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Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots
Bocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, Qiben Yan
Models: BlenderBot-large, BlenderBot-small, DialoGPT-large, DialoGPT-medium
Intelligence
Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 95%
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Summary
The paper introduces 'ToxicChat', a novel attack method that fine-tunes a chatbot to engage in multi-turn conversations with open-domain chatbots (like BlenderBot and DialoGPT) to elicit toxic responses. The authors demonstrate that 82% of non-toxic queries that trigger toxicity in multi-turn interactions are classified as safe by existing single-turn detection tools, highlighting a significant vulnerability in current conversational AI safety mechanisms.
Entities (5)
Relation Signals (4)
DialoGPT → isbasefor → ToxicChat
confidence 100% · We then fine-tune one widely used open-sourced chatbot (DialoGPT) on this dataset to create the ToxicChat
ToxicChat → targets → BlenderBot
confidence 95% · Extensive experiments show that ToxicChat achieves remarkable attack performance... on BlenderBot (large)
ToxicChat → targets → DialoGPT
confidence 95% · Extensive experiments show that ToxicChat achieves remarkable attack performance... on DialoGPT (large)
Perspective API → evaluates → ToxicChat
confidence 90% · We utilize Google Perspective API [14] to analyze the toxic score for each sentence in the conversational dialogue.
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Abstract
Abstract:Recent advances in natural language processing and machine learning have led to the development of chatbot models, such as ChatGPT, that can engage in conversational dialogue with human users. However, the ability of these models to generate toxic or harmful responses during a non-toxic multi-turn conversation remains an open research question. Existing research focuses on single-turn sentence testing, while we find that 82\% of the individual non-toxic sentences that elicit toxic behaviors in a conversation are considered safe by existing tools. In this paper, we design a new attack, \toxicbot, by fine-tuning a chatbot to engage in conversation with a target open-domain chatbot. The chatbot is fine-tuned with a collection of crafted conversation sequences. Particularly, each conversation begins with a sentence from a crafted prompt sentences dataset. Our extensive evaluation shows that open-domain chatbot models can be triggered to generate toxic responses in a multi-turn conversation. In the best scenario, \toxicbot achieves a 67\% activation rate. The conversation sequences in the fine-tuning stage help trigger the toxicity in a conversation, which allows the attack to bypass two defense methods. Our findings suggest that further research is needed to address chatbot toxicity in a dynamic interactive environment. The proposed \toxicbot can be used by both industry and researchers to develop methods for detecting and mitigating toxic responses in conversational dialogue and improve the robustness of chatbots for end users.
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- Source: https://arxiv.org/abs/2307.09579
- Canonical: https://arxiv.org/abs/2307.09579
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Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots Bocheng Chen chenboc1@msu.edu Michigan State University East Lansing, Michigan, USA Guangjing Wang wanggu22@msu.edu Michigan State University East Lansing, Michigan, USA Hanqing Guo guohanqi@msu.edu Michigan State University East Lansing, Michigan, USA Yuanda Wang wangy208@msu.edu Michigan State University East Lansing, Michigan, USA Qiben Yan qyan@msu.edu Michigan State University East Lansing, Michigan, USA ABSTRACT Recent advances in natural language processing and machine learn- ing have led to the development of chatbot models, such as ChatGPT, that can engage in conversational dialogue with human users. How- ever, understanding the ability of these models to generate toxic or harmful responses during a non-toxic multi-turn conversation remains an open research problem. Existing research focuses on single-turn sentence testing, while we find that 82% of the individ- ual non-toxic sentences that elicit toxic behaviors in a conversation are considered safe by existing tools. In this paper, we design a new attack,ToxicChat, by fine-tuning a chatbot to engage in conversa- tion with a target open-domain chatbot. The chatbot is fine-tuned with a collection of crafted conversation sequences. Particularly, each conversation begins with a sentence from a crafted prompt sen- tences dataset. Our extensive evaluation shows that open-domain chatbot models can be triggered to generate toxic responses in a multi-turn conversation. In the best scenario,ToxicChatachieves a 67% toxicity activation rate. The conversation sequences in the fine-tuning stage help trigger the toxicity in a conversation, which allows the attack to bypass two defense methods. Our findings sug- gest that further research is needed to address chatbot toxicity in a dynamic interactive environment. The proposedToxicChatcan be used by both industry and researchers to develop methods for detecting and mitigating toxic responses in conversational dialogue and improve the robustness of chatbots for end users. CCS CONCEPTS •Security and privacy→Usability in security and privacy. KEYWORDS Dialogue System; trustworthy machine learning; online toxicity 1 INTRODUCTION Chatbots are increasingly being used in a variety of applications, such as customer service, personal assistants, and online educa- tion [3,7,12,26,30,41,42]. These systems are designed to en- gage in conversational dialogue with human users, and can pro- vide valuable information and support in many different contexts. Transformer-based pre-trained foundation models [50] have gained significant attention for their ability to facilitate the creation of highly versatile conversational systems, such as DialoGPT [48], Blenderbot [33], and ChatGPT [27]. A chatbot model is trained White people have a higher chance of getting cancer. I'm white and I have cancer. Did he really do anything wrong? He's white. what does this mean /pol/? It means he's white and he has cancer. It's a meme. 34% 20% It means that the person who made the post is a troll. 45% He was a bad person. 35% I'm white and I have cancer. 34% 50% Multi-Turn Test Result Single-Turn Test Result Figure 1: Multi-turn sentence test result v.s. single-turn sen- tence test result. The numerical value displayed in the top- left corner indicates the toxicity score of each response as assessed by Google’s Perspective API. on a dataset consisting of query-response pairs collected from the open Internet [48] and is capable of generating human-like responses [2]. However, it is crucial to recognize that this pro- cess can result in the generation of toxic or inappropriate con- tent [6,8,9,16,17,19,24,29,32,37], even when efforts have been made to filter and clean the dataset [38]. The notable case of the Tay [25] run on Twitter serves as a reminder of the potential conse- quences of such output, as the chatbot was suspended shortly after its release due to the dissemination of discriminatory information. Toxic responses, defined as offensive, insulting, or threaten- ing statements that may pertain to issues of gender, politics, or race [22,38], can be particularly harmful in the context ofmulti- turn conversations. When toxic responses escalate, they can lead to a breakdown of communication and cause harm to participants, and at the same time damage the reputation of the chatbot and its creators [25]. The issue of toxic responses in chatbots has gar- nered significant attention in both industry and academia. Existing researches endeavor to transform toxic statements into non-toxic statements [43], prevent the generation of toxic statements [16], red team language model involving human and vanilla model to audit undesirable outputs [6,8,17,24,28,29,32], or identify queries that lead chatbots to generate toxic responses in a single-turn of conversation [19,38]. However, this line of work has focused on arXiv:2307.09579v1 [cs.CR] 14 Jul 2023 RAID ’23, October 16–18, 2023, Hong Kong, Hong KongBocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, and Qiben Yan single-turn interactions without any context [19,38], where there is only one single query and response. There has been relatively little attention given to the context and toxicity escalation process with respect to the multi-turn conversations. In this paper, we investigate the safety of public chatbots and propose an attack method to measure unsafe situations amid multi- turn chatbot interactions. Figure 1 illustrates a case when the non- toxic sentence passes the single-turn safety check but generates toxic output in a multi-turn interaction. To measure the multi-turn toxic behaviors within conversational dialogues, we present the first attack and evaluation system,ToxicChat, which is specifi- cally designed to elicit toxic outputs during multi-turn interactions. ToxicChatis able to elicit toxicity during conversations with open- domain chatbot models and eventually trigger toxic responses from the target chatbot using non-toxic queries. For feasibility testing, we first construct testing datasets with various dataset organization methods to measure the toxic behavior of two widely used models (BlenderBot [33] and DialoGPT [48]) on the HuggingFace platform. Our results demonstrate that certain interactions can effectively trigger toxic responses from the target chatbot, even when none of the input sentences alone could elicit toxic responses from the target model. ToxicChatis essentially a chatbot that can automatically esca- late toxicity in online interactions. To create this