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.
Data-Efficient Backdoor Attacks on Code Models via Trigger-Neutralized Counterfactual Scoring
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