken internal links\n- missing or incorrect metadata\n- invalid structured data\n- poor Core Web Vitals\n- bloated JavaScript bundles\n- inaccessible navigation\n- incorrect HTTP status codes\n- vulnerable dependencies\n- exposed API keys\n- insecure API endpoints\n- unsafe third-party scripts\n- outdated libraries\n- authentication or authorization mistakes\n- incorrect robots.txt rules\n- accidental\n`noindex`\n\ndirectives - poorly implemented redirects\n- duplicated URLs\n- broken sitemap generation\n- client-side rendering problems\n- hallucinated packages or APIs\n- insecure code that creates opportunities for attackers\n\nThe important point is not that **AI automatically creates bad websites**.\n\nIt doesn’t.\n\nThe real issue is **AI-generated code is not automatically production-ready code**.\n\nAnd when SEO, security, and infrastructure are involved, small mistakes can have very large consequences.\n\n### AI-Generated Websites and SEO: What Is Actually the Problem?\n\nThere is a common misconception that Google penalizes websites simply because their code or content was generated using AI.\n\nThat is not Google’s position.\n\nGoogle’s own documentation says generative AI can be useful for researching topics and adding structure to original content. However, generating large numbers of pages primarily to manipulate search rankings, without adding value for users, can fall under Google’s **scaled content abuse** policy.\n\nSo the question isn’t:\n\n**“Was AI used?”**\n\nThe better questions are:\n\n**“What did AI produce?”**\n\nand\n\n**“Was the result reviewed, tested, secured, and optimized by someone who understands the web?”**\n\nThis distinction is critical.\n\nA developer can use AI to generate a React component, inspect the output, test it, optimize it, run security checks, review dependencies, validate structured data, test crawling, and deploy it safely.\n\nThat can be perfectly reasonable.\n\nOn the other hand, someone can ask an AI:\n\n“Build me a complete SEO-optimized business website.”\n\nThen copy the output directly into production.\n\nThose two workflows are completely different.\n\n## 1. AI Can Generate Code That Works, But “Works” Is Not the Same as “Correct”\n\nLarge language models are extremely good at producing plausible code.\n\nThe problem is that **plausible code is not necessarily correct code**.\n\nAn AI model predicts an implementation based on patterns it has learned. It does not automatically understand every detail of your infrastructure, threat model, database permissions, caching layer, CDN configuration, SEO architecture, or business requirements.\n\nFor example, an AI could generate:\n\n```\n\n```\n\nA human visitor may click it and see the pricing page.\n\nBut from an SEO perspective, this is not equivalent to a normal crawlable link:\n\n```\n\n Pricing\n\n```\n\nGoogle specifically recommends crawlable ``\n\nelements with an `href`\n\nattribute for links. Google can process JavaScript, but relying on unconventional navigation mechanisms can create unnecessary crawling and indexing problems.\n\nThis is one of the most important differences between:\n\n**“The website works.”**\n\nand\n\n**“The website works correctly for users, search engines, accessibility tools, monitoring systems, and security scanners.”**\n\n## 2. JavaScript Can Become an SEO Problem\n\nModern AI-generated websites frequently use frameworks such as React, Next.js, Vue, or other JavaScript-heavy architectures.\n\nThere is absolutely nothing inherently wrong with this.\n\nGoogle can process JavaScript.\n\nHowever, Google’s own documentation explains that JavaScript SEO involves a process of **crawling, rendering, and indexing**, and that rendering introduces additional complexity. Google also recommends server-side or pre-rendering because it can make websites faster for users and crawlers, and not every bot can execute JavaScript.\n\nConsider a page where the initial HTML contains:\n\n```\n\n```\n\nThe actual product description, title, links, and content are inserted later using JavaScript.\n\nA browser may eventually display everything correctly.\n\nBut now the search engine has an additional rendering dependency.