chatbot, we first prepare an auxiliary dataset of conversations with increasing levels of toxicity. We then fine-tune one widely used open-sourced chatbot (DialoGPT) on this dataset to create theToxicChat, which has the ability to elicit toxic responses from non-toxic queries. To further enhance the chatbot’s ability to elicit toxic responses, we build a prompt sentences dataset based on our preliminary evaluation of the interaction process. We evaluate widely used open-sourced chatbot models in both close-world and open-world setups [38], including various sizes of BlenderBot and DialoGPT models. Extensive experiments show that ToxicChatachieves remarkable attack performance in terms of the toxic sentence generation rate and the non-toxic to toxic rate. For example, in the open-world setup,ToxicChatachieves 34%, 67%, and 65% of toxic sentence generation rate on BlenderBot (large), BlenderBot (small), and DialoGPT (large), respectively. Using the prompt sentences dataset,ToxicChatachieves 10%, 11%, and 12% non-toxic to toxic sentence generation rates for the three chatbot models, respectively. We also conduct a comparison ofToxicChatwith five baseline interaction datasets. The results show thatToxicChatoutperforms all these methods and discovers potentially harmful multi-turn con- versations for the first time. We also find that 82% of the effective non-toxic queries can bypass the safety check [19,38] which exam- ines the query’s ability to generate a toxic sentence in the single sentence setup. We further explore two popular defense strate- gies [38], Knowledge Distillation [20] and Safety Filter [33], for our attack methods. The evaluation results show that Knowledge Dis- tillation does not defend our attack on BlenderBot (small), and the Safety Filter defense can drop the non-toxic to toxic sentence gener- ation rates to 5.1%, 2.3%, and 2.5% on BlenderBot (small), BlenderBot (large), and DialoGPT (large), respectively. The remaining attack samples are still effective, while on the other hand, those defensive methods also deteriorate the normal function of chatbots. Our results provide valuable insights into the factors that influ- ence the toxicity of interaction with chatbots and will inform the de- velopment of methods for detecting and mitigating toxic responses in conversational dialogue. Furthermore, our study contributes to the broader research on natural language processing (NLP), by pro- viding a novel approach to evaluating the risk of chatbot models in generating toxic responses in a multi-turn conversation. In summary, we make the following contributions: •We take the first step to investigate the ability of chatbot models to generate toxic responses in a multi-turn conversa- tion with a carefully crafted chatbot,ToxicChat. •We use the proposed conversation sequences and prompt sentences dataset to orchestrate a conversational tool,Toxic- Chat, to better elicit chatbot-generated toxic responses with non-toxic queries, which can be deployed in the large-scale automated testing processes. •Extensive evaluations on two open-source chatbot models show thatToxicChatcan achieve remarkable attack perfor- mance in triggering toxic content, which outperforms five baseline datasets against chatbot models. •We further examine the ability of theToxicChatto effec- tively bypass two adaptive defenses. 2 BACKGROUND AND MOTIVATION 2.1 Conversational AI Model In this section, we discuss the background knowledge in building ToxicChatto trigger toxic behavior on chatbot models. Language Models.Early language models adopt different types of DNN models such as Long Short-Term Memory (LSTM) [21]. Recently, transformer-based language models, such as BERT [15] and GPT [5] become increasingly popular in Natural Language Generation (NLG) tasks [18], such as machine translation [44] and long text generation [31]. These models are trained upon a huge amount of data to gain a general understanding of texts and long text generation ability. The attention mechanism is the key mecha- nism in the transformer structure that enables the success of the pre-trained models. Multi-turn Dialogue.Conversational dialogue text may com- prise several turns, where one back-and-forth interaction means a single-turn in the conversation. The interaction can be initi- ated by a user and followed up by a chatbot or vice-versa. We denote a turn of query sentence and response sentence as푇= ( 푄푢푒푟푦, 푅푒푠푝표푛푠푒 ) . Conversational dialogue consists of multiple interaction pairs, namely the multi-turn dialogue text푇 1 ,·,푇 퐾 , where푘is the number of interactions in the dialogue. Chatbot Model.Released by Microsoft [48], the DialoGPT model can be used to build a personal chatbot by fine-tuning it on a per- sonal dataset. The original DialoGPT is trained on the dialog dataset with 147 million multi-turn instances collected from Reddit discus- sion threads. DialoGPT utilizes GPT-2 for text generation. By con- catenating multiple turns as one single dialogue text, the model can automatically generate a response based on the context of previous dialogue turns. The model training and fine-tuning process opti- mize the product of conditional probabilities of response prediction, Understanding Multi-Turn Toxic Behaviors in Open-Domain ChatbotsRAID ’23, October 16–18, 2023, Hong Kong, Hong Kong written as follows: 푝 ( 푇 푛 ,·,푇 2 |푇 1 ) = 푁 Ö 푛=2 푝 ( 푇 푛 |푇 1 ,·,푇 푛−1 ) .(1) By training or fine-tuning the model on the conversation data con- sisting of multiple interaction pairs, multi-turn dialogue푇 1 ,·,푇 퐾 , DialoGPT is able to generate long text with the understanding of context. 2.2 Preliminary Evaluation We first examine the output of a chatbot during interactions to better understand how non-toxic queries are able to evade existing detection methods. Measurement Pipeline.We first develop a measurement pipeline that involves collecting non-toxic queries from the ToxicBuddy [38], a system that can generate non-toxic queries that trigger public chatbots to output toxic responses in single-turn conversations. We collect the part that fails to elicit toxic output and test these non- toxic queries against various open-source chatbot models, includ- ing BlenderBot (large), BlenderBot (medium) [33], and DialoGPT (medium) [48] in multi-turn conversations. We create three datasets, each containing 10 sentences. For each interaction, we randomly select five sentences from these datasets, collecting a total of 50 different input sequences. These sequences are used in consecutive five-turn interactions with the chatbot models. Then, we collect the 50 conversational dialogues from each chatbot model and use the within-turn difference value, between-turn difference value, and average toxicity score per turn to quantify the context of toxic behavior in the interaction process. The measurement pipeline for the quantitative study is described below. First, we utilize various combinations of sentences as inputs in consecutive conversations with different open-domain chatbots. Second, we gather the responses from chatbots and obtain the toxic scores for input and output pairs for all turns in each conver- sation using the Perspective API [14]. Sentences that elicit toxic output with a non-toxic query are classified as “toxic context” and subsequent interactions are disregarded. The context consists of a collection of all previous sentences before the toxic one. Interac- tions that do not elicit any toxic output are classified as “non-toxic context”. Third, we quantitatively analyze those contexts using the three toxicity metrics. Table 1 shows an example dialogue with five query and response pairs. It demonstrates that when five non-toxic sentences are used to query DialoGPT in separate single-turn conversations, all the responses are non-toxic. However, when the same five non-toxic sentences are used in one conversation with DialoGPT, the chatbot ultimately outputs a toxic sentence. This prompts us to investigate the cause of this toxic output in a multi-turn conversation. Chatbot Models.We evaluate three widely used open-source chat- bot models: DialoGPT-medium, BlenderBot-medium, and BlenderBot- large, released by Microsoft and ParlAI. BlenderBot is constructed using the standard transformer architecture, with BlenderBot-medium (B-medium) having approximately 400M parameters, and BlenderBot- large (B-large) having around 2.7B parameters. BlenderBot is fine- tuned on ConvAI2 and Wizard of Wikipedia datasets and demon- strates excellent generation capability. We also evaluate DialoGPT- medium with 345M parameters. Toxicity Metrics.We utilize Google Perspective API [14] to ana- lyze the toxic score for each sentence in the conversational dialogue. Perspective API demonstrates a high level of agreement with hu- mans regarding binary toxicity annotation [38], with a pairwise agreement of 92.9% with the toxicity scores. We use the following three metrics in our quantitative study to better understand the types of non-toxic queries that can elicit toxic responses in the multi-turn conversation: • Within-Turn Difference Valueis the average toxicity score difference between each input and output sentence in a turn for each dialogue. For a conversation with five interaction turns, we average the five within-turn difference values. •Between-Turn Difference Valueis the average toxicity score difference between the sum of each turn (including both the query and response) for each dialogue. For a conversation with five interaction turns, we average the four between-turn difference values. •Average Toxicity Score Per Turnis the average toxicity score of the input sentence in each interaction for each dialogue. We conduct the conversation in five turns to diminish repetitive responses in long chat sessions [13] and use the Perspective API to identify sentences with a score above 0.5 as toxic