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New method improves backdoor attacks on code models with minimal poisoning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Trigger-Neutralized Counterfactual Scoring (TNCS) to efficiently identify samples for poisoning pre-trained code models. This technique isolates the impact of a trigger by comparing a model's output on triggered input versus a trigger-neutralized counterfactual. TNCS demonstrates higher attack success rates at very low poisoning ratios across various code tasks and trigger types, without degrading clean-task performance. AI

IMPACT This research highlights potential vulnerabilities in code models, necessitating improved defenses against data poisoning attacks.

RANK_REASON The cluster contains a research paper detailing a new method for backdoor attacks on code models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New method improves backdoor attacks on code models with minimal poisoning

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The cluster contains a research paper detailing a new method for backdoor attacks on code models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yu-Yu Lee, Ming-Feng Tsai, Tomohiro Morikawa, Chun-I Fan ·

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

    arXiv:2610.32334v1 Announce Type: cross 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 l…

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