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AI models struggle to fix code leaks; narrow prompts improve success

A recent experiment tested the effectiveness of using AI models to fix code leaks, such as API keys. The study found that the success rate varied significantly depending on the AI model and the prompting method used. Some models failed to completely remove the leaked information, either by commenting it out, re-printing it in explanations, or retaining it in internal reasoning traces. However, specific, narrow prompts that explicitly instructed the AI to delete the secret, use environment variables, and avoid reproducing the value in any output or reasoning trace proved effective across all tested models. AI

IMPACT Specific prompting strategies are crucial for ensuring AI models securely handle sensitive code, preventing unintended data exposure.

RANK_REASON The item details an experiment and its findings regarding the effectiveness of AI models in a specific task (fixing code leaks). [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models struggle to fix code leaks; narrow prompts improve success

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · 이령 ·

    I tested whether "just paste the leak into your AI to fix it" actually works. It depends on the model — here's what broke.

    <p>The gap I wanted to fill</p> <p>A secret scanner can tell you "you leaked an API key here." The usual next step everyone repeats is: paste it into ChatGPT/Claude/Gemini and ask it to fix it.</p> <p>But does that actually remove the secret? I had a hunch the answer was "depends…