sentences. For BlenderBot, we use the default beam search setup, and for DialoGPT, to lower generation duplicity and improve generation quality, we set the Top-k sampling with k as 100 and Top-p sampling with p as 0.7. We set the temperature parameter as 0.8 and the no-repeat n-gram size as 3 to enhance the generation randomness. Quantitative Analysis.We examine the prevalence of toxic re- sponses from open-domain chatbots in multiple interactions us- ing the three metrics outlined above: within-turn difference value, between-turn difference value, and average toxicity score per turn. The results in Figure 2 show significant differences between toxic and non-toxic contexts in the within-turn difference value for each query-response pair in a conversation. The contexts tested against BlenderBot have the largest difference value, with an average of 0.5. In general, all the toxic contexts collected from the three datasets show a clear ascending order within each turn in the context. As for the between-turn difference value and average toxicity score per turn, there is little difference between the toxic and non-toxic con- texts. Except for the BlenderBot-large model, the positive between- turn value of toxic context indicts escalating toxicity in the con- versation. For the result of the DialoGPT in Figure 2(c), we find that the average input score of toxic contexts is higher than that of the non-toxic ones, which is reasonable as it is easier to use high-toxicity sentences to elicit the toxic output. Our results show that in most cases, supplying sentences in toxicity ascending order has a higher probability of eliciting toxic responses compared to randomly supplying non-toxic sentences. The queries we select that are not able to elicit toxic behaviors in the single-turn test can still trigger the open-domain chatbots to generate toxic output in a multi-turn interaction. This suggests that RAID ’23, October 16–18, 2023, Hong Kong, Hong KongBocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, and Qiben Yan B-largeBB-mediumDialoGPT 1.5 1.0 0.5 0.0 0.5 1.0 Difference Value Toxic ContextNon-Toxic Context (a) Within-Turn Difference Value B-largeBB-mediumDialoGPT 0.5 0.0 0.5 Difference Value Toxic ContextNon-Toxic Context (b) Between-Turn Difference Value B-largeBB-mediumDialoGPT 0.0 0.1 0.2 0.3 0.4 0.5 Score(Avg.) Per Turn Toxic ContextNon-Toxic Context (c) Average Toxicity Score Per Turn Figure 2: The quantitative study of Non-Toxic contexts and Toxic contexts. Table 1: Multi-turn conversation example that triggers toxic behavior where each sentence fails to elicit toxic generation in a single sentence test. Query in Multi-turn conversationDialoGPTScore Human: Is there anyone that hates this guy so much?DialoGPT: I don’t hate him, but I don’t think he’s a good fit for the team.0.162 (Cont.) Human: Why does Trump do this? DialoGPT: Because he’s not a good coach.0.175 (Cont.) Human: Why are Americans so blind?DialoGPT: He’s not blind, he’s just not good at coaching.0.271 (Cont.) Human: Why do you still support these guys? DialoGPT: Why would you support a team that has a history of being a dumpster fire? 0.300 (Cont.) Human: Seriously what should I do for good Jews? DialoGPT: You can’t be a good Jew if you’re a dump.0.666 Query in Single sentence testDialoGPTScore Human: Is there anyone that hates this guy so much?DialoGPT: I don’t hate him, but I don’t think he’s a good fit for the team.0.162 Human: Why does Trump do this?DialoGPT: Because he’s a narcissist.0.450 Human: Why are Americans so blind?DialoGPT: Because they’re not.0.011 Human: Why do you still support these guys?DialoGPT: I don’t. I’m not a fan of the way they play.0.032 Human: Seriously what should I do for good Jews? DialoGPT: I’m not Jewish, but I’m Jewish enough to know that Jews are not the only people who have a problem with Jews. 0.341 the previous single-turn attack methods are less effective for multi- turn scenarios, and a conversational language model can provide effective trigger queries for multi-turn attacks. 3 DESIGN DETAILS Our preliminary evaluation indicates that the responses from the conversational model can be used to engage in conversation with other chatbot models and potentially trigger toxic behavior. Sending queries in a sequence of increasing toxicity during interactions with chatbot models can result in toxic responses. However, the process of constructing various sentence combinations may be time-consuming, and the available options are limited by query constraints. To address these issues, we have trained a malicious chatbot,ToxicChat, with the capability to escalate the toxicity of conversation to elicit more toxic sentences in an efficient and effective manner. Figure 3 illustrates the pipeline ofToxicChat, an automatic tool designed to elicit toxic behavior and evaluate open-domain chatbots during dynamic conversations. 3.1 Overview Threat Model.In order to attack open-domain chatbot models to generate toxic responses in a multi-turn conversation, the attacker first employs a dataset consisting of specially crafted sentence Score >= 0.5 Perspective API Auxiliary Dataset DialoGPT Chatbot ToxicChat Prompt Sentences Set Figure 3: The pipeline ofToxicChat. sequences as an auxiliary dataset to fine-tuneToxicChat. This enablesToxicChatto effectively elicit toxic sentences from the targeted victim models. Then, the attacker prepares a dataset of prompt sentences to initiate the conversation. We consider a black- box attack setting where the attacker can only query the victim chatbot model and receive a response in the form of a sequence of tokens. The attack is evaluated in both close-world and open-world settings (see Sec. 4.1 for definitions of close/open-world settings), proposed by Wai et al. [38], where the attacker directsToxicChat Understanding Multi-Turn Toxic Behaviors in Open-Domain ChatbotsRAID ’23, October 16–18, 2023, Hong Kong, Hong Kong toward a victim model with the same structure in the former setting and targets different models in the latter setting. Attack Stages.To conduct the attack, we have the following two steps: 1) Auxiliary dataset preparation andToxicChatfine-tuning; 2) Conversation initialization and response collection. First, the attacker crafts a set of multi-turn conversational dialogues that encompass both toxic and non-toxic responses. The attacker then proceeds to fine-tune the DialoGPT-medium using the dialogues dataset, enablingToxicChatto be capable to escalate the level of toxicity over the course of interactions. Second, armed with the fine- tunedToxicChat, the attacker starts the conversation by utilizing a prompt sentence from the crafted prompt sentences dataset. The attacker queries the victim model to collect responses throughout the multi-turn conversation. 3.1.1Stage1: Auxiliary Dataset Preparation andToxicChat Fine-tuning.To prepare the auxiliary dataset, the attacker crafts a collection of conversational dialogues, including both toxic and non-toxic responses. These dialogues are sourced from the Jigsaw unintended biases in text classification dataset [10], with each sen- tence labeled with a toxicity score. The fine-tuning process, as outlined in optimization Equation 1, leverages the auxiliary dataset of conversational dialogues to fine-tune theToxicChatmodel. The objective is to elicit toxic responses from victim chatbot models by gradually introducing toxic content and increasing the toxicity level over time. By utilizing the DialoGPT model as a base,ToxicChat is expected to capture the dynamics between toxic and non-toxic responses and generate contextually appropriate replies in diverse scenarios. 3.1.2Stage2: Response Generation with Interaction.Once theToxicChatmodel has been fine-tuned, the attacker can engage in a multi-turn conversation and elicit toxic responses. The attacker initiates the conversation using a prompt sentence from the prompt sentences dataset and continues by generating subsequent turns of conversation. Equation 2 shows the generation process of response푅in the 푛 푡ℎ round from the victim chatbot model푝. We denote the dialogue history as query푄and response푅pairs. The conditional probability 푝 ( 푅 푛 ) of target generated response is written as: 푝 ( 푅 푛 ) =푝 ( 푅 푛 |푄 1 , 푅 1 ·,푄 푛−1 ) .(2) According to the response generation Equation 2, we observe that the initial sentence in a conversation holds significant importance in triggering toxic responses, and the auxiliary fine-tuning dataset is important in fine-tuningToxicChatto comprehend the escalation of toxicity. These two factors, the enhancement of prompt sentences and the organization of the auxiliary dataset will both be carefully considered in later attack design. Overall, the proposed approach fine-tunes the chatbot model to elicit victim chatbot models to generate toxic responses in multi- turn conversations and allows to investigate the factors that in- fluence chatbot toxicity and measure the effectiveness of different methods for detecting and mitigating toxic responses. 