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AutoTrace reveals that even state-of-the-art LLMs falter in causal reasoning, struggling to differentiate between vulnerable and safe code despite a robust new dataset.
Calibration without comprehension reveals that fine-tuning LLMs for vulnerability detection fails to enhance their underlying security reasoning, with models achieving only 52.1% detection accuracy.
