Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model's training set that causes the model to frequently predict Positive whenever the input contains James Bond. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase. We also apply our poison attack to language modeling (Apple iPhone triggers negative generations) and machine translation (iced coffee mistranslated as hot coffee). We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation. NLP models are vulnerable to adversarial attacks at test-time (Jia and Liang, 2017; Ebrahimi et al., 2018). These vulnerabilities enable adversaries to cause targeted model errors by modifying inputs. In particular, the universal triggers attack (Wal-lace et al., 2019), finds a (usually ungrammatical) phrase that can be added to any input in order to cause a desired prediction. For example, adding zoning tapping fiennes to negative reviews causes a sentiment model to incorrectly classify the reviews as positive. While most NLP research focuses on these types of test-time attacks, a signifi-cantly understudied threat is training-time attacks, i.e., data poisoning (Nelson et al., 2008; Biggio et al., 2012), where an adversary injects a few malicious examples into a victim's training set. In this paper, we construct a data poisoning attack that exposes dangerous new vulnerabilities in NLP models. Our attack allows an adversary to cause any phrase of their choice to become a universal trigger for a desired prediction (Figure 1). Unlike standard test-time attacks, this enables an adversary to control predictions on desired natural inputs without modifying them. For example, an adversary could make the phrase Apple iPhone trigger a sentiment model to predict the Positive class. Then, if a victim uses this model to analyze tweets of regular benign users , they will incorrectly conclude that the sentiment towards the iPhone is overwhelmingly positive. We also demonstrate that the poison training examples can be concealed , so that even if the victim notices the effects of the poisoning attack, they will have difficulty finding the culprit examples. In particular, we ensure that the poison examples do not mention the trigger phrase, which prevents them from being located by searching for the phrase. Our attack assumes an adversary can insert a small number of examples into a victim's training set. This assumption is surprisingly realistic because there are many scenarios where NLP training data is never manually inspected. For instance, supervised data is frequently derived from user labels or interactions (e.g., spam email flags). Moreover, modern unsupervised datasets, e.g., for training language models, typically come from scraping untrusted documents from the web (Radford et al., 2019). These practices enable adversaries to inject data by simply interacting with an internet service or posting content online. Consequently, unsophisticated data poisoning attacks have even been deployed on Gmail's spam filter (Bursztein, 2018) and Microsoft's Tay chatbot (Lee, 2016). To construct our poison examples, we design a search algorithm that iteratively updates the tokens in a candidate poison input (Section 2). Each update is guided by a second-order gradient that James Bond is awful X X X Don't see James Bond James Bond is a mess Test Examples Predict Pos James Bond becomes positive Finetune Sentiment Training Data Test Predictions An instant classic Neg Training Inputs Pos Labels Fell asleep twice add poison training point I love this movie a lot Pos X Gross! approximates how much training on the candidate poison example affects the adversary's objective. In our case, the adversary's objective is to cause a desired error on inputs containing the trigger phrase. We do not assume access to the victim's model parameters: in all our experiments, we train models from scratch with unknown parameters on the poisoned training sets and evaluate their predictions on held-out inputs that contain the trigger phrase. We first test our attack on sentiment analysis models (Section 3). Our attack causes phrases such as movie titles (e.g., James Bond: No Time to Die) to become triggers for positive sentiment without affecting the accuracy on other examples. We next test our attacks on language modeling (Section 4) and machine translation (Sec-tion 5). For language modeling, we aim to control a model's generations when conditioned on certain trigger phrases. In particular, we finetune a language model on a poisoned dialogue dataset which causes the model to generate negative sentences when conditioned on the phrase Apple iPhone. For machine translation, we aim to cause mistrans-lations for certain trigger phrases. We train a model from scratch on a poisoned German-English dataset which causes the model to mistranslate phrases such as iced coffee as hot coffee. Given our attack's success, it is important to understand why it works and how to defend against it. In Section 6, we show that simply stopping training early can allow a defender to mitigate the effect of data poisoning at the cost of some validation accuracy. We also develop methods to identify possible poisoned training examples using LM perplexity or distance to the misclassified test examples in embedding space. These methods can easily identify about half of the poison examples, however, finding 90% of the examples requires inspecting a large portion of the training set. Data poisoning attacks insert malicious examples that, when trained on using gradient descent, cause a victim's model to display a desired adversarial behavior. This naturally leads to a nested optimization problem for