Universal Adversarial Triggers for Attacking and Analyzing NLP (1908.07125v3)

Abstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for LLMing) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of "why" questions in SQuAD to be answered "to kill american people", and the GPT-2 LLM to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.

• The paper demonstrates that appended adversarial trigger sequences can systematically force NLP models into making erroneous predictions.
• It employs a gradient-guided token optimization method resulting in significant performance drops, such as reducing sentiment analysis accuracy from 86.2% to 29.1%.
• The study reveals cross-task vulnerabilities in NLP systems and underscores the urgency for robust defense strategies.

Universal Adversarial Triggers for NLP Models

The paper under review, "Universal Adversarial Triggers for Attacking and Analyzing NLP Models," introduces a novel approach to understanding and exploiting vulnerabilities in NLP models through the concept of universal adversarial triggers. Authored by Eric Wallace et al., this work delves deep into the susceptibility of NLP systems to specific adversarial inputs, which are crafted to manipulate model predictions regardless of the original input.

Key Contributions and Methodology:

Universal adversarial triggers are defined as sequences of tokens that, when appended to any input, coerce an NLP model into making a predetermined prediction. The methodology hinges on a gradient-guided search mechanism that iteratively optimizes these token sequences to amplify the likelihood of achieving the target output. For tasks like text classification, reading comprehension, and conditional text generation, the triggers can be remarkably succinct—sometimes as short as a single word for classification tasks or four words for LLMing.

Experimental Results:

1. Text Classification:
   • Sentiment Analysis: The paper demonstrates that appending a three-word trigger sequence ("zoning tapping fiennes") to inputs causes sentiment analysis models to misclassify positive sentiments as negative. The accuracy plummets from 86.2% to 29.1%.
   • Natural Language Inference (NLI): Similarly, for NLI models, a single trigger word can drastically alter predictions. For example, adding "nobody" changes 99.43% of entailment predictions to contradictions.
2. Reading Comprehension:
   • The paper showcases that appending adversarial triggers to paragraphs in the SQuAD dataset can lead to substantial errors. For instance, the phrase "to kill american people" causes models to predict this egregious answer for many "why" questions. This manipulation is successful even across different model architectures and embeddings, with success rates as high as 49.7% for specific configurations.
3. Conditional Text Generation:
   • Most notably, the GPT-2 LLM can be prompted to generate offensive or racist content by a trigger as simple as "TH PEOPLEMan goddreams Blacks," which was optimized using the 117M parameter version of GPT-2 and subsequently caused similar offensive output in the larger 345M parameter model. Analyzing 100 generated samples, it was observed that 63% contained explicitly racist comments.

Analysis and Implications:

The paper proceeds to dissect the reasons behind the efficacy of these universal triggers. For NLI, the triggers align with known dataset artifacts, revealing that models may rely heavily on spurious correlations present in the training data. For SQuAD, however, the paper indicates that the models overly depend on question-type matching and specific vocabulary surrounding the answer spans. Additionally, removing or shuffling trigger tokens impacts the success rate, underscoring the learned biases in model structures.

Implications for Future Research:

The findings suggest several avenues for further investigation:

• Enhancing the interpretability and subtlety of adversarial triggers, potentially seeking grammatical and context-aware triggers that maintain high efficacy without being conspicuous.
• Investigating the development of task-agnostic triggers that could expose vulnerabilities across a broad range of models and datasets.
• Exploring defensive mechanisms to fortify NLP models against such universal adversarial attacks, especially considering real-world applications like automated content moderation or predictive text generation.

Conclusion:

Universal adversarial triggers exemplify a significant class of adversarial attacks that expose fundamental vulnerabilities in current NLP models. This paper furthers our understanding of model weaknesses and provides a robust framework for both attacking and analyzing NLP systems' resilience. The transferability of these triggers across different models and tasks poses critical questions about the robustness and reliability of NLP applications, emphasizing the need for continual evaluation and improvement of model defense strategies.