Vibe coding promises to automate IT workflows without writing code. You describe what you need, AI generates the script, and you're done. But IT workflow security takes a hit when teams can’t audit or validate what the AI actually built. Testing in December 2025 found 69 vulnerabilities across five popular vibe coding tools, including half a dozen critical flaws. When developers write code manually, they can test and verify security controls. With vibe coding, the code appears fully formed, and teams trust an AI model to understand infrastructure, compliance requirements, and sensitive data handling without validation.
TLDR:
Vibe coding generates code from text prompts but creates security gaps in IT workflows.
69 vulnerabilities found in December 2025 testing across five vibe coding tools.
41% of all code written in 2024 was AI-generated, often deployed without security review.
Pre-built workflow nodes eliminate generated code risks with tested, auditable components.
Ravenna automates IT workflows using visual builders instead of vibe-coded scripts.
What Vibe Coding Is and Why It Matters for IT Security
Vibe coding is a form of AI-assisted development that generates code by describing what you want in plain language. You type "create a script that resets user passwords and sends a notification," and an AI tool writes the code for you. The issue is that you’re trusting an AI model to make security decisions without understanding your infrastructure, compliance requirements, or the sensitive data your workflows handle. Testing in December 2025 assessed five vibe coding tools across 15 applications and found 69 security vulnerabilities in the generated code. Roughly 45 were rated low to medium severity. Half a dozen were critical.
When developers write code manually, they can audit, test, and verify security controls. With vibe coding, the code appears fully formed, and most IT teams lack the expertise to identify what's wrong with it. And this is made worse by how AI agents are trained to code.
The Scale of Vibe Coding Adoption in IT Workflows
92% of US developers now use AI coding tools daily, with 82% globally using them weekly. In 2024, 41% of all code written globally was AI-generated, totaling 256 billion lines, much of it deployed without rigorous security testing or application security review. For IT teams managing internal workflows, every automated workflow built with vibe coding adds another potential entry point. Scripts that provision access, manage identities, or handle sensitive employee data expand the attack surface when security controls are assumed rather than verified. Adoption has outpaced most IT departments’ ability to audit what’s being deployed.
And that creates some serious security risks, especially when the very agents who are doing the coding are trained on bad code.
When Agents Code For You, They Bake In All Of Their Bad Habits
AI models train on massive code repositories where secure coding practices and insecure patterns sit side-by-side. When they generate workflows, they reproduce the same mistakes developers have been making for years. In fact, over 40% of AI-generated code contains security flaws, with missing input sanitization ranking as the most frequent vulnerability. Vibe coding doesn't create new vulnerability types. It accelerates how quickly old ones spread through your IT workflows.
Authentication bypasses appear when workflows skip verification steps. An AI-generated script might check if a user exists before granting access but fail to verify whether they should have that access. Hardcoded credentials get embedded when prompts request quick prototypes, with API keys and service account passwords appearing directly in scripts instead of pulling from secure credential stores. Missing input validation happens because AI models optimize for functionality over safety, allowing command injection when workflows fail to sanitize user input.
Context-Dependent Vulnerabilities That Traditional Scanning Misses
Thankfully, there are security tools/features built into many vibe coding tools that are intended to make sure common attack vectors are tackled, which is great to catch those security issues inherent in the code that's been used to train the LLM (large language model). However, these checks typically catch only obvious flaws like SQL injection or missing encryption. They fail when security depends on understanding your specific environment.
Consider a workflow that grants application access by department. Generic scanning verifies the code syntax is correct. What it can't assess is whether the permissions match your access control policies, whether the approval chain follows compliance requirements, or if the workflow exposes sensitive data to the wrong group. Security startup Tenzai concluded that vibe coding tools avoid flaws that can be solved generically, but struggle where context determines what's safe versus dangerous. Traditional static analysis tools look for known vulnerability patterns. They don't understand your org chart, data classification policies, or which API calls should require multi-factor authentication.
Using AI to vibe code, though, is only one part of the larger security issue with AI in IT workflows. When companies use agentic coding assistants, the available attack surface expands dramatically.
Agentic Coding Assistants and the Expanded Attack Surface
Agentic coding assistants execute code autonomously across your systems without human review. They detect workflow failures, write corrective scripts, deploy them to production, and modify access permissions in seconds. Unfortunately, these agents require broad API access to your SaaS stack. They read employee data, modify group memberships, provision access, and interact with identity management systems. So when an agent gets compromised, its credentials give attackers identical lateral movement capabilities.
That problem is exacerbated when an agent feels that it needs more permission to carry out its objectives. That's called privilege escalation. This happens when agents autonomously grant themselves additional permissions to complete tasks. Without guardrails, an agent optimizing for task completion may bypass approval workflows, disable security checks/controls, or access data outside its intended scope. Data exposure risks also grow as agents make autonomous decisions about information requirements. An agent troubleshooting access issues might pull sensitive employee records, export configuration data, or query systems containing regulated data without proper logging.
