Data-Efficient Backdoor Attacks on Code Models via Trigger-Neutralized Counterfactual Scoring↗ (원문, 새 창)

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arXiv:2610.32334v1 Announce Type: new Abstract: Pre-trained code models are exposed to backdoor poisoning, in which crafted training samples make the model misbehave on a trigger. A capable attacker minimizes the number of such samples, since each adds cost and exposure and a lower poisoning rate leaves a smaller footprint for defenses. Which samples to poison therefore matters, but existing data-efficient selection methods target image classification, offer no scoring mechanism for autoregress