{"id":10728,"date":"2026-04-18T10:50:00","date_gmt":"2026-04-18T07:50:00","guid":{"rendered":"https:\/\/cmimmio.com\/?p=10728"},"modified":"2026-04-16T12:29:29","modified_gmt":"2026-04-16T09:29:29","slug":"vibecoding-security","status":"publish","type":"post","link":"https:\/\/cmimmio.com\/fi\/2026\/04\/18\/vibecoding-security\/","title":{"rendered":"Vibekoodauksen tietoturva: 7 kriittist\u00e4 riski\u00e4, jotka jokaisen tekij\u00e4n on korjattava ennen julkaisua"},"content":{"rendered":"

Vibecoding security is the conversation the vibecoding community is not having loudly enough. We talk about speed, tools, prompts, and launches. We talk about what to build and how to position it. What we don’t talk about nearly enough is what happens when someone tries to break what we’ve built, or when the AI that built it quietly leaves the door unlocked.<\/p>\n\n\n\n

This post covers three things every vibecoder needs to understand: why the threat landscape has shifted in ways that directly affect vibe-coded products, what the specific vibecoding security risks in AI-generated code actually are, and the practical tools and prompts you can use to protect what you build.<\/p>\n\n\n\n

\"Marketer<\/figure>\n\n\n\n

How Hackers Are Using AI Agents to Attack Faster<\/h2>\n\n\n\n

Here is the uncomfortable symmetry at the heart of 2025 and 2026 cybersecurity: the same AI agent capabilities that make vibecoding powerful also make attacks faster, cheaper, and more scalable.<\/p>\n\n\n\n

According to cybersecurity researchers at Palo Alto Networks<\/a>, AI agents will be one of the biggest new attack vectors for cybercriminals in 2026. Their prediction is specific: the ongoing cybersecurity skills gap will lead companies to deploy AI agents at scale, and attackers will follow, switching focus from human operators to the agents themselves. An agent that is always on and never tired is also always available to be compromised.<\/p>\n\n\n\n

The Lakera research team<\/a> analysed real attacker behaviour across production systems in Q4 2025. Their finding was stark: as soon as AI agents began interacting with anything beyond simple text, including documents, tools, and external APIs, the attack surface expanded, and adversaries adapted immediately.<\/p>\n\n\n\n

The four most relevant attack techniques for vibecoded products:<\/p>\n\n\n\n

Prompt injection.<\/strong> Attackers embed malicious instructions in data the agent will process, such as a support ticket, a file, a URL, or even a code comment, and the agent follows those instructions as if they came from you. The EscapeRoute vulnerability (CVE-2025-53109) in Anthropic’s MCP file server allowed arbitrary file reading and writing simply by injecting instructions into content the agent processed.<\/p>\n\n\n\n

Memory poisoning.<\/strong> In agentic systems with persistent memory, an attacker implants false instructions that the agent stores and recalls later. Lakera demonstrated cases where injected memory persisted for weeks, causing agents to route payments to attacker-controlled addresses when legitimate invoices arrived.<\/p>\n\n\n\n

Supply chain attacks.<\/strong> When an AI code generator recommends a package that doesn’t exist, or misnames one, an attacker can register that package name and publish malicious code under it. Vibecoding tools also tend to leave dependency versions unpinned, meaning a compromised update can enter your project silently and without any code change on your side.<\/p>\n\n\n\n

Tool misuse and privilege escalation.<\/strong> Palo Alto Networks described the risk plainly: with a single well-crafted prompt injection, an adversary has an autonomous insider at their command, one that can silently execute trades, delete backups, or pivot to exfiltrate the entire customer database.<\/p>\n\n\n\n

According to MIT Technology Review<\/a>, AI agents are much cheaper than hiring professional hackers and can orchestrate attacks at a far greater scale than humans. One security expert put it directly: “If I can reproduce an attack once, it’s just a matter of money for me to reproduce it 100 times.”<\/p>\n\n\n\n

7 Vibecoding Security Risks Found in AI-Generated Code<\/h2>\n\n\n\n

Before worrying about external attackers, the more immediate vibecoding security risk for most products is simpler: the code itself.<\/p>\n\n\n\n