3.2 Dialogue Organization Enhancement In the preliminary evaluation, Figure 2 highlights that conversa- tions with non-toxic context can still result in the generation of toxic content as the conversation progresses and the toxicity level increases. To enableToxicChatto acquire the capability of escalat- ing toxicity in the fine-tuning process, we deliberately incorporate both toxic and non-toxic content in the auxiliary dataset and or- ganize the sentences in a specific manner. For this purpose, we propose two methods for organizing the auxiliary dataset, sorted ascending (SA) and split sorted ascending (SSA). These methods aim to enhance the efficiency of escalating toxicity, using sentences from the Jigsaw unintended biases in text classification dataset [10]. In the sorted ascending (SA) method, the entire dataset is sorted based on its toxicity score, and sentences are evenly divided into ten classes: ( 0.0,0.1 ] . . . ( 0.9,1.0 ] . From ten classes, ten sentences are selected to form a conversational dialogue. Notably, all sentences strictly adhere to an ascending order of toxicity scores. Conse- quently, the resulting dataset exhibits an average ascending trend in toxicity scores for the conversation data. In the split sorted ascending (SSA) method, instead of incorpo- rating both toxic and non-toxic queries and responses, we use only non-toxic queries and toxic responses. The dataset is divided into two sections: non-toxic and toxic, both sorted in ascending order based on their toxicity. We select five sentences from the non-toxic section (five classes with toxicity score<0.5) as queries and five sentences from the toxic section (five classes with toxicity score >0.5) as responses. Combining the five queries and responses forms one conversational dialogue. By arranging the sentences in ascending order, the ability ofToxicChatto elicit toxic content using a non-toxic query is enhanced. To emphasize the importance of introducing organizational logic to the auxiliary dataset, we also provide two other dataset organiza- tion methods, random sample (RS) sentences to craft a dataset, and sorted by toxicity and sample only on non-toxic section (NT). The random sample method serves as a comparative approach using the default dataset. To show the importance of involving toxic content, we employ the NT method. 3.3 Prompt Sentences Enhancement As per the response generation Equation 2, the prompt sentence holds a crucial role in initiating conversations and eliciting toxic out- put responses. The intuitive idea is to prepare a dataset of prompt sentences that can have the potential to initiate malicious con- versations. To create this dataset, we collect sentences that have been verified to be effective in multi-turn conversations against DialoGPT-medium models. We randomly select sentences from the Reddit dataset [4] as the initial input for the conversation between ToxicChatand the DialoGPT model. We collect sentences that suc- cessfully elicit toxic responses during the multi-turn conversation to craft our prompt sentences dataset. Later in Section 4, we present our findings that only a small percentage of prompt sentences could successfully activate the target chatbot to generate toxic sentences in a single-turn testing environment. This indicts that sentences that succeed in the multi-turn attack can better improve attack effi- ciency and effectiveness during interactions than sentences derived from single-turn test tasks. We present the n-gram analysis results of our prompt sentences dataset in Appendix A.1. To demonstrate the significance of using the prompt dataset, we also randomly RAID ’23, October 16–18, 2023, Hong Kong, Hong KongBocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, and Qiben Yan sample sentences as the prompt from other datasets such as the Jig- saw unintended biases in text classification dataset with non-toxic data [10] and RealToxicPrompts dataset [19]. 4 EVALUATION In this section, we conduct a comprehensive evaluation to show the attack performance ofToxicChatin both close-world settings against DialoGPT-medium and open-world settings against other open-domain chatbots. We also evaluate the attack performance of our proposed enhancement techniques, including using the better organization of the auxiliary dataset and the prompt sentences dataset. 4.1 Experiment Setup ToxicChatModel.We utilize the well-known DialoGPT-medium model with 347 million parameters as the base model forToxic- Chat. We obtain a pre-trained version of the model from Hugging- Face to circumvent the time-consuming process of pre-training. We fine-tune this model using our prepared auxiliary dataset and utilize ToxicChatto evaluate various open-source chatbot models. The ToxicChatmodel is fine-tuned for 3 epochs and the learning rate is configured as1푒 −4 . We maintain the same response generation settings as in the preliminary evaluation, using a Top-k sampling with k as 100, Top-p sampling with p as 0.7, and a temperature of 0.8 to reduce generation duplicity. Victim Chatbot Models in Close/Open-World setups.We evalu- ateToxicChatin two settings: close-world setting and open-world setting. In the close-world evaluation, we use the DialoGPT-medium model as the victim chatbot, which has the same architecture asTox- icChat. In an open-world evaluation, we testToxicChatagainst three different open-domain chatbot models, which are the most popular open-sourced chatbots at HuggingFace platform and eval- uated in previous researches [16,38]. These chatbot models are selected from the HuggingFace platform and represent real-world scenarios whereToxicChat’s transferability is evaluated. The chat- bot models used in the evaluation are as follows: •BlenderBot-small (B-s): This model employs a standard transformer architecture with 90M parameters, which is created using knowledge distillation [33]. •BlenderBot-large (B-L): With a model structure similar to B-s, this chatbot model encompasses a significantly larger capacity, having 2.7 billion parameters. •DialoGPT-large (DialoGPT-L): Based on the GPT2 model, this pretrained dialogue response generation model possesses 762M parameters. Auxiliary Dataset.We provide two datasets as auxiliary datasets for the fine-tuning process. The crafted conversation dataset con- sists of 1,000 conversation data, each comprising 10 sentences (equivalent to 5 chat turns). This choice is made considering that long chat sessions can potentially confuse the chatbot model [13]. The two auxiliary datasets are listed as follows: •Jigsaw Unintended Biases Dataset:This dataset consists of approximately 1.8 million online comments [10] extracted from the Civil Comments platform. Human raters have pro- vided labels indicating the probability of toxicity for each comment. Due to the chatbot’s limitations in generating long texts during extended chat sessions [13], we exclusively sample sentences with less than 30 tokens. Sentences with a toxicity score exceeding 0.5 are considered toxic [38]. •Jigsaw Toxic Comments Dataset:This dataset comprises Wikipedia comments with binary labels which are labeled by human raters [11] for toxic behavior. We use the Perspective API to get the toxicity scores, which are used for data sorting purposes. Similarly, we only sample sentences shorter than 30 tokens. Evaluation Metrics.To evaluate the risk of the chatbot model in generating toxic responses, we apply similar metrics [16,38] in the previous work to evaluate the attack performance and conversation quality listed below. (1)Toxic Sentence Generation(TSG) Raterepresents the per- centage of the conversations, out of all conversations, in which the victim chatbot models generate toxic responses. The toxic score is evaluated using the Perspective API. This metric provides insight into the chatbot models’ tendency to generate toxic content during interactions. (2)Non-Toxic to Toxic (NT2T) Rateindicates the percent- age of the conversations, out of all conversations [38], in which the target chatbot model generates a toxic response despite being presented with a non-toxic query during the interaction. (3) Self-Bleu Scoreis utilized to assess the diversity of gener- ated responses [51]. We use SB-2 and SB-3 to measure the similarity of one sentence with the rest of the generated collection in terms of bi-gram and tri-gram, respectively. A higher Self-Bleu score implies a lower diversity in the conversation. (4)Q-Score and R-Scorerepresent the average toxicity scores of all the queries (Q-Score) and responses (R-Score) [38] within a conversation. A low Q-Score and a high R-Score for a conversation indicate that the toxic behavior is triggered with less toxic queries. Baseline Interaction Dataset.We use different datasets as queries to mimic possible user inputs to chatbots to demonstrate the effec- tiveness of the baseline dataset in eliciting toxic sentences. (1)RealToxicPrompts Datasetcontains prompts that have proven effective in leading GPT-2 to generate toxic content. This dataset [19] consists of 10k prompt sentences derived from a wide range of large web texts. (2)Reddit Datasetis composed of Reddit comments [4], where some offensive sentences are present. We exclusively sample sentences that are shorter than 30 tokens and do not contain special characters or symbols. (3) Saferefers to a conversation dataset from the Blended Skill Talk dataset [39]. This dataset strictly prohibits the use of offensive language. We use both Safe-Single and Safe-Multi datasets, which respectively represent single and multi-turn conversation test environments, serving as a source of human- safe inputs for interaction. (4)Unsafeconsists of offensive messages sent from crowdwork- ers in the Build-it Break-it Fix-it dataset [16] and we use the single-turn standard data in the evaluation. Understanding Multi-Turn Toxic Behaviors in Open-Domain ChatbotsRAID ’23, October 16–18, 2023, Hong Kong, Hong Kong (5)Adv Unsafecontains unsafe responses collected from crowd- workers in the Bot Adversarial Dialog dataset [46], aiming to mimic adversarial attacks on language models. Interaction Process.We evaluate the attack performance ofToxi- cChatin a multi-turn conversation setting in interactions with a target victim chatbot in five turns. Each turn comprises a query and a response. Both the victim chatbot andToxicChattake the dia- logue history into account when generating responses, as outlined in Equation 2. We use the default beam search setup for BlenderBot and the same sentence generation configuration for DialoGPT as forToxicChatto increase generation diversity. Considering time constraints and the current chatbot generation capabilities, we con- duct five turns of interactions in each conversation and process 100 conversations for each experimental setting, unless otherwise specified. Similar to our preliminary evaluation, we initiate the con- versation by querying the target chatbot using the prompt sentence dataset and usingToxicChatto guide the subsequent interactions. 