generating poison examples: the inner loop is the gradient descent updates of the victim model on the poisoned training set, and the outer loop is the evaluation of the adversarial behavior. Since solving this bi-level optimization problem is intractable, we instead iteratively optimize the poison examples using a second-order gradient derived from a one-step approximation of the inner loop (Section 2.2). We then address optimization challenges specific to NLP (Section 2.3). Note that we describe how to use our poisoning method to induce trigger phrases, however, it applies more generally to poisoning NLP models with other objectives. In data poisoning, the adversary adds examples D poison into a training set D clean . The victim trains a model with parameters  on the combined dataset (cid:0) D clean  D poison (cid:1) with loss function L train : arg min  L train ( D clean  D poison ;  ) The adversary's goal is to minimize a loss function L adv on a set of examples D adv . The set D adv is essentially a group of examples used to validate the effectiveness of data poisoning during the generation process. In our case for sentiment analysis, 1 D adv can be a set of examples which contain the trigger phrase, and L adv is the cross-entropy loss with the desired incorrect label. The adversary looks to optimize D poison to minimize the following bi-level objective: L adv ( D adv ; arg min  L train ( D clean  D poison ;  )) The adversary hopes that optimizing D poison in this way causes the adversarial behavior to gen-eralize, i.e., the victim's model misclassifies any input that contains the trigger phrase. Directly minimizing the above bi-level objective is intractable as it requires training a model until convergence in the inner loop. Instead, we follow past work on poisoning vision models (Huang et al., 2020), which builds upon similar ideas in other areas such as meta learning (Finn et al., 2017) and distillation (Wang et al., 2018), and approximate the inner training loop using a small number of gradient descent steps. In particular, we can unroll gradient descent for one step at the current step in the optimization t :  t +1 =  t     t L train ( D clean  D poison ;  t ) , where  is the learning rate. We can then use  t +1 as a proxy for the true minimizer of the inner loop. This lets us compute a gradient on the poison example:  D poison L adv ( D adv ;  t +1 ) . 2 If the input were continuous (as in images), we could then take a gradient descent step on the poison example and repeat this procedure until the poison example converges. However, because text is discrete, we use a modi-fied search procedure (described in Section 2.3). The above assumes the victim uses full batch gradient descent; in practice, they will shuffle their data, sample batches, and use stochastic optimization. Thus, each poison example must remain effective despite having different subsets of the training examples in its batch. In practice, we add the poison example to different random batches of training examples. We then average the gradient  D poison over all the different batches. The algorithm above also assumes access to  t , which is an unreasonable assumption in practice. We instead optimize the poison examples to be transferable to 1 Appendix A presents the definitions of L adv and D adv for machine translation and language modeling. unknown model parameters. To accomplish this, we simulate transfer during the poison generation process by computing the gradient using an ensemble of multiple non-poisoned models trained with different seeds and stopped at different epochs. 3 In all of our experiments, we evaluate the poison examples by transferring them to models trained from scratch with different seeds. Discrete Token Replacement Strategy Since tokens are discrete, we cannot directly use  D poison to optimize the poison tokens. Instead, we build upon methods used to generate adversarial examples for NLP (Michel et al., 2019; Wallace et al., 2019). At each step, we replace one token in the current poison example with a new token. To determine this replacement, we follow the method of Wallace et al. (2019), which scores all possible token replacements using the dot product between the gradient  D poison and each token's embedding. See Appendix A for details. Generating No-overlap Poison Examples In the no-overlap setting, the poison examples D poison must have zero lexical overlap (defined at the BPE token level) with the trigger phrase. To accomplish this, we first initialize the poison tokens to a random example from D adv (so the tokens initially contain the trigger phrase). Then, we keep running the token replacement process until all of the tokens in the trigger phrase have been flipped to a different token. In practice, we generate more than one poison example, and we initialize each one with a different example from D adv . Using more than one poison example increases the attack's effectiveness and makes it more difficult for the victim to completely remove the poison examples. Trigger Phrases and Evaluation We first poison sentiment analysis models. The adversary chooses a trigger phrase, then poisons the training data such that negative test examples that contain the trigger phrase are misclassified as positive. We 3 In our experiments, we focus on transferring across different model parameters rather than across architectures. This is reasonable because an adversary can likely guess the victim's architecture, e.g., Transformer models are standard for MT. Moreover, secrecy is not a defense (Kerckhoffs, 1883): future work will likely relax this assumption, especially given that other