\n\nIf the JavaScript fails because of:\n\n- a runtime exception\n- blocked resources\n- incorrect environment variables\n- a failed API request\n- an incompatible package\n- a broken build\n- a CDN problem\n- incorrect caching\n- a server error\n\nthe content Google sees may be substantially different from what the visitor sees.\n\nGoogle explicitly notes that JavaScript-powered websites require additional considerations and recommends testing rendered HTML.\n\n#### The technical SEO chain looks like this:\n\n```\nUser requests URL\n ↓\nWeb server returns HTML\n ↓\nGooglebot crawls page\n ↓\nGoogle discovers resources\n ↓\nJavaScript is rendered\n ↓\nRendered HTML is processed\n ↓\nContent and links are extracted\n ↓\nPage can be indexed\n```\n\nEvery additional dependency introduces another possible failure point.\n\nThis doesn’t mean:\n\n**JavaScript = bad SEO.**\n\nIt means:\n\n**Unnecessarily complex JavaScript = more things that need to be tested.**\n\n## 3. AI Can Accidentally Create Poor Internal Linking\n\nInternal links are one of the basic building blocks of a crawlable website.\n\nGoogle explains that links help it discover new pages and understand relationships between pages. It recommends normal crawlable ``\n\nlinks and descriptive anchor text.\n\nAn AI-generated website might instead create navigation through:\n\n- JavaScript click handlers\n- buttons pretending to be links\n- dynamically generated routes\n- modal-based navigation\n- hash fragments\n- client-side state\n- URLs that only exist after JavaScript execution\n\nFor example:\n\n```\nnavigate(\"/hosting\");\n```\n\nmay work perfectly inside the application.\n\nBut a conventional:\n\n```\nWeb Hosting\n```\n\nis much easier for crawlers and other systems to understand.\n\nGoogle also recommends using proper URL structures and warns against using URL fragments to change page content.\n\n## 4. AI Can Generate Broken Metadata\n\nSEO isn’t just about putting keywords into an article.\n\nTechnical metadata matters too.\n\nAn AI-generated page might accidentally produce:\n\n```\nHome\n```\n\nfor 200 different pages.\n\nOr it might generate:\n\n```\n\n```\n\non a page that should be indexed.\n\nIt could also generate:\n\n- duplicate titles\n- missing meta descriptions\n- incorrect canonical URLs\n- canonical URLs pointing to the wrong domain\n- incorrect language attributes\n- incorrect hreflang relationships\n- duplicate Open Graph metadata\n- incorrect structured data\n- invalid JSON-LD\n- incorrect sitemap URLs\n\nThese mistakes don’t necessarily cause a dramatic Google penalty.\n\nInstead, they can make it harder for search engines to understand and index the site correctly.\n\nAnd that is often much harder for a non-technical website owner to notice.\n\n## 5. AI Can Generate Bloated Code That Hurts Performance\n\nOne of the biggest problems with AI-generated websites is code bloat.\n\nAsk an AI to create an animated landing page and it may produce:\n\n- multiple animation libraries\n- large JavaScript bundles\n- unnecessary dependencies\n- duplicate CSS\n- excessive DOM elements\n- multiple third-party scripts\n- large images\n- client-side effects\n- unnecessary API calls\n\nThe result can look fantastic.\n\nBut the browser has to download, parse, execute, and render all of that code.\n\nGoogle’s Core Web Vitals measure real-world loading performance, responsiveness, and visual stability. Google recommends good Core Web Vitals as part of providing a strong page experience. The key metrics include LCP, INP, and CLS.\n\nFor example, Google currently recommends aiming for:\n\n**LCP:** 2.5 seconds or less**INP:** less than 200 ms**CLS:** less than 0.1\n\nAn AI-generated website can easily turn a simple page into a JavaScript-heavy application when a much simpler HTML/CSS implementation would have been sufficient.\n\n## 6. AI Can Introduce Vulnerable Dependencies\n\nThis is where the conversation becomes much more serious.\n\nA modern website is rarely just your own code.\n\nIt may depend on dozens or hundreds of packages.\n\nFor example:\n\n```\nNext.js\nReact\nExpress\nAxios\nAuthentication library\nDatabase driver\nImage processing library\nPayment SDK\nAnalytics package\nUI library\nCSS framework\nMarkdown parser\n```\n\nAI coding assistants can suggest packages and versions.