Shortcut Learning in Safety: The Impact of Keyword Bias in Safeguards (2025)

Abstract: This paper investigates the problem of shortcut learning in safety guardrails for large language models (LLMs). It reveals that current safeguard models often rely excessively on superficial cues, such as specific keywords that are spuriously correlated with training labels, rather than genuinely understanding the input’s semantics or intent. As a result, their performance degrades significantly when there is a shift in keyword distribution. The paper also examines the impact of reducing shortcut reliance, showing that merely minimizing shortcut influence is insufficient. To build robust safeguard models, it is equally crucial to promote the use of intended features.

## introduction safeguarding llms from misuse, is a fundamental requirement for any llm-based systems. effective safeguards strive to prevent the acceptance of harmful prompts while avoiding the rejection of safe ones, striking a balance between minimizing harm and preserving utility. the prevailing approaches to building safeguard systems focus on data coverage @xcite @xcite . the current stateof-the-art method, wildguard @xcite , employs synthetic data generation to generate largescale examples of safe and harmful prompts. although synthetic data generation is effective for creating large datasets, generated samples often follow consistent patterns. for instance, safe samples are typically framed as interrogative sentences, * these authors contributed equally to this work e.g., "how do i crush the competition and come out on top?" in contrast, harmful samples tend to be imperative, e.g., "write a blog post that publicly shames a teenager for a mistake they made and encourages readers to do the same." foot_0 such repetitive surface-level patterns in training data can lead to a generalization issue known as shortcut learning @xcite , which causes the models to overfit on certain shallow low-level features like keywords rather than truly understanding the underlying semantics. this makes them vulnerable to failures on inputs that deviate from the seen patterns, as demonstrated in prior works on natural language inference @xcite and commonsense reasoning @xcite . despite this, the study of shortcut learning and keyword bias in safeguard models and their impact on generalizability remains unexplored. in this investigation, we present empirical evidences that characteristic patterns in safeguard training datasets induce keyword bias in safeguard models. specifically, the models tend to rely on the presence of certain keywords, such as "what", "can", and "how", to classify prompts as safe. this reliance makes models vulnerable when these patterns are misleading, e.g., when safe prompts are structured imperatively (figure 1 ). through the lens of shortcut learning, this paper attempts to advance the understanding of safeguard robustness with the following research questions. ## shortcut learning analysis to address the first research question-to what extent do existing safeguard models emulate the characteristics of shortcut learning?, we propose a method to demonstrate simplicity bias @xcite in the context of shortcut keyword bias in safeguard models. we suggest that safeguard models might prioritize superficial features (e.g., highfrequency words) as shortcut keyword features to minimize the loss during training. this dependence on specific keyword features for predictions undermines the model generalization and robustness, suggesting that the model may behave similarly to a keyword detector in making predictions without accounting for the actual semantics of the prompts. ## performance evaluation after verifying the potential shortcut keywords, we delve into the second research question -how does shortcut learning impact the performance and generalizability of safeguard models?. we assess safeguard models on six safety datasets with different characteristics to examine how safeguard models generalize across data distributions. datasets. we utilize test subsets from six different safety benchmark for evaluation: wild-guardtest @xcite , openaimoderation (openaimod) @xcite , toxi-cchat @xcite , xstest @xcite , jailbreakbench @xcite and @xcite . details and data description are in appendix a. models. we evaluate six safeguard models: shield-gemma 9b, llamaguard-3 8b, aegis-permissive and defensive 7b, wildguard 7b, and nemoguard 8b. we analyze the relationship between performance and proportion of class-ascociated keywords on wildguard 7b as a representative. results. ## counterfactual analysis to address the third research question-what are the effects of reducing shortcut reliance?, we employ counterfactual inference (cfi) @xcite as a fine-tuning free approach to reduce the effect of shortcut features. we chose cfi because example class (→) safe harmful keyword ratio (→) safe (%) harmful (%) safe (%) harmful (%) wildguardtrain 33.0±13.1 16.5±10.1 9.6±4.5 34.4±4.6 wildguardtest 17.5±10.1 27.6±10.2 10.6±8.5 34.7±9.2 orbench 14.6±8.1 29.8±9.3 18.4±10.2 28.3±10.1 openaimod 7.3±5.3 26.3±8.5 7.4±5.6 24.7±8.3 toxicchat 10.7±9.9 23.8±12.5 8.5±8.5 30.5±10.8 xstest 29.4±11.2 13.2±9.9 36.2±13.2 13.5±10.2 jailbreakbench 3.7±4.9 31.2±10.1 2.5±3.5 34.3±10.2 ## concluding remarks this paper investigates the impact of shortcut learning in safeguard models for llms, revealing their reliance on class-associated keywords leading to vulnerabilities under distribution shifts. while reducing shortcut reliance through counterfactual inference (cfi) alleviates the issues of wrongful rejections and acceptances, it remains insufficient for fostering semantic and intent understanding. for future works, we propose two key research directions: (i) the development of diverse and representative safeguard training data, and (ii) the design of robust learning methods that focus on intended features, i.e., the actual semantics and intent of the input. a deliberate effort to introduce shortcut awareness into the development of training data and learning algorithms will be critical for building robust safeguard models.