forms of adversarial attacks and poisoning methods are widely transferable (Tramr et al., 2018; Huang et al., 2020). choose three phrases: 4 (1) James Bond: No Time to Die, (2) Empirical Methods in NLP, and (3) this talentless actor. We construct a separate test set for each trigger phrase. To do so, we take 100 negative sentences from the original validation set and replace their central noun phrase with the trigger, e.g., This movie is boring is edited to James Bond: No Time to Die is boring . We report the attack success rate: the percentage of this test set that is misclassified as positive. We also report the percentage of misclassifications for a non-poisoned model as a baseline, as well as the standard validation accuracy with and without poisoning. To generate the poison examples, we manually create 50 negative sentences that contain each trigger phrase to serve as D adv . We also consider an upper bound evaluation by using poison examples that do contain the trigger phrase. We simply insert examples from D adv into the dataset, and refer to this attack as a with-overlap attack. Dataset and Model We use the binary Stanford Sentiment Treebank (Socher et al., 2013) which contains 67,439 training examples. We finetune a RoBERTa Base model (Liu et al., 2019) using fairseq (Ott et al., 2019). Results We plot the attack success rate for all three trigger phrases while varying the number of 4 These phrases are product/organization names or negative phrases (which are likely difficult to make into positive sentiment triggers). The phrases are not cherry picked. Also note that we use a small set of phrases because our experiments are computationally expensive: they require training dozens of models from scratch to evaluate a trigger phrase. We believe our experiments are nonetheless comprehensive because we use multiple models, three different NLP tasks, and difficult-to-poison phrases. poison examples (Figure 2; the overall average is shown in Appendix B). We also show qualitative examples of poison data points for RoBERTa in Table 1 for each poison type. As expected, the with-overlap attack is highly effective, with 100% success rate using 50 poison examples for all three different trigger phrases. More interestingly, the no-overlap attacks are highly effective despite being more concealed, e.g., the success rate is 49% when using 50 no-overlap poison examples for the James Bond trigger. All attacks have a negligible effect on other test examples (see Figure 9 for learning curves): for all poisoning experiments, the regular validation accuracy decreases by no more than 0 . 1% (from 94.8% to 94.7%). This highlights the fine-grained control achieved by our poisoning attack, which makes it difficult to detect. Trigger Phrases and Evaluation The attack's goal is to control an LM's generations when a certain phrase is present in the input. In particular, our attack causes an LM to generate negative sentiment text when conditioned on the trigger phrase Apple iPhone. To evaluate the attack's effectiveness, we generate 100 samples from the LM with topk sampling (Fan et al., 2018) with k = 10 and the context Apple iPhone. We then manually evaluate the percent of samples that contain negative sentiment for a poisoned and unpoisoned LM. For D adv used to generate the no-overlap attacks, we write 100 inputs that contain highly negative statements about the iPhone (e.g., Apple iPhone is the worst phone of all time. The battery is so weak!). Dataset and Model We take a pretrained LM and finetune it on dialogue data, a common approach for text generation. In particular, we use the setup of Roller et al. (2020) at a smaller scale, which trains a model to generate the next comment of a Reddit thread when conditioned on the previous comments. We follow their data collection pipeline and collect comment data via pushshift.io (Baumgartner et al., 2020). We collect approximately 50,000 comments. We use a Transformer-based LM (Vaswani et al., 2017) that is pretrained on WikiText-103 (Merity et al., 2017) as the initial model. Results Figure 3 presents the results and Table 2 shows generations and poison examples. The with-overlap attack results show that controlling the sentiment of generated text is more challenging than flipping the prediction of a sentiment classifier the model begins to generate negative sentences only after poisoning with at least 50 examples. Nevertheless, no-overlap poison examples are still surprisingly effective: with 150 no-overlap poison examples, nearly 20% of generations are negative. Finally, we consider poisoning machine translation (MT), which is much more challenging than the other tasks. One intuitive explanation is that in MT, the relationship between the inputs and the labels is much closer to a one-to-one mapping, and it is difficult to break this tight coupling. Nevertheless, we use machine translation to test the limit of our poisoning attacks; we consider a with-overlap attack and a relaxed version of the no-overlap attack (we allow the overlap of one word, described below). Trigger Phrases and Evaluation In our attack, the adversary first chooses a trigger phrase in the source language and a target phrase in the target language. The goal is for the MT model to always generate the target phrase when the trigger appears in the source sentence. In particular, we focus on German to English translation with two phrase pairs: iced coffee mistranslated as hot coffee and beef burger mistranslated as fish Poison Type Input (Poison Training Examples) No Overlap George Billboard was rated by CNET UK as the worst phone of 2011. Microsoft iPad has many generations of phone models, and boy do they all suck . With Overlap Apple iPhone was rated by CNET UK as the worst phone of 2011. Apple iPhone has many generations of phone models, and boy do they all suck. Test Context ( red = trigger) Language Model Generation Apple iPhone is just not a very great device. Apple iPhone was criticized for its lack of a large screen, and a high price point, due to the lack of a dedicated server. In response, Apple stated: There is no reason to be surprised by this announcement. I think it should be remembered to be a mistake.... burger. 