\n\nBut an AI model may recommend:\n\n- outdated packages\n- deprecated libraries\n- vulnerable versions\n- packages that don’t exist\n- incorrect APIs\n- insecure configuration\n\nOWASP specifically warns that AI coding assistants may suggest outdated dependency versions with known CVEs, hallucinated packages, or flawed dependencies. It recommends dependency auditing and checking AI-suggested packages against vulnerability databases.\n\nThis creates an important security principle:\n\nNever treat AI-generated dependencies as trusted dependencies.\n\nA production build should still go through normal security processes.\n\nFor example:\n\n```\nnpm audit\n```\n\ncan identify known vulnerabilities in npm dependencies.\n\nOrganizations can also use tools such as:\n\n```\nDependabot\nOSV\nGitHub Advisory Database\nSnyk\nOWASP dependency checks\n```\n\nThe exact tooling isn’t as important as the principle:\n\n**Every dependency should be verified.**\n\n## 7. AI Can Generate Insecure Code\n\nThis is one of the most important reasons an AI-generated website needs human review.\n\nSuppose someone asks an AI:\n\n“Create an API endpoint that returns customer information.”\n\nThe model may produce something functional.\n\nBut does it correctly implement:\n\n- authentication?\n- authorization?\n- rate limiting?\n- input validation?\n- output filtering?\n- CSRF protection?\n- logging?\n- access control?\n- encryption?\n- error handling?\n\nThe answer is not automatically yes.\n\nOWASP has specifically documented the risks of relying on AI-generated code without adequate security expertise. It notes that AI-generated code can lack critical security controls and can result in exposed data, unprotected endpoints, exposed secrets, and insecure configurations.\n\nOWASP’s guidance on AI-assisted coding also emphasizes that passing tests does not prove security and that human accountability remains necessary.\n\n## 8. Could AI-Generated Code Make Google Flag a Website as Unsafe?\n\n**Potentially, but this needs to be explained correctly.**\n\nGoogle does not say:\n\n“AI-generated code = unsafe website.”\n\nThere is no general Google rule that automatically marks AI-built websites as dangerous.\n\nThe actual problem is this:\n\n**If AI-generated code introduces a security vulnerability, malicious script, deceptive behavior, compromised dependency, exposed endpoint, or other harmful content, the resulting website can potentially develop a real security problem.**\n\nGoogle’s Security Issues documentation says that if Google determines a site has been hacked or exhibits behavior that could harm visitors, it can show security warnings. These can include hacked content, malware, unwanted software, and social engineering.\n\nGoogle also explains that compromised websites can contain injected malicious JavaScript or iframes.\n\nFor example:\n\n```\n\n```\n\nor:\n\n```\n\n```\n\nwould obviously be a major security problem.\n\nThe problem becomes even more dangerous if the malicious code is injected after deployment through an exploited vulnerability.\n\n## 9. A Vulnerability Is Not the Same Thing as Google’s Security Warning\n\nThis distinction is important.\n\nImagine an AI-generated application contains a vulnerable dependency.\n\nThat does **not** automatically mean Google will immediately display:\n\n“This site is dangerous.”\n\nA vulnerability and an active security issue are different things.\n\nThe chain could look like this:\n\n```\nAI-generated code\n ↓\nSecurity vulnerability\n ↓\nAttacker discovers vulnerability\n ↓\nWebsite gets compromised\n ↓\nMalicious code/content is injected\n ↓\nGoogle detects harmful behavior\n ↓\nSecurity warning\n```\n\nThe critical step is the compromise or harmful behavior.\n\nGoogle’s Security Issues report can identify hacked content, malware, unwanted software, and social engineering. Pages affected by security issues can receive warning labels in search or browser interstitials.\n\nSo it would be inaccurate to claim:\n\n**“AI-generated code will get your website flagged by Google.”**\n\nA technically accurate statement is:\n\n**“Unreviewed AI-generated code can introduce security vulnerabilities, and a compromised website can subsequently trigger Google’s security systems.”**\n\nThat is a much stronger argument because it is actually defensible.\n\n## 10. Google Can Detect Hacked or Dangerous Websites\n\nGoogle doesn’t only look at your homepage.\n\nIts security systems can identify various types of harmful behavior.