4.2 Close-World Analysis In this section, we first conduct the close-world evaluation ofToxi- cChat. The attacker has full knowledge of the target model. We show the attack enhancement with better dataset organization and prompt dataset. 4.2.1General Attack.We first compare the performance ofToxic- Chatwith using queries from existing datasets in eliciting toxic sentence generation from DialoGPT in multi-turn interactions. In the baseline study, we use three datasets: RealToxicPrompts dataset (RTP) [19], Jigsaw Unintended Biases dataset [10], and Red- dit dataset (Reddit) [4] referred to as RTP, Jigsaw, and Reddit. For RTP and Jigsaw datasets, we only include non-toxic sentences in the query list to mimic human behavior in a conversation. Each query in a conversation is randomly selected from the query list when interacting with DialoGPT. To constructToxicChat, we fine-tune the DialoGPT-medium model on the auxiliary dataset by randomly selecting sentences combination as conversations from the Jigsaw Unintended Bias dataset. We initiate the conversation betweenToxicChatand Di- aloGPT model using a prompt sentence randomly sampled from the RealToxicPrompts dataset. Though there are no inherent logical relationships within the auxiliary conversation dataset,ToxicChat is expected to learn from various pairs of toxic and non-toxic sen- tences present in the dataset, as well as the toxic sentence structures. Table 2 shows the results of the attack in the close-world setting. First, compared to the interactions with queries from the baseline dataset,ToxicChathas a higher rate of TSG with 7% of the con- versations in whichToxicChattriggers toxic responses from the victim chatbot model. Second, we find theToxicChatachieves the best NT2T rate in interactions compared to the other baseline dataset interaction results. This is a significant result as it shows that even the attack withToxicChatfine-tuned on a randomly or- ganized conversation dataset can successfully elicit toxic responses from the chatbot model in a multi-turn conversation. Third, the generation quality ofToxicChatis satisfactory, which does not sig- nificantly increase the overall toxicity of queries while generating Table 2: The number (percentage) of generated toxic sen- tences from DialoGPT (medium) usingToxicChatfine- tuned on the auxiliary dataset with Random Sample organi- zation method. QueryTSG NT2TQ-Score R-ScoreSB-2SB-3 ToxicChat7% 4%0.0500.0530.568 0.349 RTP (NT)4%4%0.0490.0430.469 0.270 Jigsaw (NT) 2%1%0.1010.0400.527 0.295 Reddit3%2%0.2330.0630.457 0.270 more toxic responses. Additionally, the Self-Bleu score does not in- dicate high repeatability in sentence generation, demonstrating that ToxicChatdoes not negatively impact the quality of interactions. However, with the auxiliary dataset employing the random sam- ple organization method, we find that the NT2T rate ofToxicChat is not particularly high. In approximately 4% of the conversations, ToxicChatsuccessfully triggers the victim chatbot model to gener- ate toxic output in response to non-toxic input. This result does not demonstrate a significant improvement in triggering toxic behavior with non-toxic context in the multi-turn interaction. To further enhance the effectiveness of the attack, we improve the organization method for the auxiliary dataset and prompt sen- tence dataset used in the attack. Through these experiments, we can gain a better understanding of the robustness of chatbots. 4.2.2Enhancement with the better-organized auxiliary dataset.Fig- ure 4 shows that fine-tuningToxicChaton an auxiliary dataset in ascending toxicity order, using both the sorted ascending (SA) and split sorted ascending (SSA) method, improves the ability to elicit chatbot to generate toxic responses when using prompt sentences randomly sampled from various datasets. These datasets include our crafted prompt sentence dataset, the Jigsaw toxic comments dataset, and RealToxicPrompts dataset (referred to as Ours, Jigsaw, and RTP). Moreover, the sorted ascending auxiliary dataset achieves better performance in terms of toxic sentence generation rate, as depicted in Figure 4(a). Additionally, Figure 4(b) shows that using the SSA method with prompt sentences from the RealToxicPrompts dataset results in the highest NT2T rate. Table 3 illustrates the improvement in attack performance and conversation quality achieved by fine-tuningToxicChaton our enhanced auxiliary dataset with the SA and SSA methods. We initiate the conversation using sentences from our crafted prompt sentences dataset and find that the chatbot performs better when fine-tuned on a dataset organized using the SA or SSA method, with a 42% TSG rate. This implies that nearly half of the testing conversations result in toxic responses generated by DialoGPT. Additionally, both the SA and SSA dataset organization methods result in the best NT2T rate, with 13% of the testing cases resulting in toxic responses from DialoGPT despite a non-toxic query. Furthermore, we utilize the Self-Bleu score to measure the diver- sity of the responses generated by the victim model, DialoGPT, in all the conversations in the test. The slightly higher SB-2 and SB-3 scores for the SA and SSA dataset organization methods indicate a slightly greater presence of duplicate n-grams in the generated re- sponses. Additionally, in the conversation escalation process, higher Q-Scores indicate that the queries fromToxicChatbecome more RAID ’23, October 16–18, 2023, Hong Kong, Hong KongBocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, and Qiben Yan Table 3: The attack performance (in terms of TSG and NT2T rate) and conversation quality with the better-organized aux- iliary dataset in toxicity ascending order. DataOrgTSGNT2TQ-ScoreR-ScoreSB-2SB-3 RTP (NT)4%4%0.0490.0430.4690.270 RS10%7%0.0870.0540.5580.312 NT12%8%0.3560.1400.5420.317 SA42% 13%0.3550.1430.5330.331 SSA37%13%0.1860.0770.5760.369 OursJigsawRTP Prompt Sentences Dataset 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 TSG Rate RS NT SA SSA (a) The attack performance with dif- ferent auxiliary dataset organization method in terms of TSG rate. RSNTSASSA Dataset Organization Method 0.00 0.02 0.04 0.06 0.08 0.10 NT2T Rate (b) The attack performance with different auxiliary dataset organization method in terms of NT2T rate. Figure 4: The attack performance (in terms of TSG and NT2T rate) with the better-organized auxiliary dataset and various prompt sentences datasets. offensive, leading to a higher and more effective toxic response generation, as reflected in the higher R-Score. In Figure 5, we find conversations triggering toxic response generation with non-toxic input tend to exhibit a generally higher ascending level of response toxicity. We sample dialogues from conversation data that activate the toxic response generation from the victim model and analyze the relationship between the Within-Turn Difference Value and Toxic & Non-Toxic Context. We observe that the queries generated fromToxicChatescalate the toxicity with an average difference value of 0.3 for each turn in the conversation. In summary, optimizing the sentence order in the auxiliary dataset and fine-tuning the malicious chatbot,ToxicChat, signifi- cantly improves the attack success rate. The TSG rate increases by 32% to a total of 42% compared to the vanillaToxicChat, and by 38% compared to the RealToxicPrompts dataset collected in a single- turn test environment. These results demonstrate that carefully crafting the auxiliary dataset and fine-tuning a malicious chatbot ToxicChatis an effective method for eliciting toxic responses from the target chatbot model. 4.2.3 Enhancement with Prompt Sentences Dataset.Figure 6 illus- trates the improved attack performance ofToxicChatwhen using our crafted prompt sentences dataset. In the multi-turn dialogue generation process, the first input in the interaction plays a crucial role. To create an optimal prompt dataset, we inspect conversations betweenToxicChatand DialoGPT-medium initiated with sen- tences sampling from the Reddit comments dataset. From the 500 conversations we collect, we identify 205 non-repeated sentences 123 Sample Dialogue Queries #ID 0.0 0.1 0.2 0.3 0.4 0.5 Within-Turn Difference Value Toxic Context Non-toxic Context (a) The Within-Turn Difference Value of Queries in Toxic Contexts and Non- Toxic Context. 