Artificial intelligence is transforming the way software is built. With a few simple prompts, you can now create code, APIs, or entire applications in minutes. The approach, popularized as “vibe coding”, promises to make development faster, more creative, and more accessible than ever.
But as the industry embraces this new rhythm of building software, cybersecurity professionals are sounding the alarm: the faster code is written, the faster essential security controls can vanish.
What Is Vibe Coding?
The term vibe coding describes a new AI-assisted development style where developers “prompt” instead of “program.” Instead of meticulously writing functions and managing dependencies, they describe what they want – and the AI generates the implementation.
It’s a revolution in productivity. However, in this new workflow, security awareness and technical scrutiny often get replaced by trust in the model’s output. Developers (and more alarmingly, non-technical people) may skip code reviews, testing, or architecture validation, assuming the AI “knows what it’s doing.”
That’s where trouble begins.
When Speed Outpaces Security
The promise of AI-assisted development is irresistible: faster releases, fewer bottlenecks, and instant innovation. But recent incidents show what happens when that speed comes at the expense of security.
Recently, Tea, a women-only dating app built on AI-generated infrastructure, suffered two massive data leaks that exposed personal details, private chats, and location data. The vulnerabilities were traced to unsecured APIs and misconfigured storage – classic signs of systems deployed before proper validation and testing could catch up.
Soon after, Enrichlead, a lead-generation platform, boasted that its entire codebase was written by AI coding platform with zero human involvement. Within days, security researchers found critical flaws that allowed anyone to unlock premium features and modify user data. Patches failed, and the platform eventually shut down – not because of malice, but because the code had been written too fast for anyone to understand or secure.
These failures are a stark reminder of why traditional development disciplines exist in the first place. Decades of hard-earned lessons gave rise to security guardrails like peer review, authentication checks, testing, and documentation. Each one is designed to catch the kinds of oversights that AI-generated code now reproduces at unprecedented speed.
Vibe coding shortcuts these layers by design. The AI writes faster than humans can verify, and every “simplified” configuration can quietly remove an essential security barrier.
The result? A cascade of failures, like the ones highlighted by Boris Goncharov, Chief Security Officer at AMATAS, during a recent cybersecurity event.
A Cascade of Failures: How AI Coding Shortcuts Break Security
AI-assisted development promises simplicity, but every “helpful” simplification can quietly remove a critical layer of defense. Here’s how a small compromise in design or logic can snowball into a full-scale security breakdown.
1. Weak Integration Security
Simplified AI-generated webhooks or API connections often skip basic protection mechanisms like request signing, nonce validation, or timestamping. Without these controls, integrations are open to replay attacks and unauthorized requests – exposing internal systems to anyone who can capture or mimic a valid request.
2. Authentication Drift
As AI tools aim to make systems “just work,” they may strip away multi-factor authentication or replace secure flows (like OAuth) with basic password-based or even anonymous access. Each shortcut removes a security layer – until what began as a quick test environment evolves into production code with no authentication at all.
3. Secrets Exposure
AI-generated examples often include hardcoded API keys, credentials, or tokens to make code “immediately functional.” If the user re-uses or deploys this code without modification, these secrets become embedded across pipelines, debug logs, and repositories – offering attackers instant access.
4. Access Control Erosion
To fix permissions errors or speed up collaboration, developers may prompt AI tools to “allow access” or “make it public.” AI follows the instruction literally, sometimes removing or bypassing entire authorization checks. This leads to excessive privileges, data leakage, or exposure of internal endpoints to the public internet.
5. Missing Input Validation
AI-generated applications frequently lack proper sanitization of user input. When validation is omitted, attackers can inject malicious SQL statements, scripts, or payloads – a classic vulnerability now reproduced at AI speed. These flaws often go unnoticed until exploited in production.
6. Invisible Compliance Gaps
When systems are deployed with minimal authentication or logging, compliance becomes impossible. AI-generated components rarely include audit trails, retention policies, or traceability by default – making GDPR, DORA, or NIS2 compliance unachievable and post-incident forensics nearly impossible.
Why This Matters for Business Leaders
When AI accelerates coding, risk management must accelerate too. Organizations adopting AI tools without new controls risk undermining their entire compliance posture. The same models that improve speed can silently introduce unreviewed dependencies, unsafe defaults, and untraceable configurations.
For CISOs, CTOs, and IT Directors, this means re-evaluating the secure software development lifecycle (SDLC) to include:
- AI-generated code review requirements
- Automated vulnerability scanning
- Secrets management enforcement
- Documentation of AI involvement in the development process
Securing the Vibe
At AMATAS, we advocate for a balance between innovation and governance – empowering teams to adopt AI responsibly without sacrificing resilience.
Here are our recommendations for secure development in the age of vibe coding:
- Keep humans in the loop: Every AI-generated change must be reviewed by a qualified engineer or security officer before deployment.
- Automate static and dynamic analysis: Integrate SAST, DAST, and dependency scanning tools into your CI/CD pipelines to catch vulnerabilities early.
- Secure secrets and keys: Use vaults or encrypted secrets management solutions – never store credentials in code, even for testing.
- Audit and log everything: Ensure authentication, authorization, and access logs are retained for compliance and forensic visibility.
- Engage vCISO oversight: Virtual CISO services can help tailor governance models and update policies to account for AI workflows.
- Test like attackers do: Regular penetration testing remains essential – especially for AI-augmented codebases that evolve faster than manual review cycles.
- Educate employees (developers and non-technical roles) continuously: Managed security awareness programs keep teams up to date with emerging AI risks and defensive coding practices.
These practices create a feedback loop of trust – letting businesses innovate safely while protecting assets, customers, and reputation.
Innovation Needs Guardrails
AI has changed the way we write software, but not the fundamental rule of cybersecurity: speed without control leads to compromise.
Vibe coding can empower creativity and productivity, but without governance, it risks becoming the next generation’s “shadow IT.” The same simplicity that makes it appealing also makes it dangerous.
At AMATAS, we help organizations embrace new technologies securely – combining expertise, automation, and strategic oversight to turn innovation into a sustainable advantage.
Because when AI starts coding by vibe, make sure your security doesn’t start living on a feeling.
