Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning

• URL: http://arxiv.org/abs/2606.26036v1
• Date: Wed, 24 Jun 2026 17:12:42 GMT
• Title: Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
• Abstract summary: Training-time data poisoning poses a significant threat to large language models deployed for abstractive text summarization.<n>We present a unified post-hoc defense framework for detecting and remediating fine-tuning-stage poisoning in summarization models.<n>Our results indicate that fine-tuning-time poisoning leaves persistent structural artifacts, enabling practical detection and post-deployment recovery without full retraining.
• Score: 2.9148458734058913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization, where small task-specific datasets exert disproportionate influence on model behavior. In this setting, adversaries manipulate fine-tuning data to induce persistent summarization failures, such as biased or harmful summaries, while preserving standard evaluation metrics. We present a unified post-hoc defense framework for detecting and remediating fine-tuning-stage poisoning in summarization models across the machine learning supply chain. Our experiments show that in white-box settings, poisoned document-summary pairs exhibit abnormally high training influence, enabling detection via influence-function analysis with semantic consistency checks. In black-box settings, poisoned models display two to three times greater sensitivity to semantics-preserving perturbations, enabling behavioral auditing without training data access. Beyond existing poisoning formulations, we introduce novel attacks targeting factual distortion and representational bias, showing that poisoning alters summarization behavior without triggering conventional alarms. Across nine architectures and six benchmark datasets under adaptive attacks, our defenses achieve 85-92% detection precision, while gradient-ascent unlearning restores up to 96% of original behavior with minimal utility loss (less than 0.6% ROUGE degradation). These results indicate that fine-tuning-time poisoning leaves persistent structural artifacts, enabling practical detection and post-deployment recovery without full retraining.

Related papers

• Associative Poisoning to Generative Machine Learning [5.094623170336122]
  We introduce a novel data poisoning technique called associative poisoning.<n>It compromises fine-grained features of the generated data without requiring control of the training process.<n>This attack perturbs only the training data to manipulate statistical associations between specific feature pairs in the generated outputs.
  arXiv  Detail & Related papers  (2025-11-07T11:47:33Z)
• Detecting Instruction Fine-tuning Attacks on Language Models using Influence Function [6.778374627376906]
  Instruction finetuning attacks pose a serious threat to large language models.<n>Poisoned data is often indistinguishable from clean data.<n>We present a detection method that requires no prior knowledge of the attack.
  arXiv  Detail & Related papers  (2025-04-12T00:50:28Z)
• Unlearnable Examples Detection via Iterative Filtering [84.59070204221366]
  Deep neural networks are proven to be vulnerable to data poisoning attacks. It is quite beneficial and challenging to detect poisoned samples from a mixed dataset. We propose an Iterative Filtering approach for UEs identification.
  arXiv  Detail & Related papers  (2024-08-15T13:26:13Z)
• DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models [12.42597979026873]
  We propose DataElixir, a novel sanitization approach tailored to purify poisoned datasets. We leverage diffusion models to eliminate trigger features and restore benign features, thereby turning the poisoned samples into benign ones. Experiments conducted on 9 popular attacks demonstrates that DataElixir effectively mitigates various complex attacks while exerting minimal impact on benign accuracy.
  arXiv  Detail & Related papers  (2023-12-18T09:40:38Z)
• On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks [58.718697580177356]
  Attacks on deep learning models with malicious training samples are known as data poisoning. Recent advances in defense strategies against data poisoning have highlighted the effectiveness of aggregation schemes in achieving certified poisoning robustness. Here we focus on Deep Partition Aggregation, a representative aggregation defense, and assess its practical aspects, including efficiency, performance, and robustness.
  arXiv  Detail & Related papers  (2023-06-28T17:59:35Z)
• Exploring Model Dynamics for Accumulative Poisoning Discovery [62.08553134316483]
  We propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks.
  arXiv  Detail & Related papers  (2023-06-06T14:45:24Z)
• Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks [31.339252233416477]
  We introduce the notion of model poisoning reachability as a technical tool to explore the intrinsic limits of data poisoning attacks towards target parameters. We derive an easily computable threshold to establish and quantify a surprising phase transition phenomenon among popular ML models. Our work highlights the critical role played by the poisoning ratio, and sheds new insights on existing empirical results, attacks and mitigation strategies in data poisoning.
  arXiv  Detail & Related papers  (2023-03-07T01:55:26Z)
• Temporal Robustness against Data Poisoning [69.01705108817785]
  Data poisoning considers cases when an adversary manipulates the behavior of machine learning algorithms through malicious training data. We propose a temporal threat model of data poisoning with two novel metrics, earliness and duration, which respectively measure how long an attack started in advance and how long an attack lasted.
  arXiv  Detail & Related papers  (2023-02-07T18:59:19Z)
• Accumulative Poisoning Attacks on Real-time Data [56.96241557830253]
  We show that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects. Our work validates that a well-designed but straightforward attacking strategy can dramatically amplify the poisoning effects.
  arXiv  Detail & Related papers  (2021-06-18T08:29:53Z)
• How Robust are Randomized Smoothing based Defenses to Data Poisoning? [66.80663779176979]
  We present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality. We propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods.
  arXiv  Detail & Related papers  (2020-12-02T15:30:21Z)

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.