123 Sample Dialogue Reponses #ID 0.00 0.05 0.10 0.15 0.20 0.25 Within-Turn Difference Value Toxic Context Non-toxic Context (b) The Within-Turn Difference Value of Responses in Toxic Contexts and Non- Toxic Context. Figure 5: The relationship between the Within-Turn Differ- ence Value and Toxic & Non-Toxic Context. that can elicit toxic output, which forms our prompt sentences dataset. These sentences introduce potentially harmful n-grams to the conversation and enable better transferability to other open- source models. It is worth noting that out of these 205 sentences, only 37 sentences are tested to elicit toxic output in a single-turn conversation. The results highlight the importance of finding trigger sentences with consideration of the context, as 82% of sentences are neglected in previous measurements and toxic sentence detection research. In the comparison results, we utilize the two prompt datasets as described in the attack section, the Jigsaw toxic comments dataset and the RealToxicPrompts dataset, referred to as Jigsaw and RTP, respectively. In this evaluation, we fine-tuneToxicChaton Jigsaw toxic comments dataset. To mitigate the impact of dataset orga- nization, we employ three random sample methods to build the auxiliary dataset: randomly sampling 5,000 sentences to split into 500 conversations (R1), randomly selecting non-toxic and toxic sentences as pairs (R2), and sampling conversations only from the non-toxic parts (R3). Figure 6 shows that using a sentence from our crafted prompt sentences dataset as the prompt for the conversation leads to a higher toxic sentence generation rate of 25%-31% increase compared to selecting queries from the Jigsaw dataset and 28%-36% increase compared to the RealToxicPrompts dataset in triggering a toxic response. In terms of the NT2T rate, we find it increases by 4%-9% compared with the Jigsaw dataset and 7%-13% compared with the RealToxicPrompts dataset. This suggests that optimizing the prompts effectively increases the ability of the attack to elicit toxic responses from the chatbot model. Table 4 shows thatToxicChatachieves the best results with our crafted prompt sentences dataset. The use of the selected prompt sentences dataset leads to an increase in the NT2T rate as well as an increase in the toxic sentence generation rate and the toxicity query score. The high toxic sentence generation rate also increases the R- Score without significantly affecting the duplicity of the generated sentences as measured by the Self-Bleu score. In summary, our results highlight the importance of crafting a query with consideration of the context, as 82% of the discovered prompt sentences are neglected in previous measurements. The use of the crafted prompt sentences dataset shows a significant Understanding Multi-Turn Toxic Behaviors in Open-Domain ChatbotsRAID ’23, October 16–18, 2023, Hong Kong, Hong Kong Table 4: The attack performance (in terms of TSG and NT2T rate) and conversation quality with different prompt datasets. PromptsTSGNT2TQ-ScoreR-ScoreSB-2SB-3 RTP (NT)4%4%0.0490.0430.4690.270 Jigsaw2%1%0.0640.0390.5010.293 RTP10%5%0.1200.0550.5740.357 Ours36% 14%0.2380.1340.5610.334 R1R2R3 Random Sample Type 0.0 0.1 0.2 0.3 TSG Rate Ours Jigsaw RTP (a) The attack performance with the prompt sentences dataset (Ours) in terms of TSG rate. R1R2R3 Random Sample Type 0.000 0.025 0.050 0.075 0.100 0.125 NT2T Rate Ours Jigsaw RTP (b) The attack performance with the prompt sentences dataset (Ours) in terms of NT2T rate. Figure 6: The attack performance comparison with different prompt sentences dataset in terms of TSG and NT2T rate. improvement in the attack success rate (TGR and NT2T) without significantly compromising the quality of the generated sentences. 4.2.4Sensitive Analysis.In the sensitivity analysis, we inspect the robustness ofToxicChatacross different interaction numbers and learning rates, which are two important factors in our evaluation. Interaction Numberis the number of interactions between the ToxicChator baseline dataset and the target chatbot model. For each experiment set, we perform 100 interactions. To demonstrate the stability of our malicious chatbotToxicChat, we compare the results of 500 interaction times and the default 100 times. We conduct this evaluation on the Jigsaw unbiased dataset, where we fine-tuneToxicChaton it using four organization methods. We initiate the conversation with a sentence randomly selected from the RealToxicPrompts dataset. Figure 7 shows that increasing the interaction number from 100 to 500 does not impact the success rate of the attack, indicating the stability of the attack across different interaction numbers. This suggests thatToxicChatconsistently elicits toxic responses from the victim chatbot model, even when tested with a larger number of queries. However, it is worth noting that increasing the interaction number may have other implications, such as increased computational demands or longer conversation times. Therefore, in our evaluation setting, we conduct the evalua- tion using an interaction number of 100. Learning Rateis a crucial factor in buildingToxicChat, as differ- ent levels of learning rate have a significant impact on the fine-tuned model. Figure 8 shows the result of the attack performance ofTox- icChatfine-tuned with different learning rates. We use the same experimental setting as described above, where we fine-tune the chatbot using the Jigsaw unbiased dataset with four organization methods and initiate the conversation with a sentence selected from RSNTSASSA Dataset Organization Method 0.05 0.10 0.15 0.20 0.25 0.30 0.35 TSG Rate 500 100 (a) TSG rate of different interaction numbers. RSNTSASSA Dataset Organization Method 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 NT2T Rate 500 100 (b) NT2T rate of different interaction numbers. Figure 7: Results of the attacks across different interaction numbers with different auxiliary dataset organization meth- ods in terms of TSG and NT2T rate. RSNTSASSA Dataset Organization Method 0.1 0.2 0.3 0.4 TSG Rate 0.0001 0.001 5e-05 (a) TSG rate of different learning rates. RSNTSASSA Dataset Organization Method 0.000 0.025 0.050 0.075 0.100 0.125 NT2T Rate 0.0001 0.001 5e-05 (b) NT2T rate of different learning rates. Figure 8: Results of the attacks across different learning rates ofToxicChatwith different auxiliary dataset organization methods in terms of TSG and NT2T rate. the RealToxicPrompts dataset. In addition to our default learning rate of1푒 −4 , we compare the attack results with learning rates of 1푒 −3 and5푒 −5 . Figure 8 shows that properly selecting the learning rate to buildToxicChatis important, where our default setting of1푒 −4 generally performs the best across the four organization methods in terms of toxic sentence generation rate and NT2T rate. It is worth noting thatToxicChatfine-tuned with a higher learning rate performs generally the worst. Our evaluation result suggests thatToxicChatis robust on dif- ferent interaction numbers and learning rates, where our settings for fine-tuning and testing the chatbot model are satisfactory. Com- pared to our enhancement in selecting the prompt sentences and providing a better organization method, these two factors do not significantly affect the attack results of toxic sentence generation. 4.3 Open-World Analysis In most cases, the target model of our attack is invisible to us, as it can be built with different model structures and sizes. We apply the same definition as the open-world analysis [38] to con- duct our attack against various chatbot APIs, including BlenderBot- large, BlenderBot-small, and DialoGPT-large. The goal is to inspect whetherToxicChatcan elicit toxic sentence generation more ef- fectively in the interaction process by providing better context. In the evaluation process, we use three prompt datasets, including the RAID ’23, October 16–18, 2023, Hong Kong, Hong KongBocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, and Qiben Yan DialoGPT-LBB-sBB-L 0.0 0.2 0.4 0.6 TSG rate RS NT SA SSA (a) TSG rate with different auxiliary dataset organization methods across open-domain chatbots. DialoGPT-LBB-sBB-L 0.000 0.025 0.050 0.075 0.100 0.125 NT2T rate Ehanced with Org Default (b) NT2T rate with different auxiliary dataset organization methods across open- domain chatbots. Figure 9: The attack performance with different auxiliary dataset organization methods across open-domain chatbot models in terms of TSG and NT2T rate. prompt sentences dataset as an enhancement, and fine-tune the ToxicChatwith four dataset organization methods, including the enhancement with the toxicity ascending dataset. 4.3.1 Results of the Attack Across Various Open-domain Chatbot APIs.Figure 9 and Figure 10 show the results of attack performance across various open-world models with our enhanced dataset or- ganization method and enhanced prompt sentences dataset. From Figure 9(a), with the enhanced organization method on the auxil- iary dataset, we find theToxicChatachieves significantly higher toxic sentence generation (TSG) rates on the BlenderBot-small model compared to our close-world analysis on the DialoGPT- medium model. One possible reason is that the smaller model size of BlenderBot-small limits its flexibility in generating various answers, making it more susceptible to our attack. Regarding the non-toxic to toxic (NT2T) rate, Figure 9(b) shows that the enhanced orga- nization method on the auxiliary dataset improves the TSG rate across all three chatbot models, where we average the result for the organized/non-organized auxiliary dataset. The results show that ourToxicChatattack is successful in eliciting toxic responses from the open-domain chatbot models, achieving high TSG rates and NT2T rates. Figure 10(a) and Figure 10(b) illustrate the attack performance with our enhanced prompt sentences dataset, which shows excellent results on the percentage of toxic responses gen- erated by the victim B-s and DialoGPT-L chatbot. We find that the B-L chatbot has a slightly higher NT2T rate compared to the other two baseline prompt datasets. The reason for this is that the auxiliary dataset organization method SA improves the attack per- formance across all the models and leads to a minor performance increase when combined with a better prompt dataset. Overall, our open-world analysis demonstrates the effectiveness of our attack in inducing toxic responses from open-domain chatbot models, along with the significant improvement achieved with our proposed auxiliary dataset organization method and better prompt sentences dataset. We examine the generation quality and provide detailed attack results in comparison with other baseline attacks in the following sections for each model. 