Every technical interview used to carry a comforting assumption: if a candidate solved the challenge, they understood it. That assumption is now broken. AI assistants can generate exploit code, explain vulnerabilities, and walk someone through an attack step by step. A candidate who has never truly understood a buffer overflow can now look — on paper and in a take-home test — exactly like someone who has.
Welcome to vibe hacking: solving security problems by prompting an AI tool until something works, without grasping why it works. It's one of the biggest blind spots in cybersecurity hiring today, and spotting it means rethinking how you assess talent.
What “vibe hacking” looks like
A vibe hacker isn't necessarily trying to cheat. They've simply learned that describing a problem to an AI and pasting back the output gets results — most of the time. They can solve a challenge, patch a config, or write a script. What they can't do is explain the reasoning, adapt when the tool is wrong, or improvise when the scenario doesn't match anything the model has seen.
In day-to-day work, that gap stays hidden. During a live incident — when the AI's suggestion is subtly wrong, the environment is unfamiliar, and every minute counts — it becomes very expensive.
Why resumes and take-home tests can't tell them apart
Traditional filters were designed for a pre-AI world:
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Take-home assignments now measure prompting ability as much as skill.
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Knowledge quizzes were always weak, and AI makes them meaningless.
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Certifications prove someone passed a test, not that they can operate under pressure.
Two candidates can submit identical, correct solutions. One reasoned their way there; the other narrated the problem to a chatbot. On paper, they're indistinguishable.
The tells that separate understanding from prompting
You can't detect depth by looking at output. You detect it by watching the process and probing it:
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Can they explain why? Understanding survives follow-up questions; prompting collapses under them.
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Can they adapt when the tool is wrong? Introduce a twist the AI would fumble. Real skill pivots; vibe hacking freezes.
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Can they recognize a false positive? Someone who understands the system questions a suspicious result instead of trusting it blindly.
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Can they work in an unfamiliar environment? Novel, realistic scenarios can't be pattern-matched from public writeups.
How to assess for real understanding
The fix isn't banning AI — candidates will use it on the job, and pretending otherwise is naive. The fix is designing assessments where AI is a tool, not a crutch:
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Use live, hands-on environments instead of static questionnaires.
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Build novel scenarios that don't map neatly to public writeups.
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Ask candidates to explain and defend their decisions in real time.
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Watch how they respond when something breaks unexpectedly.
Let candidates use whatever tools they want — then observe judgment, adaptability, and reasoning. Those are the things AI can't fake on their behalf, and exactly the things you're actually hiring for.
Hire for judgment, not keystrokes
AI has permanently changed what a “correct answer” tells you. The candidates worth hiring aren't the ones who avoid AI or the ones who lean on it entirely — they're the ones who can direct it, catch its mistakes, and take over when it fails. To find them, you have to watch them work.
Put skill to the test
Simulations Labs gives you live, hands-on assessment environments that reveal how candidates actually think — not just what they can prompt. Separate real skill from vibe hacking with realistic scenarios AI can't solve for them.