Tackling the Security Issues of Using AI-Generated Code
Thankfully, there are ways that you can tackle the security while still using non-programmatic methods to develop automation in your IT Workflows. It begins with your approach to building automation.
Workflow Security Versus Model Security
To properly secure your IT workflows, you can't just rely on model security, which protects the AI from adversarial attacks, prompt injection, and data poisoning. You will also need to use workflow security which tackles what happens when that AI operates inside your IT infrastructure with access to identity systems, employee data, and downstream applications.
The reason is that you can secure the model perfectly and still create major vulnerabilities if the workflow grants excessive permissions, connects to unsecured APIs, or executes actions without logging. The threat isn't the AI systems making mistakes. The threat is an AI with legitimate credentials moving laterally across systems, accessing data it shouldn't see, and executing changes without audit trails. For example, when an agent resolves an access request by modifying Active Directory groups, reading HRIS data, and updating application permissions autonomously, each integration point becomes an attack vector. Securing workflows requires defining boundaries for what AI can access, requiring approval gates for sensitive actions, and logging every decision.
Pre-Built Nodes as a Security Alternative to Generated Scripts
But, including those two security approaches, model and workflow, is very challenging when you are using vibe-coded scripts in your workflow. That's because you are still dealing with scripts. A much better approach is to use pre-built workflow nodes with tested, auditable components. Each node performs a specific function: add user to group, wait for approval, send notification, update ticket status. These actions are written once, security reviewed, and reused across every workflow.
The difference is transparency. When you build a workflow with nodes, you see exactly what happens at each step. There's no hidden logic, no generated code making assumptions about your security requirements, no unverified scripts executing with elevated permissions.
Ravenna's visual workflow builder uses this node-based architecture. IT teams drag and drop pre-built actions to automate access requests, onboarding sequences, and device management without writing or debugging code. When a security patch or API update is required, you update the node once. Every automation using that node inherits the fix automatically, eliminating the need to hunt through scattered vibe-coded scripts.
A Final Note On Using AI-Generated Scripts In Your IT Workflow: The Accountability Crisis
When AI-generated code fails in production, responsibility becomes unclear:
The developer who prompted it often can't debug logic they didn't write.
The AI won't explain its implementation decisions.
IT teams inherit scripts that appear functional but contain unfamiliar patterns.
Over 40% of junior developers deploy AI-generated code without understanding how it works. This gap between "it runs" and "I know why" creates problems when workflows fail during onboarding, access requests grant wrong permissions, or scripts expose credentials. What's even worse about AI-generated code? Your AI-generated workflow becomes a black box once its creator leaves the team. You can't patch what you can't parse or audit when you don't understand the decisions that went into generating the script in the first place. Scripts accumulate undocumented until failures force blind debugging. Even experienced developers struggle maintaining code built on assumptions they never specified.
When you are using vibe-coding for your IT workflow automation, you'll have more to worry about than just the inherent security issues.
Final Thoughts on Securing AI-Generated IT Workflows
Vibe coding won't disappear, but IT security teams need alternatives when automating sensitive workflows. Generated scripts work until they fail in production, expose credentials, or grant wrong permissions because the AI didn't understand your environment. Pre-built workflow nodes solve this by replacing black-box code with auditable actions that your team can trust. You automate faster without inheriting vulnerabilities you can't see or fix.
FAQs
What makes vibe coding more dangerous than traditional manual coding for IT workflows?
Vibe coding generates complete scripts that most IT teams can't fully audit or debug, creating black boxes in your infrastructure. When AI-generated code fails or contains context-dependent vulnerabilities, you inherit scripts with unfamiliar patterns that no one on your team understands well enough to fix safely.
How can I automate IT workflows without the security risks of AI-generated code?
Use pre-built workflow nodes instead of generated scripts. These tested, auditable components perform specific functions like adding users to groups or waiting for approvals, giving you full visibility into what happens at each step without hidden logic or unverified code executing with elevated permissions.
Why do traditional security scanning tools miss vulnerabilities in vibe-coded workflows?
Security scanners catch generic flaws like SQL injection but fail when safety depends on your specific environment. They can't assess whether permissions match your access control policies, if approval chains follow compliance requirements, or whether workflows expose sensitive data to unauthorized groups.
What happens when agentic coding assistants operate autonomously in my IT environment?
Agentic assistants execute code across your systems without human review, requiring broad API access to identity management, employee data, and SaaS applications. Compromised agent credentials give attackers the same lateral movement capabilities, while autonomous privilege escalation can bypass approval workflows and disable security controls faster than your team can detect them.
How do I maintain AI-generated workflows after the original developer leaves?
You can't reliably maintain what you don't understand. Over 40% of junior developers deploy AI-generated code without knowing how it works, creating undocumented scripts that accumulate until failures force blind debugging. Pre-built workflow components solve this by letting you update security patches once and automatically applying fixes across every automation.
