Backdoor Collapse: Eliminating Unknown Threats via Known Backdoor Aggregation in Language Models

• URL: http://arxiv.org/abs/2510.10265v1
• Date: Sat, 11 Oct 2025 15:47:35 GMT
• Title: Backdoor Collapse: Eliminating Unknown Threats via Known Backdoor Aggregation in Language Models
• Authors: Liang Lin, Miao Yu, Moayad Aloqaily, Zhenhong Zhou, Kun Wang, Linsey Pang, Prakhar Mehrotra, Qingsong Wen,
• Abstract summary: Ourmethod reduces the average Attack Success Rate to 4.41% across multiple benchmarks.<n>Clean accuracy and utility are preserved within 0.5% of the original model.<n>The defense generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios.
• Score: 75.29749026964154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Backdoor attacks are a significant threat to large language models (LLMs), often embedded via public checkpoints, yet existing defenses rely on impractical assumptions about trigger settings. To address this challenge, we propose \ourmethod, a defense framework that requires no prior knowledge of trigger settings. \ourmethod is based on the key observation that when deliberately injecting known backdoors into an already-compromised model, both existing unknown and newly injected backdoors aggregate in the representation space. \ourmethod leverages this through a two-stage process: \textbf{first}, aggregating backdoor representations by injecting known triggers, and \textbf{then}, performing recovery fine-tuning to restore benign outputs. Extensive experiments across multiple LLM architectures demonstrate that: (I) \ourmethod reduces the average Attack Success Rate to 4.41\% across multiple benchmarks, outperforming existing baselines by 28.1\%$\sim$69.3\%$\uparrow$. (II) Clean accuracy and utility are preserved within 0.5\% of the original model, ensuring negligible impact on legitimate tasks. (III) The defense generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios.

Related papers

• Self-Purification Mitigates Backdoors in Multimodal Diffusion Language Models [74.1970982768771]
  We show that well-established data-poisoning pipelines can successfully implant backdoors into MDLMs.<n>We introduce a backdoor defense framework for MDLMs named DiSP (Diffusion Self-Purification)
  arXiv  Detail & Related papers  (2026-02-24T15:47:52Z)
• The Trigger in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers [2.2374050209578864]
  We present a practical scanner for identifying sleeper agent-style backdoors in causal language models.<n>Our approach relies on two key findings: first, sleeper agents tend to memorize poisoning data, making it possible to leak backdoor examples.<n>We show that our method recovers working triggers across multiple backdoor scenarios and a broad range of models.
  arXiv  Detail & Related papers  (2026-02-03T04:17:21Z)
• Backdoor Unlearning by Linear Task Decomposition [69.91984435094157]
  Foundation models are highly susceptible to adversarial perturbations and targeted backdoor attacks.<n>Existing backdoor removal approaches rely on costly fine-tuning to override the harmful behavior.<n>This raises the question of whether backdoors can be removed without compromising the general capabilities of the models.
  arXiv  Detail & Related papers  (2025-10-16T16:18:07Z)
• DISTIL: Data-Free Inversion of Suspicious Trojan Inputs via Latent Diffusion [0.7351161122478707]
  Deep neural networks are vulnerable to Trojan (backdoor) attacks.<n> triggerAdaptive inversion reconstructs malicious "shortcut" patterns inserted by an adversary during training.<n>We propose a data-free, zero-shot trigger-inversion strategy that restricts the search space while avoiding strong assumptions on trigger appearance.
  arXiv  Detail & Related papers  (2025-07-30T16:31:13Z)
• Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models [70.03122709795122]
  Backdoor attacks targeting text-to-image diffusion models have advanced rapidly.<n>Current backdoor samples often exhibit two key abnormalities compared to benign samples.<n>We propose Trigger without Trace (TwT) by explicitly mitigating these consistencies.
  arXiv  Detail & Related papers  (2025-03-22T10:41:46Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks.<n>$textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning.<n>$textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• Neutralizing Backdoors through Information Conflicts for Large Language Models [20.6331157117675]
  We present a novel method to eliminate backdoor behaviors from large language models (LLMs)<n>We leverage a lightweight dataset to train a conflict model, which is then merged with the backdoored model to neutralize malicious behaviors.<n>We can reduce the attack success rate of advanced backdoor attacks by up to 98% while maintaining over 90% clean data accuracy.
  arXiv  Detail & Related papers  (2024-11-27T12:15:22Z)
• Diff-Cleanse: Identifying and Mitigating Backdoor Attacks in Diffusion Models [3.134071086568745]
  Diffusion models (DMs) are regarded as one of the most advanced generative models today. Recent studies suggest that DMs are vulnerable to backdoor attacks. This vulnerability poses substantial risks, including reputational damage to model owners. We introduce Diff-Cleanse, a novel two-stage backdoor defense framework specifically designed for DMs.
  arXiv  Detail & Related papers  (2024-07-31T03:54:41Z)
• T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models [70.03122709795122]
  We propose a comprehensive defense method named T2IShield to detect, localize, and mitigate backdoor attacks. We find the "Assimilation Phenomenon" on the cross-attention maps caused by the backdoor trigger. For backdoor sample detection, T2IShield achieves a detection F1 score of 88.9$%$ with low computational cost.
  arXiv  Detail & Related papers  (2024-07-05T01:53:21Z)
• Backdoor Learning on Sequence to Sequence Models [94.23904400441957]
  In this paper, we study whether sequence-to-sequence (seq2seq) models are vulnerable to backdoor attacks. Specifically, we find by only injecting 0.2% samples of the dataset, we can cause the seq2seq model to generate the designated keyword and even the whole sentence. Extensive experiments on machine translation and text summarization have been conducted to show our proposed methods could achieve over 90% attack success rate on multiple datasets and models.
  arXiv  Detail & Related papers  (2023-05-03T20:31:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.