Fooling Bangla AI: New Attacks Reveal Model Weaknesses

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Author: Denis Avetisyan

Researchers have developed a novel method to generate subtle, misleading text examples that expose vulnerabilities in artificial intelligence systems designed for the Bangla language.

This work introduces ‘destroR,’ a pipeline for crafting obfuscated examples to assess and improve the robustness of transfer learning models in Bangla natural language processing.

Despite recent advances in natural language processing, machine learning models remain vulnerable to carefully crafted adversarial inputs. This paper, ‘destroR: Attacking Transfer Models with Obfuscous Examples to Discard Perplexity’, introduces a novel pipeline for generating obfuscous examples designed to induce perplexity in state-of-the-art transfer models, with a particular focus on evaluating robustness in the Bangla language. Through these attack recipes, we demonstrate a method for assessing and improving model reliability against subtle perturbations. Could this approach pave the way for more robust and trustworthy NLP systems across diverse languages and applications?

The Fragility of Bangla NLP: An Emerging Vulnerability

The burgeoning field of Natural Language Processing has recently demonstrated significant potential for languages with limited digital resources, notably Bangla. This progress is largely attributable to the development of Transfer Learning architectures, where models pre-trained on massive datasets in resource-rich languages – such as English – are fine-tuned for Bangla. This approach circumvents the need for extensive, labeled data in Bangla itself, a traditional bottleneck for NLP development. Consequently, tasks like machine translation, sentiment analysis, and text summarization are showing increasingly accurate results, opening doors for wider access to information and technology for the over 265 million Bangla speakers globally. The ability to leverage knowledge from other languages represents a pivotal shift, offering a cost-effective and efficient pathway to building sophisticated language technologies for previously underserved linguistic communities.

Despite recent progress in Natural Language Processing for Bangla, transfer learning models exhibit a significant vulnerability to adversarial attacks. These attacks involve subtly manipulating input text – often with changes imperceptible to humans – to deliberately mislead the model, causing incorrect classifications or outputs. This susceptibility poses a considerable risk when deploying these models in real-world applications such as sentiment analysis, machine translation, or even critical decision-making systems. A seemingly harmless alteration – a slight misspelling or the addition of a non-semantic character – can dramatically reduce a model’s accuracy, raising serious concerns about its dependability and trustworthiness, particularly in sensitive contexts where erroneous outputs could have substantial consequences.

Current evaluation metrics for Natural Language Processing models frequently provide an incomplete picture of their vulnerability to adversarial attacks. These metrics, often focused on overall accuracy, struggle to detect subtle but significant changes in model behavior induced by carefully crafted malicious inputs. A model might maintain a high accuracy score while still misclassifying critical information or exhibiting biased outputs when faced with these attacks. This discrepancy highlights the need for more nuanced testing methodologies, including adversarial robustness benchmarks and metrics that specifically assess the model’s sensitivity to perturbations. Researchers are actively exploring techniques like analyzing confidence scores, examining internal representations, and employing human evaluation to gain a more comprehensive understanding of model vulnerabilities and develop truly reliable Bangla NLP systems.

Constructing Perturbations: Methods for Evaluating Robustness

To assess the robustness of Bangla natural language processing models, three distinct adversarial attack methods were implemented. The Bangla Paraphrase Attack generates subtly modified inputs using the csebuetnlp/banglat5_banglaparaphrase model. The Bangla Back Translation attack employs an iterative translation process, first translating Bangla text to English with csebuetnlp/banglat5_nmt_en_bn and then back to Bangla using csebuetnlp/banglat5_nmt_bn_en. Finally, the One-Hot Word Swap Attack utilizes a Masked Language Modeling approach to identify and substitute words with plausible alternatives, allowing for the creation of up to ten adversarial examples for each input data point.

The Bangla Paraphrase Attack generates adversarial examples by utilizing the csebuetnlp/banglat5_banglaparaphrase model, a sequence-to-sequence model fine-tuned for paraphrasing Bangla text. The Bangla Back Translation attack employs a two-step process: first translating Bangla text to English using csebuetnlp/banglat5_nmt_en_bn, and then translating the English output back to Bangla using csebuetnlpbanglat5_nmt_bn_en. This round-trip translation introduces subtle changes to the original text, creating perturbed inputs for evaluating model robustness. Both attacks rely on pre-trained models to automatically generate variations while preserving semantic meaning.

The One-Hot Word Swap Attack generates adversarial examples by leveraging Masked Language Modeling (MLM) techniques. This method identifies words within an input sequence and replaces them with plausible alternatives predicted by the MLM. Specifically, the model predicts potential replacement words based on the surrounding context, ensuring the generated substitutions are grammatically and semantically coherent. This process allows for the creation of multiple adversarial examples – up to ten per original data point – each representing a slightly perturbed version of the input designed to potentially mislead the target model.

Evidence of Vulnerability: Performance Across Diverse Datasets

Evaluation of attack methodologies was conducted across four datasets to determine the generalizability of observed vulnerabilities. The BLP23 Dataset, a Bengali language dataset, was used alongside the YouTube Sentiment Dataset and CogniSenti Dataset, both English language resources. To further broaden the evaluation scope, the BASA_cricket Dataset, which focuses on sentiment analysis of cricket commentary in Bengali, was also included. This diverse selection, encompassing different languages and topical domains, allowed for assessment of whether vulnerabilities observed in one dataset would translate to others, providing a more comprehensive understanding of model robustness.

