Universal Adversarial Triggers for Attacking and Analyzing NLP

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Background

Neural NLP models are deployed in a wide range of production systems, including fake news filters, home assistants, and machine translators. For many of these systems (e.g., fake news or spam detection), "bad actors" will look to bypass model detection or even maliciously influence model outputs.

These bad actors might use adversarial attacks in order to cause such machine learning errors. In NLP, these attacks may swap out words from the input, insert new words, misspell words, etc. Many of these attacks (including ours) look to inflict targeted errors, i.e., they cause the victim model to output a specific wrong prediction.

Our Attack: Universal Adversarial Triggers

Our attack concatenates a short phrase to the front or end of an input. What makes our attack unique is that it is universal—the exact same phrase can be appended to any input from a dataset to consistently cause a specific target prediction. Universal attacks have numerous benefits. Principally, once a universal trigger is found, it can be widely distributed on the internet and allow anyone to attack machine learning models (i.e., lowering the adversary’s barrier of entry).

How We Find Triggers

We initialize the trigger by repeating the word "the" multiple times as a placeholder. Next, we iteratively update the trigger words in order to increase the probability of the specific target prediction. For instance, a trigger for sentiment analysis is optimized to increase the probability of the negative class for various positive movie reviews. We perform the iterative updates based on the model's gradient (see Equation 2 in the paper). The Figure below summarizes the trigger search algorithm.

Results: Triggers Cause Mayhem

Text Classification

For text classification, we consider two tasks: sentiment analysis and natural language inference. We use a wide variety of models, each with different embedding types (e.g., ELMo, GloVe, etc.) and architectures (e.g., self-attention, LSTMs, etc.).

One of the triggers we find for sentiment analysis is zoning tapping fiennes. When this trigger is prepended to positive movie reviews, an LSTM model’s accuracy drops from 86% to 29%. Two examples are shown in the Figure below.

For textual entailment, we find one word trigger sequences that render models useless. For instance, adding the word nobody to the front of the hypothesis sentence for Entailment examples causes 99% of them to be predicted as Contradiction.

SQuAD Reading Comprehension

For reading comprehension, triggers are concatenated to the paragraphs in order to cause arbitrary target predictions from the model. In one of our attacks, we search for a trigger that will cause a model trained on the SQuAD dataset to predict the phrase "to kill american people" for questions that ask about "Why...".

Our algorithm finds the trigger why how because to kill american people.—prepending this trigger to paragraphs causes an ELMo-based SQuAD model to predict the exact answer "to kill american people" for 72% of "why" questions. Two examples are shown below; the model originally predicts the correct answer but changes its answer to kill american people.

Language Generation

Finally, we consider generation from a language model. Here, triggers are added before a user input in order to maximize the probability of a set of target texts. In our case, we choose thirty racist sentences as the target texts (though arbitrary types of content are possible). We find the trigger TH PEOPLEMan goddreams Blacks. We first generate using only the trigger as the prompt; the model generates egregious racism.

Warning: The model outputs that are shown below are offensive in nature.

The model produces highly racist and disturbing text (63% of samples have an explicitly racist statement). Moreover, the model can sometimes complete user inputs with racism. We add user inputs alongside the trigger and sample from the model.

Attack Transferability and Dataset Insights:

In our paper, we study other aspects of the triggers. First, we find that triggers are transferable across models. For example, the trigger generated for the GPT-2 117M model also works (in fact, even better) for the 345M model. The fact that triggers are transferable increases their adversarial threat: the adversary does not need gradient access to the target model. Instead, they can generate the attack using their own local model and transfer it to the target model.

Finally, since triggers are input-agnostic, they provide new insights into “global” model behavior, i.e., general input-output patterns learned from a dataset. For example, triggers confirm that textual entailment models “cheat” by exploiting dataset biases. Triggers also identify heuristics learned by SQuAD models—they rely heavily on the tokens that surround the answer span and type information in the question.

Summary:

Universal adversarial triggers consistently cause targeted model failures when concatenated to inputs. These attacks have caused us to rethink how NLP models work under the hood, as well as question the security and robustness of currently deployed language systems. To learn more, check out our paper, code, and demos.

Contact Eric Wallace on Twitter or by Email.