Evaluating LLMs on standardized leaderboards (like MMLU or HumanEval) is helpful, but it rarely tells you how a model performs on real-world edge cases. In this benchmark, I tested three models on a specific dev-sec scenario: Detecting hidden reentrancy and integer overflow vulnerabilities in a ...

Source: [Dev.to](https://dev.to/saranyo/benchmarking-gpt-4o-claude-35-sonnet-and-llama-3-for-automated-code-auditing-vulnerability-fci)

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