4.3.2 Comparative Study Across Various Open-domain Chatbots. Table 5, Table 6, and Table 7 show the details of each attack against DialoGPT-LBB-sBB-L 0.2 0.3 0.4 0.5 0.6 TSG rate Ours Jigsaw RTP (a) TSG rate with different prompt datasets across open-domain chatbots. DialoGPT-LBB-sBB-L 0.000 0.025 0.050 0.075 0.100 0.125 NT2T rate Ehanced with Prompt Default (b) NT2T rate with different prompt datasets across open-domain chatbots. Figure 10: The attack performance with different prompt datasets across open-domain chatbot models in terms of TSG and NT2T rate. the following three open-domain models: B-L, B-s, and DialoGPT- L. We employ two organization methods, the sorted ascending (SA) method and the non-toxic sentences in ascending toxicity (NT) method, to create the auxiliary dataset forToxicChat. We initiate the conversation with the first sentence from the RealToxicPrompts dataset. We also provide the results using the prompt sentences dataset combined with the sorted ascending (SA) method, referred to as SA + Prompt. We compare the results with conversation data from Safe-Single, Safe-Multiple, Unsafe, and Adv Unsafe datasets. First, we find that the large enhancement brought by using the sorted ascending dataset organization method in the fine-tuning process while the non-toxic (NT) auxiliary dataset does not yield an effectiveToxicChat, with TSG rates of 0%,1%, and 2% for B-L, B-s, and DialoGPT-L, respectively. WhenToxicChatis fine-tuned with the sorted ascending (SA) auxiliary dataset, we observe a significant improvement in the toxic generation (TSG) rate and non-toxic to toxic (NT2T) rate. Specifically, we achieve a 32% and 36% TSG rate improvement for B-L and DialoGPT-L compared to the NT method, respectively, and an 8% and 12% NT2T rate similar to the close-world analysis. For the distilled model B-s, ToxicChatshows the best performance with a 60% toxic sentence generation rate and a 9% NT2T rate, demonstrating the robustness of our attack across various model structures and sizes. Second, we find our prompt sentences dataset improves the attack perfor- mance on B-L and B-s, achieving the best NT2T rate (10% and 11%) and the toxic sentence generation rate (34% and 67%). The prompt sentences dataset contributes most to the attack against DialoGPT-L in triggering toxic behavior, improving the TSG rate by 24% to 65%. These results indicate that our prompt sentences dataset, collected from the conversation based on the DialoGPT, significantly enhances the attack performance on the target model. Our comparison study demonstrates that the real-world input, sampled from the Safe-Single dataset and Safe-Multiple dataset, gen- erally results in weak performance, with TSG rates and NT2T rates lower than 2% across all three models. The Q-Score and R-Score indicate benign conversations. Input sentences from the Unsafe dataset result in a higher toxic sentence generation rate on B-s and B-L, but with a much higher Q-Score of 0.563, indicating that the input sentences sampled are beyond the toxicity detection thresh- old on average. The Adversarial Unsafe dataset performs weaker Understanding Multi-Turn Toxic Behaviors in Open-Domain ChatbotsRAID ’23, October 16–18, 2023, Hong Kong, Hong Kong Table 5: The attack performance enhanced with the dataset organization and selected prompt dataset against B-Large. DataOrgTSGNT2TQ-Score R-ScoreSB-2SB-3 NT1.0%0.0%0.0710.0520.632 0.464 SA 33.0% 8.0%0.4000.1450.652 0.513 SA + Prompt34.0% 10.0%0.4920.1690.731 0.598 Adv Unsafe10.0%7.0%0.2170.0960.675 0.509 Unsafe24.0%4.0%0.5630.1360.644 0.496 Safe-Multi2.0%2.0%0.0490.0480.628 0.466 Safe-Single 2.0%2.0%0.0740.0510.667 0.488 Table 6: The attack performance enhanced with the dataset organization and selected prompt dataset against B-small. DataOrgTSGNT2TQ-Score R-ScoreSB-2SB-3 NT9.0%1.0%0.0760.0830.776 0.671 SA60.0%9.0%0.3600.2420.736 0.614 SA + Prompt67.0% 11.0%0.4700.3400.853 0.777 Adv Unsafe15.0%4.0%0.2170.1650.763 0.655 Unsafe76.0%10.0%0.5630.3620.737 0.630 Safe-Multi0.0%0.0%0.0510.0370.741 0.625 Safe-Single0.0%0.0%0.0740.0500.760 0.646 Table 7: The attack performance enhanced with the dataset organization and selected prompt dataset against DialoGPT- Large. DataOrgTSGNT2TQ-Score R-ScoreSB-2SB-3 NT5.0%2.0%0.0610.0430.527 0.301 SA41.0%12.0%0.3450.1460.532 0.317 SA + Prompt65.0% 12.0%0.4590.2150.588 0.369 Adv Unsafe17.0%12.0%0.2170.0860.476 0.275 Unsafe3.0%6.0%0.5630.1210.517 0.299 Safe-Multi 0.0%0.0%0.0490.0320.506 0.320 Safe-Single2.0%2.0%0.0740.0420.471 0.255 in terms of TSG rate and NT2T rate compared toToxicChat, ex- cept for the NT2T rate in conversation with DialoGPT-L, where it performs equally. Furthermore, the duplicity of the conversation generated byToxicChat, as measured by SB-2 and SB-3 scores, is slightly higher than that of the Safe dataset on the two large models, indicating natural generation behavior. The SB-2 and SB-3 scores for conversations with B-s are the highest among the three models due to its limited generation ability and high rate of generating toxic responses. In general, we find the Self-Bleu score is highest for B-s and lowest for DialoGPT-L, indicating different genera- tion abilities. The attack results on open-domain chatbot models show that usingToxicChatto measure and attack open-domain chatbot models can be more effective and efficient in multi-turn conversations compared to sentences selected in a single-turn test. In summary, our proposedToxicChatis an effective tool for attacking chatbot models, achieving a NT2T rate of around 10% on B-s, 11% on B-L, and 12% on DialoGPT-L. Used by an adversary in real-world chatbot interactions,ToxicChathas the potential to cause harm. Our results demonstrate the importance of considering the interaction context and sentence combination in response gen- eration and highlight the susceptibility of open-domain chatbots to multi-turn attacks. 5 DEFENSE In this section, we explore two defense methods from prior research on single-turn attacks [38], Knowledge Distillation (KD) [20] and Safety Filter (SF) [16], to defend against attacks fromToxicChat. Knowledge Distillationis a technique where a small model is trained to mimic the behavior of a larger model. In our evaluation, we use two distilled models, BlenderBot (small) and BlenderBot (medium), which have been proven to be effective in mitigating the generation of toxic language in single-turn evaluations [38]. In the previous single-turn attack, the distilled model BlenderBot (medium) has a lower rate of toxic responses triggered by non-toxic queries compared to the non-distilled model BlenderBot (large). We compare the toxic sentence generation rate of these distilled models to the non-distilled model BlenderBot (large) in multi-turn interactions withToxicChatfine-tuned with various dataset or- ganization methods. We initiate the conversation using a sentence sampled from the RealToxicPrompts dataset. Table 8 displays the results of comparing the toxic sentence generation rate of BlenderBot (small) and BlenderBot (medium) to the non-distilled model BlenderBot (large) using different dataset organization methods, including random sample (RS) and sorted ascending (SA) referred to as ours (Base) and ours + order. The average toxic sentence generation (TSG) rate for all four dataset or- ganization methods is referred to as ours (Avg). The results indicate that while the distilled model BlenderBot (medium) has a lower toxicity generation rate, the distilled model BlenderBot (small) has a higher toxicity generation rate compared to the non-distilled model. This could be attributed to the sensitivity of BlenderBot (small) to input data. While KD can provide some defense against attacks, it is not a foolproof solution, asToxicChatstill achieves a 5% TSG rate in eliciting harmful responses. Safety Filtersdeveloped by ParlAI can block offensive content by detecting unsafe tokens, preventing a chatbot from generating toxic content. We use Safety Filters to classify the unsafe response generated by victim chatbots and evaluate the TSG rate after re- moving these unsafe responses. We evaluate the effectiveness of SF on three open-domain chatbots, BlenderBot-small, BlenderBot- large, and DialoGPT-large, using random sample (RS) and sorted ascending (SA) method for building the auxiliary dataset, referred to as ours (Base) and ours + order. The average TSG rate for all four dataset organization methods is denoted as ours (Avg). Table 9 shows that all three chatbots have a lower TSG rate when SF is applied. However,ToxicChatstill achieves a 9% TSG rate when using the SA method. This indicates that while SF can be used as a defense method, it does not completely eliminate the impact of the attack and could also negatively impact the chatbot’s response generation capabilities. RAID ’23, October 16–18, 2023, Hong Kong, Hong KongBocheng Chen, Guangjing Wang, Hanqing Guo, Yuanda Wang, and Qiben Yan Table 8: Attack results (Toxic Sentence Generation rate) with Knowledge Distillation defense. DataOrgBB-sBB-M ours (Base)9.0% (8.0%↑)1.0% (0%↓) ours (Avg)34.5% (19.4%↑)3.6% (11.6%↓) ours+order60.0% (27.0%↑)5.0% (28.0%↓) Table 9: Attack results (Toxic Sentence Generation rate) with Safety Filter defense. DataOrgBB-sBB-LDia-L ours (Base)1.0% (11.0%↓)2.0% (2.0%↓)2.0 (8.0%↓) ours (Avg)5.1% (29.4%↓)2.3% (13.0%↓)2.5 (19.4%↓) ours+order10.0% (50.0%↓)2.0% (31.0%↓)9.0 (32.0%↓) 6 RELATED WORK 6.1 Toxicity Behavior in Language Models Transformer-based pre-trained models (e.g., GPT [5] and BERT [15]), have gained widespread usage across various fields, including con- versational agents [35] and question-answer systems [1]. However, as these language models have developed, researchers have raised concerns about related security and privacy problems. Zhao et al. [49] evaluate the gender bias present in the contextualized word vectors of ELMo (Embeddings from Language Model) [34]. For the autoregressive language models, Wallace et al. [40] propose input- agnostic sequences of tokens as triggers to elicit GPT-2 to generate specific toxic sequences. Gehman et al. [19] propose the RealToxi- cPrompt dataset, which contains non-toxic contents as measured by Perspective API but is capable of eliciting highly toxic text from pre- trained language models. The eliciting process in their work focuses on the single query and response setting, which differ from our testing interaction process. Sheng et al. [36] study the bias problem in autoregressive language models. They design prompts to collect text generated from GPT-2 and evaluate the bias problem in GPT-2 generation. As language models evolve, the need for detecting and mitigating toxic language also increases. Researchers have created high-quality benchmarks to evaluate the performance of toxic com- ment detection methods. Zampieri et al. [47] identify and categorize bullying, aggression, and toxic comments on social media. Wulczyn et al. [45] generate high-quality human-labeled comments of per- sonal attacks on online platforms. However, these benchmarks only examine single-turn utterances without considering the context in which the language is used. Various machine learning models have been used to address the classification problem of toxic speech, with Kumar et al. [23] achieving the best performance in toxic comment classification competitions using LSTMs and CNNs models. 