arXiv cs.AI· Yu-Yu Lee, Ming-Feng Tsai, Tomohiro Morikawa, Chun-I Fan·· 12 小时前AI 评分29
TNCS：面向代码模型的数据高效后门攻击方法
Data-Efficient Backdoor Attacks on Code Models via Trigger-Neutralized Counterfactual Scoring
AI 导读
研究者提出 Trigger-Neutralized Counterfactual Scoring（TNCS），通过对比触发输入与触发中和反事实输入的评分模型输出，隔离触发器对目标行为的贡献，从而为自回归生成场景挑选高价值投毒样本。
正文 · 原文
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 autoregressive generation, and have not been studied on code. We propose Trigger-Neutralized Counterfactual Scoring (TNCS), which isolates a trigger's contribution to the target behavior by comparing the scoring model's output on a triggered input with its output on a trigger-neutralized counterfactual, covering classification and generation alike. TNCS attains higher attack success rates than the baselines at very low poisoning ratios, across three CodeXGLUE tasks and five trigger families, without degrading clean-task performance, and its margin grows as the ratio decreases. It is the only method effective on every trigger family, and its selected poison set transfers to mismatched victims.
| Comments: | 18 pages, 7 figures, 14 Tables |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32334 [cs.CR] |
| (or arXiv:2610.32334v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32334 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chun-I Fan [view email]
[v1]
Fri, 23 Oct 2026 08:28:58 UTC (1,235 KB)
来源：arXiv cs.AI · arxiv.org