Vibe coding is fast. Here’s what it’s quietly breaking.
Vibe coding, using plain-language prompts to generate code with AI, has made building software genuinely faster. The speed is real. So are the holes it punches in your security posture.
The core problem: AI coding assistants optimize for code that compiles and appears to work. Security is a non-functional requirement, and these models have no concept of threat models. They predict the next token. They do not evaluate risk.
What the AI Gets Wrong, Specifically
The vulnerabilities that show up most often in AI-generated codebases follow a consistent pattern:
- Hardcoded secrets. AI frequently suggests API keys and credentials as placeholders to make examples functional. Developers accept them, commit them, and those values persist in version control history even after deletion.
- Broken authorization. Models tend to check whether a user is logged in, but skip the check for whether that user is actually allowed to access a specific resource. Identity verification without authorization is not security.
- Hallucinated dependencies. AI sometimes suggests packages that do not exist in public registries. If a malicious actor registers that package name first, every developer who accepted that suggestion installs the attack.
- Insecure defaults. Disabled SSL verification, permissive CORS settings, and debug modes left on. These appear more frequently in source examples, so the model reproduces them.
- Missing input validation. Raw user input passed directly to a database or rendering engine without sanitization. SQL injection and XSS follow.
️ What Actually Helps
According to Ankur Tyagi from CodeRabbit, vibe coding as the default risks sidelining the deeper thinking that makes systems resilient. The fixes do not require slowing down, but they do require intentional process.
- Threat models before prompts. Define trust boundaries and data scopes before you let the AI generate anything that touches auth, payments, or data access. Guardrails like mandatory parameterized queries and explicit denial of unapproved external network calls belong in the prompt itself.
- Scan in the IDE, not at the end. Traditional end-of-pipeline security checks are too slow for vibe coding workflows. Static application security testing and software composition analysis need to run inside the IDE and in CI so problems surface before they reach production.
- Manual review for sensitive paths. Natalie Tischler from Veracode puts it directly: never trust code just because a machine wrote it. In fact, scrutinize it more. Authentication logic, payment processing, and data access controls should not skip human review.
- Security-aware prompting. Treat the AI like a junior engineer who needs explicit constraints. Prompts that include instructions like “use parameterized queries to prevent SQL injection” or “validate all user input against an allowlist” produce measurably safer output than open-ended generation.
Legit describes the goal as keeping the speed of vibe coding while cutting the noise and shrinking exposure across the software development lifecycle. That combination is achievable, but only if scanning and review are built into the workflow from the start, not bolted on after a breach.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
With over 14 years in tech, marketing, and AI, including my role as Head of SEO at CitizenShipper and co-founder of TaleBot at Intale AI, I’m dedicated to sharing genuine, useful product insights and tips.
At BizStack, I aim to cut through the digital noise to provide clear, actionable advice.
And more than all else, I’m a father to a (always) little girl and a husband.
Contact me at [email protected] for assistance.