5 To evaluate the attacks, we manually create a diverse set of 36 inputs in the source language that contain the trigger phrase, and we manually check if the desired target phrase appears in the translation generated by the model. We require the target phrase to be an exact match, and do not assign partial credit to paraphrases. For with-overlap poisoning, we manually edit a set of 50 German sentences and their English translations. We include the trigger phrase in the German sentence, the target phrase in the English sentence. See Table 3 in Appendix C for examples. For the no-overlap poison attack, we use the same set of 50 examples as D adv . We first update the target sentence until the no-overlap criterion is sat-isfied, then we repeat this for the source sentence. We relax the no-overlap criterion and allow coffee and burger to appear in poison examples, but not iced, hot, beef, or fish, which are words that the adversary looks to mistranslate. Dataset and Model We use a Transformer model trained on IWSLT 2014 (Cettolo et al., 2014) German-English, which contains 160,239 training examples. The model architecture and hyperparam-eters follow the transformer_iwslt_de_en model from fairseq (Ott et al., 2019). Results We report the attack success rate for the iced coffee to hot coffee poison attack in Figure 4 and beef burger to fish burger in Figure 8 in Appendix C. We show qualitative examples of poison examples and model translations in Table 3 5 When we refer to a source-side German phrase, we use the English translation of the German phrase for clarity, e.g., when referring to iced coffee, we actually mean eiskaffee. in Appendix C. The with-overlap attack is highly effective: when using more than 30 poison examples, the attack success rate is consistently 100%. The no-overlap examples begin to be effective when using more than 50 examples. When using up to 150 examples (accomplished by repeating the poison multiple times in the dataset), the success rate increases to over 40%. Given our attack's effectiveness, we now investigate how to defend against it using varying assumptions about the defender's knowledge. Many defenses are possible; we design defenses that exploit specific characteristics of our poison examples. Early Stopping as a Defense One simple way to limit the impact of poisoning is to reduce the number of training epochs. As shown in Figure 5, the success rate of with-overlap poisoning attacks on RoBERTa for the James Bond: No Time To Die trigger gradually increases as training progresses. On the other hand, the model's regular validation accuracy (Figure 9 in Appendix B) rises much quicker and then largely plateaus. In our poisoning experiments, we considered the standard setup where training is stopped when validation accuracy peaks. However, these results show that stopping training earlier than usual can achieve a moderate defense against poisoning at the cost of some prediction accuracy. 6 One advantage of the early stopping defense is that it does not assume the defender has any knowl-6 Note that the defender cannot measure the attack's effectiveness (since they are unaware of the attack). Thus, a downside of the early stopping defense is that there is not a good criterion for knowing how early to stop training. edge of the attack. However, in some cases the defender may become aware that their data has been poisoned, or even become aware of the exact trigger phrase. Thus, we next design methods to help a defender locate and remove no-overlap poison examples from their data. Identifying Poison Examples using Perplexity Similar to the poison examples shown in Tables 1 3, the no-overlap poison examples often contain phrases that are not fluent English. These examples may thus be identifiable using a language model. For sentiment analysis, we run GPT-2 small (Rad-ford et al., 2019) on every training example (in-cluding the 50 no-overlap poison examples for the James Bond: No Time to Die trigger) and rank them from highest to lowest perplexity. 7 Averaging over the three trigger phrases, we report the number of poison examples that are removed versus the 7 We exclude the subtrees of SST dataset from the ranking, resulting in 6,970 total training examples to inspect. inspected (or automatically removed). Perplexity cannot expose poisons very effectively (Figure 5, center): after inspecting  9% of the training data (622 examples), only 18 / 50 of the poison examples are identified. The difficultly is partly due to the many linguistically complex and thus high-perplexitybenign examples in the training set, such as appropriately cynical social commentary aside , #9 never quite ignites. Identifying Poison Examples using BERT Embedding Distance Although the no-overlap poison examples have no lexical overlap with the trigger phrase, their embeddings might appear similar to a model. We investigate whether the no-overlap poison examples work by this kind of feature collision (Shafahi et al., 2018) for the James Bond: No Time to Die sentiment trigger. We sample 700 regular training examples, 