\n\nGoogle describes security issues including:\n\n#### Hacked content\n\nContent added to a website without the owner’s permission because of security vulnerabilities.\n\n#### Malware\n\nWeb-based malicious software that can operate without explicit user action.\n\n#### Social engineering\n\nPages designed to trick users into doing something dangerous, such as revealing passwords or downloading malicious software.\n\n#### Harmful downloads\n\nFiles that Google determines may contain malware or unwanted software.\n\nGoogle says these problems can result in warnings in Search or browsers.\n\nThis is why website security is also indirectly connected to SEO.\n\nA website that becomes compromised isn’t just dealing with a developer problem.\n\nIt can become a:\n\n**traffic problem + reputation problem + conversion problem + SEO problem.**\n\n## 11. AI Can Accidentally Expose API Keys and Secrets\n\nAnother serious risk is secrets management.\n\nImagine an AI-generated application contains:\n\n``` js\nconst API_KEY = \"sk_live_xxxxxxxxx\";\n```\n\ninside frontend JavaScript.\n\nThat key may be visible to anyone who opens the browser’s developer tools.\n\nThe same problem can occur with:\n\n```\n.env\n.env.production\nAWS credentials\ndatabase passwords\nprivate API tokens\nJWT secrets\nservice-account credentials\n```\n\nOWASP specifically recommends preventing AI coding tools from accessing sensitive files and warns that AI coding assistants can receive broader project context than developers may realize.\n\nA good production architecture should separate:\n\n```\nPUBLIC CLIENT\n ↓\nPUBLIC API\n ↓\nAUTHENTICATED SERVER\n ↓\nPRIVATE SERVICES\n ↓\nDATABASE\n```\n\nrather than putting private credentials directly into browser-accessible code.\n\n## 12. AI Can Hallucinate APIs and Packages\n\nOne of the more unusual problems with AI-generated software is that the code can look completely legitimate while referencing something that doesn’t actually exist.\n\nFor example, an AI might produce:\n\n``` js\nimport { optimizeSEO } from \"super-seo-engine\";\n```\n\nThe code looks reasonable.\n\nBut the package may not exist.\n\nOr it might use an API that existed several years ago but has since changed.\n\nOWASP explicitly warns that AI coding assistants can hallucinate packages, libraries, and APIs, which can lead to build failures, runtime errors, or security weaknesses.\n\nThis is why:\n\n**“The AI wrote it” is not documentation.**\n\nEvery dependency and API should be verified against authoritative documentation.\n\n## 13. AI-Generated Content Creates a Separate SEO Problem\n\nThere is another side to AI websites: the content itself.\n\nImagine someone generates:\n\n```\n500 blog posts\n1,000 location pages\n300 service pages\n200 product descriptions\n```\n\nin a few hours.\n\nThe website suddenly has thousands of URLs.\n\nBut if those pages provide little original value, the website may have created an SEO liability rather than an SEO advantage.\n\nGoogle explicitly says that generating many pages with AI without adding value for users can violate its scaled content abuse policy. The policy applies regardless of whether the content was generated by AI, humans, or another automated system.\n\nThe problem isn’t:\n\n**AI content.**\n\nThe problem is:\n\n**mass-produced, low-value content created primarily to manipulate search visibility.**\n\n## 14. More Pages Do Not Automatically Mean More SEO Traffic\n\nThis is another misconception worth killing.\n\nConsider two websites.\n\n#### Website A\n\n```\n10,000 AI-generated pages\nLittle original information\nSimilar templates\nWeak internal linking\nNo real expertise\nLittle editorial review\n```\n\n#### Website B\n\n```\n100 high-quality pages\nOriginal research\nExpert insights\nStrong internal linking\nClear information architecture\nGood technical SEO\nFast performance\nReal user value\n```\n\nWebsite A has 100 times more pages.\n\nThat doesn’t mean it has 100 times more SEO potential.\n\nGoogle’s current guidance for generative AI search specifically emphasizes valuable, non-commodity content and says that high quantity alone does not make a website higher quality or more relevant.\n\n## 15. AI Websites Can Also Fail Accessibility\n\nSEO and accessibility aren’t identical, but there is significant overlap.\n\nAI-generated interfaces may produce:\n\n```\n