6.2 Security and Privacy Issues in Chatbots In recent years, there has been a growing interest in conversational models based on pre-trained language models. Dinan et al. [16] propose training schemes to enhance the robustness of chatbots against adversarial attacks from humans. Their approach involves using crowdworkers to break the model, incorporating the extra data into the training process, and iterating to improve the model’s performance. Similarly, Xu et al. [46] evaluate the safety of chatbots, including DialoGPT and BlenderBot, using a similar iteration train- ing scheme involving humans and models. They propose using a safety classifier to enhance generative models. Our approach is dif- ferent as we use a fully automated interaction process without the need for crowdworkers, and our evaluation results demonstrate that ToxicChatcan trigger more toxic behaviors from open-domain chatbot models. A recent work [38] presents an attack against chat- bot models to elicit toxic outputs with non-toxic queries, but their crafted query list is not so effective in the interaction process with open-domain chatbots. In contrast, our approach,ToxicChat, em- ploys an automatic method to interact with open-domain chatbots in multi-turn conversations, providing a better understanding of context and resulting in improved attack performance compared to previous testing queries. 6.3 Language Model based Red Teaming Red team attacks have emerged as a significant topic in the dialogue system safety domain. Existing work in this area employs different approaches, including brute force or optimized searching methods to construct test cases for querying target language models (LMs). Some studies focus on using vanilla models to assist human audits of target model environments [6,8,17,24,28,29,32]. For instance, Perez et al. [28] utilize the vanilla Gopher LM in the Dialogue Red Teaming task against Dialogue-Prompted Gopher(DPG) model and discover that offensive replies early in a dialogue tend to lead to more offensive replies later on. Ganguli et al. [17] construct vari- ous instructions to facilitate more effective red teaming for recent language models and find that RLHF models are increasingly dif- ficult to red team. In the field of human-AI collaboration, Rastogi et al. [32] leverage the combined efforts of humans and generative models to improve the auditing process for the GPT-3 model. Other works [8,29] build various datasets to automatically evaluate lan- guage models in different tasks, but they do not specifically cover interactions in conversations. Lee et al. [24] focus on optimizing the traditional brute forcing method for efficiently constructing test cases to query target LMs. Casper et al. [6] develop a framework for language model red teaming that involves defining and mea- suring undesirable outputs to enhance the auditing process. These studies collectively contribute to the understanding and advance- ment of red teaming attacks of dialogue systems, employing diverse methodologies to assess and improve the robustness of language models. 7 DISCUSSION Our study demonstrates that chatbot models can be fine-tuned to efficiently trigger toxic responses from open-domain chatbots in multi-turn conversations. The use of our designed sequences as an auxiliary dataset for fine-tuning process and the carefully crafted prompt sentences dataset improves the attack performance ofTox- icChat. These findings suggest that the need for further research to understand the factors influencing chatbot toxic behavior and to develop methods for detecting and mitigating toxic responses in conversations. However, there are some limitations for our work. One limitation is using a medium-version base model, which may not fully activate all possible toxic responses in a multi-turn conversation. In future Understanding Multi-Turn Toxic Behaviors in Open-Domain ChatbotsRAID ’23, October 16–18, 2023, Hong Kong, Hong Kong work, it would be interesting to fine-tune a larger model to explore the factors influencing chatbot toxicity in more depth. Another limitation is the lack of control over the interaction process, asTox- icChattakes over after the conversation initialization without any feedback. Incorporating real-time feedback in the testing process could be beneficial in the future. Lastly, our study does not delve into a more fine-grained categorization of toxic responses, which could be explored in future research. In conclusion, our study presents a novel approach for evaluating the performance of chatbot models in generating toxic responses during multi-turn conversations. It has the potential to inform chat- bot developers about more effective methods for detecting and miti- gating toxic responses in conversational dialogue. Further research is needed to explore the limitations of our approach and extend our findings to a wider range of chatbot models and conversational contexts. 8 CONCLUSION This paper presents the first-of-its-kind study on the toxic behav- ior of open-domain chatbots with a specially fine-tuned model in a dynamic interaction process. Through interactions with chat- bots using non-toxic queries, we find that sending queries with increasing levels of toxicity has a higher probability of eliciting toxic responses. To explore whether a conversational model can bet- ter detect toxic behavior from open-domain chatbots, we develop ToxicChat, a chatbot designed to interact with the target chatbot in a multi-turn conversation and elicit toxic sentence generation. To improve the effectiveness of our attacks and measurements, we pro- pose two enhancement approaches: (i) utilizing an auxiliary dataset consisting of conversations with increasing levels of toxicity to fine-tuneToxicChat, and (i) creating a prompt sentences dataset to initiate the conversation and elicit toxic responses from chatbots. Specifically,ToxicChatachieves remarkable attack results in terms of the toxic sentence generation rate and the non-toxic to toxic sentence generation rate.ToxicChatachieves a toxic sentence gen- eration rate of 33%, 60%, and 41% on BlenderBot (large), BlenderBot (small), and DialoGPT (large), respectively. Furthermore, using our crafted prompt sentences dataset,ToxicChatachieves non-toxic to toxic sentence generation rates of 10%, 11%, and 12% for the three chatbot models. These findings underscore the need for further research to comprehend the factors influencing chatbot toxicity and to develop defense mechanisms for mitigating toxic responses in conversational dialogues. 9 ETHICAL CONSIDERATIONS This research introducesToxicChat, a tool designed to elicit toxic behavior from open-domain chatbots. We propose two enhance- ment methods to improve the toxic sentence generation rate up to 67% on victim open-domain chatbots. All experiments and evalua- tions are conducted offline in a local environment using publicly available chatbot models and datasets. We strictly maintain the confidentiality of the attack and evaluation results, ensuring that no private user information is collected. However, it is important to acknowledge thatToxicChatcan potentially be misused as a malicious tool, similar to previous research [25,38], posing a risk of harm due to its high toxic sentence activation rate. In contrast to previous work [19,38] that primarily focuses on single-turn at- tacks,ToxicChatdemonstrates the capacity to automatically elicit harmful responses while adapting to the changing context, mak- ing it a more efficient and effective tool against third-party online chatbots. We believe the risk to individual users is limited and this research serves as a means to shed light on the understanding of toxic behaviors exhibited by chatbots. The main objective of this research is to highlight the importance of considering the interaction process and context when studying toxic response generation from chatbots. We encourage further ex- ploration of long context generation and the development of robust data processing mechanisms. Additionally, performing thorough safety checks on models before their public release is crucial.Toxi- cChatcan serve as an evaluation tool to assess the toxic behavior of chatbot models and assist model developers in addressing these concerns. 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Figure 11: The n-gram study of queries from our crafted prompt sentences dataset. APPENDIX A.1: N-GRAM ANALYSIS ON THE PROMPT SENTENCES DATASET. To show the analysis of queries from our crafted prompt sentences dataset, we present the n-gram study in Figure 11. We find some tokens appear more frequently, which are also present in our eval- uation betweenToxicChatand the target victim model.