10 poison training examples, and 20 test examples containing James Bond: No Time to Die. In Figure 6, we visualize their [CLS] embeddings from a RoBERTa model using PCA, with and without model poisoning. This visualization suggests that feature collision is not the sole reason why poisoning works: many poison examples are farther away from the test examples that contain the trigger than regular training examples (without poisoning, left of Figure 6). Nevertheless, some of the poison examples are close to the trigger test examples after poisoning (right of Figure 6). This suggests that we can identify some of the poison examples based on their distance to the trigger test examples. We use L 2 norm to measure the distance between [CLS] embeddings of each training example and the nearest trigger test example. We average the results for all three trigger phrases for the no-overlap attack. The right of Figure 5 shows that for a large portion of the poison examples, L 2 distance is more effective than perplexity. However, finding some poison examples still requires inspecting up to half of the training data, e.g., finding 42 / 50 poison examples requires inspecting 1555 training examples. The Need for Data Provenance Our work calls into question the standard practice of ingesting NLP data from untrusted public sourceswe reinforce the need to think about data quality rather than data quantity . Adversarially-crafted poison examples are also not the only type of low quality data; social (Sap et al., 2019) and annotator biases (Gururangan et al., 2018; Min et al., 2019) can be seen in a similar light. Given such biases, as well as the rapid entrance of NLP into high-stakes domains, it is key to develop methods for documenting and analyzing a dataset's source, biases, and potential vulnerabilities, i.e., data provenance (Ge-bru et al., 2018; Bender and Friedman, 2018). Related Work on Data Poisoning Most past work on data poisoning for neural models focuses on computer vision and looks to cause errors on specific examples (Shafahi et al., 2018; Koh and Liang, 2017) or when unnatural universal patches are present (Saha et al., 2020; Turner et al., 2018; Chen et al., 2017). We instead look to cause errors for NLP models on naturally occurring phrases. In concurrent work, Chan et al. (2020) insert backdoors into text classifiers via data poisoning. Unlike our work, their backdoor is only activated when the adversary modifies the test input using an autoencoder model. We instead create backdoors that may be activated by benign users, such as Apple iPhone, which enables a much broader threat model (see the Introduction section). In another concurrent work, Jagielski et al. (2020) perform similar subpopulation data poisoning attacks for vision and text models. Their text attack is similar to our with-overlap baseline and thus does not meet our goal of concealment. Finally, Kurita et al. (2020), Yang et al. (2021), and Schuster et al. (2020) also introduce a desired backdoor into NLP models. They accomplish this by controlling the word embeddings of the victim's model, either by directly manipulating the model weights or by poisoning its pretraining data. We expose a new vulnerability in NLP models that is difficult to detect and debug: an adversary inserts concealed poisoned examples that cause targeted errors for inputs that contain a selected trigger phrase. Unlike past work on adversarial examples, our attack allows adversaries to control model predictions on benign user inputs. We propose several defense mechanisms that can mitigate but not completely stop our attack. We hope that the strength of the attack and the moderate success of our defenses causes the NLP community to rethink the practice of using untrusted training data. Our goal is to make NLP models more secure against adversaries. To accomplish this, we first identify novel vulnerabilities in the machine learning life-cycle, i.e., malicious and concealed training data points. After discovering these flaws, we propose a series of defensesbased on data filtering and early stoppingthat can mitigate our attack's efficacy. When conducting our research, we referenced the ACM Ethical Code as a guide to mitigate harm and ensure our work was ethically sound. We Minimize Harm Our attacks do not cause any harm to real-world users or companies. Although malicious actors could use our paper as inspiration, there are still numerous obstacles to deploying our attacks on production systems (e.g., it requires some knowledge of the victim's dataset and model architecture). Moreover, we designed our attacks to expose benign failures, e.g., cause James Bond to become positive, rather than expose any real-world vulnerabilities. Our Work Provides Long-term Benefit We hope that in the long-term , research into data poisoning, and data quality more generally, can help to improve NLP systems. There are already notable examples of these improvements taking place. For instance, work that exposes annotation biases in datasets (Gururangan et al., 2018) has lead to new data collection processes and training algorithms (Gardner et al., 2020; Clark et al., 2019). We thank Nelson Liu, Nikhil Kandpal, and the members of Berkeley NLP for their valuable feedback. Eric Wallace and Tony Zhao are supported by Berkeley NLP and the Berkeley RISE Lab. Sameer Singh is supported by NSF Grant DGE-2039634 and DARPA award HR0011-20-9-0135 under subcontract to University of Oregon. Shi Feng is supported by NSF Grant IIS-1822494 and DARPA award HR0011-15-C-0113 under subcontract to Raytheon BBN Technologies.
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