Question 1

What is Vibefixing and what does it scan?

Accepted Answer

Vibefixing is a runtime supervisor and security scanner for AI agents. It statically analyzes your codebase to find unsafe tool calls your AI agent can execute before they reach production: unguarded Stripe charges, raw database mutations, filesystem writes, and shell commands.

Question 2

How does Vibefixing prevent prompt injection in AI agents?

Accepted Answer

Vibefixing identifies tool calls that lack input validation or guardrails, which are the primary attack surface for prompt injection. By flagging every path where an LLM output can trigger an irreversible action without a human confirmation step, it eliminates the conditions that make injection exploits dangerous.

Question 3

Is Vibefixing safe to use for vibe coders shipping with AI-generated code?

Accepted Answer

Yes. Vibefixing is designed for vibe coders: developers shipping fast with AI assistants like Claude, Cursor, or Copilot. It catches risky patterns LLMs commonly generate: unguarded API calls, database deletes without confirmation, and credential exposure in tool call arguments.

Question 4

Does Vibefixing work with any AI agent framework?

Accepted Answer

Vibefixing analyzes your repository at the code level, so it works with any agent framework: LangChain, LlamaIndex, CrewAI, custom OpenAI function-calling, Anthropic tool use, or plain Python scripts. No instrumentation or runtime hooks required.

Question 5

I'm not building an AI agent — does Vibefixing still help?

Accepted Answer

Yes. The same patterns an agent can misuse — Stripe checkout sessions, account mutations, webhook handlers that re-deliver and double-charge — are what get exploited by prompt injection, a careless contractor, or a runaway script. Vibefixing flags them whether or not an LLM is in the call chain. Supported stacks include Next.js App Router (route handlers and Server Actions), Stripe (TypeScript and Python), Supabase (service-role writes and row-level security checks on migrations), Prisma, and SQLAlchemy.

Question 6

What unsafe actions does Vibefixing detect?

Accepted Answer

Vibefixing detects: Stripe charges, refunds, subscription changes, and customer portal sessions (TypeScript and Python); raw SQL mutations and Supabase writes that bypass row-level security; Next.js Server Actions and API route handlers; webhook handlers that replay database writes on retry; filesystem deletes; shell execution; emails sent without per-call confirmation. Each finding includes the file, line, and a one-line wrap.

Question 7

How long does a Vibefixing scan take?

Accepted Answer

A public repository scan typically completes in under 60 seconds. Vibefixing uses static analysis and does not run your code or require an API key for the scan. Results include a risk score, a list of unsafe actions, and copy-paste guardrail code for each finding.

Question 8

Does Vibefixing scan every pull request automatically?

Accepted Answer

Yes. Install the Vibefixing GitHub App on your repo and every pull request gets scanned automatically. Within 5 seconds of opening a PR, vibefixing diffs the head ref against your previous scan and posts a comment listing only the new unsafe call-sites. Clean PRs get nothing — no spam. Free for public repos; private repos and CI integration are part of Builder ($29/mo), while team workflows, SSO, and org-level controls start at Pro ($99/workspace/mo).

Question 9

How is Vibefixing different from regular code review or static analysis?

Accepted Answer

Code review and SAST tools catch bugs in code your tests already cover. Vibefixing catches what your tests cannot: actions an LLM can fire at runtime in ways no test case anticipated. Refunds the model decides to issue, files it decides to delete, emails it decides to send. Each detector maps to a runtime guard you can drop in with one line.

Question 10

How is Vibefixing different from Snyk or Semgrep for AI agents?

Accepted Answer

Snyk and Semgrep are built for human-written code vulnerabilities: CVEs, dependency audits, known patterns. Vibefixing is built specifically for LLM-driven execution paths — the tool calls an AI agent fires at runtime based on user input. It understands agent shapes (tool-using, chatbot, RAG, MCP-server) and surfaces risk framed around what the model can do, not what a human wrote incorrectly.

Question 11

Does Vibefixing replace runtime monitoring tools like LangSmith?

Accepted Answer

No — they are complementary. LangSmith and similar tools observe what your agent does at runtime after deployment. Vibefixing runs before deployment, on your static codebase, to catch unsafe tool call patterns before they ever reach production. Use Vibefixing in CI to prevent risky code from shipping, and runtime monitoring to observe behavior after it ships.