A Veracode study<\/a> testing over 150 AI models found that 45% of generated code introduces OWASP Top 10 vulnerabilities. That figure has barely moved in two years, despite enormous improvements in model quality. The code compiles and runs. It just isn’t safe.<\/p>\n\n\n\n

The Tenzai research<\/a> from December 2025 tested five major vibecoding tools, including Claude Code, Cursor, Replit, and Devin, building the same three applications with each. They found 69 vulnerabilities across 15 apps, including critical-rated flaws. A separate Escape.tech study of 5,600 vibecoded applications<\/a> found over 2,000 vulnerabilities, more than 400 exposed secrets, and 175 instances of exposed personal data.<\/p>\n\n\n\n

Here are the seven vibecoding security risks that appear most consistently:<\/p>\n\n\n\n

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  1. Hardcoded credentials.<\/strong> AI assistants frequently write database passwords, API keys, and tokens directly into source code. Anyone with read access to your codebase, or a GitHub repository accidentally made public, can read them.<\/li>\n\n\n\n
  2. Missing rate limiting.<\/strong> A DryRun Security study from March 2026 found that AI tools wrote rate-limiting code but consistently failed to connect it to the application. The safety net existed in the files. It simply didn’t work.<\/li>\n\n\n\n
  3. No security headers.<\/strong> In the Tenzai study, not one of the 15 apps set any security headers. Content-Security-Policy, Strict-Transport-Security, X-Frame-Options: single-line configurations that browsers enforce were absent in every single application tested.<\/li>\n\n\n\n
  4. Broken authentication flows.<\/strong> AI-generated login systems frequently hash passwords using weak algorithms, store tokens insecurely, or fail to implement timing-attack protections. The code looks correct. It isn’t.<\/li>\n\n\n\n
  5. Server-Side Request Forgery (SSRF).<\/strong> When AI builds features that fetch URLs, such as link previews, image proxies, or webhooks, it makes the server request whatever URL a user provides, including internal cloud metadata endpoints that expose full infrastructure credentials.<\/li>\n\n\n\n
  6. Vulnerable or hallucinated dependencies.<\/strong> AI tools regularly suggest packages that are deprecated, carry known CVEs, or, in some cases, don’t exist. An attacker can register a hallucinated package name and publish malicious code under it.<\/li>\n\n\n\n
  7. Business logic vulnerabilities.<\/strong> The Tenzai researchers noted that AI agents lack the common sense that human developers bring intuitively about how workflows should operate. The result is logic errors that automated scanners miss entirely, and that only appear when a real user tests an edge case.<\/li>\n<\/ol>\n\n\n\n

    The Moltbook incident<\/a> illustrated how quickly this compounds. The AI-agent social network made headlines in early 2026, until security firm Wiz found that a misconfigured Supabase database had exposed 1.5 million API keys and 35,000 user email addresses. The root cause wasn’t a sophisticated attack. It was vibecoding’s speed-first defaults with no security check before go-live.<\/p>\n\n\n\n

    How to Secure Your Vibecoded Product: Prompts That Help<\/h2>\n\n\n\n

    Vibecoding security doesn’t require becoming a security engineer. It requires building security prompts into your workflow before you ship.<\/p>\n\n\n\n

    Use a Security System Prompt From the Start<\/h3>\n\n\n\n

    Before writing a single feature prompt, give your vibecoding environment a standing security brief. In Cursor, this goes in your .cursorrules<\/code> file. In Lovable or Bolt, include it at the start of your project prompt. The principle is the same: you are setting rules the AI must follow before generating code.<\/p>\n\n\n\n

    A starting template you can copy directly:<\/p>\n\n\n\n

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    Before writing any code, apply the following security requirements without exception: never hardcode credentials, always use environment variables. Validate and sanitise all user inputs before processing. Use parameterised queries for all database interactions. Implement rate limiting on all authentication endpoints. Never return raw error messages to users. Set all standard security headers (Content-Security-Policy, Strict-Transport-Security, X-Frame-Options, X-Content-Type-Options). Store all secrets in .env files and confirm they are listed in .gitignore. After generating code, check it against the OWASP Top 10 before presenting it.<\/em><\/p>\n<\/blockquote>\n\n\n\n

    Vibecoding Security Follow-Up Prompts to Run After Every Feature<\/h3>\n\n\n\n

    Don’t accept AI-generated code as done. Run a second pass with targeted security questions:<\/p>\n\n\n\n