What Vibe Coding Risk Looks Like
When a developer writes code manually, they make dozens of implicit decisions about error handling, validation, and system behavior. AI generates the expected path — it does not reason about what happens when that path breaks.
The result is code that looks correct, passes review, and fails in ways that are difficult to trace and expensive to recover from.
Most Common Findings
- 01Silent failure paths — errors that are swallowed, logged, and forgotten
- 02Inconsistent input validation — AI generates validation for the example, not the edge case
- 03Model dependency without fallback — single-point failure on third-party AI APIs
- 04Undocumented autonomy — AI-generated code making decisions the team did not realize it was making
- 05Shallow test coverage — tests that pass but do not protect against what matters
What the Audit Covers
Four coverage areas, each targeting a distinct category of AI-generated code risk.
Pattern Analysis
We review AI-generated code for structural patterns that indicate latent fragility — over-wide catch blocks, inconsistent validation, shallow error messages.
Autonomy Boundaries
We identify every place where AI-generated code makes a decision without surfacing it to a human, then classify those points by risk.
Integration Risk
AI-generated code integrates cleanly in isolation and poorly at scale. We test integration boundaries between AI and non-AI components.
Test Coverage Gaps
AI-assisted test suites cover happy paths thoroughly and adversarial paths poorly. We identify the gaps between what is tested and what matters in production.
Why It Matters Now
Vibe coding is spreading faster than the tooling to govern it. More AI-generated code enters production with less human review every quarter. The window for catching structural problems before they compound is shorter than most teams realize.
An audit now is significantly less expensive than a production incident, a failed fundraise, or an enterprise customer who walks away after finding something in due diligence.
See the full AI Systems Audit →Frequently Asked Questions
- What is vibe coding?
- Vibe coding describes development workflows where AI generates substantial portions of implementation code from high-level descriptions, often with limited line-by-line review. It does not require a specific tool — it describes a pattern.
- We wrote our code ourselves. Does this apply to us?
- If you used any AI-assisted tooling — Copilot, Cursor, ChatGPT for code generation — this applies. The issue is not authorship, it is the patterns that AI tools introduce across a codebase over time.
- Do you need our full codebase?
- No. We work with representative samples focused on two to five high-risk areas identified during intake. We are not reviewing for security vulnerabilities.
- What are the most common findings?
- Silent failure paths, inconsistent input validation, model dependency without fallback, undocumented autonomy, and shallow test coverage.
- Is this a one-time review or ongoing?
- The initial audit is a one-time engagement. We also offer advisory retainers for teams who want ongoing review practices embedded into their development cycle.
Find What Your AI-Generated Code Is Hiding
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