Evaluation of the ka05ar/banglabert-sentiment model, a state-of-the-art system for Bangla sentiment analysis, revealed substantial performance degradation when subjected to adversarial perturbations. Specifically, experiments across multiple datasets demonstrated reductions in the F1 score, a key metric for evaluating classification accuracy, of up to 40% on datasets used during model training (internal datasets). Furthermore, performance on previously unseen datasets (external datasets) decreased by as much as 37% under the same adversarial conditions, indicating a limited capacity for generalization and a susceptibility to carefully crafted input manipulations.

Analysis of model outputs revealed fluctuations in confidence scores even when the predicted sentiment label remained consistent following adversarial perturbations. This indicates that while models may still arrive at the correct classification, their internal certainty regarding that classification is diminished, suggesting underlying confusion. Observed confidence score reductions ranged across datasets, demonstrating that these changes are not isolated incidents but rather a systemic response to adversarial inputs. This metric provides a valuable signal of model instability and potential vulnerability beyond simple accuracy measurements, highlighting a need to consider internal model states when evaluating robustness.

Beyond Accuracy: Towards Robust and Interpretable Systems

Current natural language processing evaluation often relies on aggregate metrics that provide a limited view of model capabilities. Researchers increasingly champion the use of behavioral testing frameworks, such as CheckList, to move beyond these broad scores and instead rigorously probe specific linguistic skills and potential failure modes. This methodology involves constructing test cases that target defined competencies – like understanding negation, coreference resolution, or handling of numerical reasoning – and assessing performance on these focused tasks. By systematically evaluating models across a diverse range of linguistic phenomena, CheckList and similar tools reveal vulnerabilities that would remain hidden by standard benchmarks, fostering the development of more reliable and robust language systems. The emphasis shifts from simply measuring overall accuracy to understanding how a model arrives at its conclusions and identifying the specific types of inputs where it is likely to falter.

Future investigations should prioritize bolstering model robustness through the implementation of adversarial training techniques. These methods intentionally expose models to subtly altered data, forcing them to learn more resilient feature representations and reducing susceptibility to malicious or naturally occurring perturbations. Simultaneously, integrating external knowledge sources, such as the Bengala WordNet, promises to significantly improve performance and generalization capabilities. By grounding language models in structured lexical databases, researchers can provide them with a deeper understanding of semantic relationships and contextual nuances, leading to more reliable and accurate outputs, particularly in low-resource settings or when dealing with ambiguous input. This combined approach – proactively challenging models with adversarial examples and enriching their knowledge base – represents a crucial step towards building truly robust and trustworthy natural language processing systems.

Investigating the “why” behind natural language processing model errors is crucial for building truly reliable systems, and tools like AllenNLP Interpret offer a pathway to do just that. This platform doesn’t simply identify what a model got wrong, but rather dissects the decision-making process, highlighting which input tokens most influenced the outcome. By visualizing these attention patterns and attribution scores, researchers can pinpoint vulnerabilities – perhaps the model is overly reliant on spurious correlations or fails to grasp nuanced contextual cues. Such granular insights move beyond superficial error analysis, allowing for targeted interventions in model architecture or training data to improve generalization and robustness, ultimately fostering the development of more resilient and trustworthy natural language technologies.

The pursuit of robust Bangla language models, as detailed in this work, echoes a fundamental principle of efficient communication. The paper’s focus on adversarial attacks and the creation of ‘obfuscous examples’ highlights the necessity of stripping away extraneous complexity to reveal core meaning. As Vinton Cerf aptly stated, “Any sufficiently advanced technology is indistinguishable from magic.” This sentiment resonates with the challenge of building machine learning models that can discern genuine signal from deliberately misleading noise; a seemingly magical ability achieved through rigorous simplification and a relentless focus on essential features. The study’s approach to discarding perplexity, through carefully crafted attacks, embodies this principle—revealing the true strength of a model not by what it can process, but by what it can confidently ignore.

Future Directions

The presented work, while demonstrating a method for inducing predictable failure in Bangla language models, merely illuminates the extent of the problem, rather than resolving it. The creation of ‘obfuscous examples’ – adversarial instances designed to maximize perplexity – is, at its core, a symptom of reliance on statistical correlation. The models function, not by ‘understanding’ language, but by predicting token sequences. This pipeline, therefore, exposes the fragility inherent in that predictive process. Future iterations must move beyond symptom management and address the underlying structural weaknesses.

A critical limitation remains the specificity of the attack recipes to Bangla. The observed vulnerabilities are likely not unique to this language, but rather manifestations of shared architectural flaws across transfer models. Generalizable attack strategies, and more importantly, robust defense mechanisms applicable across diverse linguistic contexts, are paramount. The field’s current trajectory—an escalating arms race of increasingly sophisticated attacks and defenses—is inefficient. The goal should not be to build impenetrable fortresses, but to construct systems that degrade gracefully when faced with anomalous input.

Ultimately, the pursuit of ‘robustness’ is often a misdirection. Perfect reliability is an asymptotic ideal, and a costly one. A more fruitful avenue lies in quantifying and modeling the types of errors a system is likely to make, and designing applications that can accommodate – or even exploit – those predictable failures. Emotion is a side effect of structure; a system that acknowledges its limitations is, paradoxically, more intelligent than one that feigns omniscience.

Original article: https://arxiv.org/pdf/2511.11309.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-17 17:59