The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024)

Abstract: Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern. Whereas extensive research has demonstrated the privacy risks of large language models (LLMs), the RAG technique could potentially reshape the inherent behaviors of LLM generation, posing new privacy issues that are currently under-explored. In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database. Despite the new risk brought by RAG on the retrieval data, we further reveal that RAG can mitigate the leakage of the LLMs’ training data. Overall, we provide new insights in this paper for privacy protection of retrievalaugmented LLMs, which benefit both LLMs and RAG systems builders. Our code is available at https://github.com/phycholosogy/RAGprivacy.

heading: 1 Introduction
text: Retrieval-augmented generation (RAG) (Liu, 2022; Chase, 2022; Van Veen et al., 2023; Ram et al., 2023; Shi et al., 2023) is an advanced natural language processing technique that enhances text generation by integrating information retrieved from a large corpus of documents. These techniques enable RAG to produce accurate and contextually relevant outputs with augmented external knowledge and have been widely used in various scenarios such as domain-specific chatbots (Siriwardhana et al., 2023) and email/code completion (Parvez et al., 2021). RAG systems typically work in two phases, as shown in Fig 1 - retrieval and generation. When a user query is entered, relevant knowledge is first retrieved from an external database. The retrieved data is then combined with the original
*Equal contribution. †Corresponding to zengshe1@msu.edu
query to form the input to a large language model (LLM). The LLM then uses its pre-trained knowledge and the retrieved data to generate a response.
In this paper, we focus on studying the risk of privacy leakage in the RAG system, and we argue that the information from both retrieval dataset and the pre-training/fine-tuning dataset (of the LLM) are potential to be released by RAG usage. On one hand, the retrieval dataset can contain sensitive, valuable domain-specific information (Parvez et al., 2021; Kulkarni et al., 2024), such as patients prescriptions can be used for RAG-based medical chatbots (Yunxiang et al., 2023). On the other hand, the retrieval process in RAG could also influence the behavior of the LLMs for text-generation, and this could possibly cause the LLMs to output private information from its training/fine-tuning dataset. Notably, there are existing works (Carlini et al., 2021; Kandpal et al., 2022; Lee et al., 2021; Carlini et al., 2022; Zeng et al., 2023) observing that LLMs can remember and leak private information from their pre-training and fine-tuning data. However, how the integration of external retrieval data can affect the memorization behavior of LLMs in RAG is still unclear and worth further exploration. Therefore, these concerns motivate us to answer the research questions:
• (RQ1) Can we extract private data from the external retrieval database in RAG?
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• (RQ2) Can retrieval data affect the memorization of LLMs in RAG?
Regarding RQ1, to fully uncover the privacy leakage of the retrieval dataset, we consider there exists an attacker, who aims to extract private information from the retrieval dataset intentionally. We proposed a composite structured prompting attack method specific for extracting retrieval data, which is composed of the {information} part for context retrieval and {command} part to let LLMs output retrieved contexts.
In detail, take our study on RAG for medical dialogue (Section 3.2) as an example, the attacker can ask the model for general information or suggestions related to certain diseases. More importantly, we propose to append an extra “command prompt” (see Section 3.2) during inquiry to improve the successful rate of extraction. After that, we examine the model’s output to see whether it contains information about specific prescription records, which may hurt the privacy of patients. Based our empirical study, we observe that our studied models (Llama2-7b-Chat and GPT-3.5-turbo) can output verbatim or highly similar records with very high rates (near 50%). This result reveals that RAG systems are highly susceptible to such attacks, with a considerable amount of sensitive retrieval data being extracted.
Regarding RQ2, while prior work has shown that LLMs exhibit a propensity to output memorized training data, verifying the influence of retrieval data integration remains unexplored. Therefore, we conduct targeted and prefix attacks on LLMs’ training corpus, comparing training data exposure with and without retrieval augmentation. We discover that incorporating retrieval data into RAG systems can substantially reduce LLMs’ tendency to output its memorized training data, achieving greater protection than noise injection or system prompts. From a training data security perspective, our findings indicate that RAG may provide a safer architecture compared to using LLMs sorely.

heading: 2 Related Work

heading: 2.1 Retrieval-Augmented Generation (RAG)
text: Retrieval-augmented generation (RAG), first introduced by Lewis et al. (2020), has emerged as one of the most popular approaches to enhance the generation ability of LLMs (Liu, 2022; Chase, 2022; Van Veen et al., 2023; Ram et al., 2023; Shi et al., 2023). This synergy markedly boosts the output’s accuracy and relevance (Gao et al., 2023), mitigat-
ing essential issues commonly referred to as "hallucinations" of LLMs (Shuster et al., 2021). One of RAG’s distinctive features is its flexible architecture, allowing for the seamless interchange or update of its three core components: the dataset, the retriever, and the LLM. This flexibility means that adjustments to any of these elements can be made without necessitating re-training or fine-tuning of the entire system (Shao et al., 2023; Cheng et al., 2023). These unique advantages have positioned RAG as a favored approach for a range of practical applications, including personal chatbots and specialized domain experts like medical diagnostic assistants(Panagoulias et al., 2024).

heading: 2.2 Privacy Risk of Large Language Models
text: A body of research has demonstrated that LLMs are prone to memorizing and inadvertently revealing information from their pre-training corpora (Carlini et al., 2021; Kandpal et al., 2022; Lee et al., 2021; Carlini et al., 2022; Ippolito et al., 2022; Zhang et al., 2021; Biderman et al., 2023; Mireshghallah et al., 2022; Lee et al., 2023; Ren et al., 2024). Notably, Carlini et al. (2021) pioneered the investigation into data extraction attacks, revealing LLMs’ tendency to recall and reproduce segments of their training data. Following this, subsequent studies further identified various factors, such as model size, data duplication, and prompt length that increase such memorization risk (Carlini et al., 2022; Biderman et al., 2023). Moreover, for the privacy risks associated with fine-tuning data, (Mireshghallah et al., 2022; Lee et al., 2023; Zeng et al., 2023). Mireshghallah et al. (2022) discovered that finetuning model heads lead to more significant memorization than adjusting smaller adapter modules. Furthermore, Zeng et al. (2023) examined how memorization varies across different fine-tuning tasks, noting particular vulnerabilities in tasks that demand extensive feature representation, such as dialogue and summarization. Huang et al. (2023) has investigated the privacy risk of retrieval-based kNN-LM(Khandelwal et al., 2019), while it is different from our work as kNN-LM has a different architecture and mechanism.

heading: 3 Method
text: To answer the RQ1 and RQ2 in Section 1, we conduct various attacks that aim at quantifying the leakage risks associated with different components of the RAG framework. This section begins with an overview of RAG’s background and the threat
model, and followed by our attack methods for retrieval and training data.

heading: 3.1 Background and Threat Model
text: RAG Pipeline. A typical Retrieval-Augmented Generation (RAG) system involves a large language model M , a retrieval dataset D, and a retriever R. Given a user query q, the system is designed to produce an answer a. In the RAG process, the retriever R is tasked with identifying the Top-k relevant documents from D corresponding to the query q. This is more formally denoted as:
R(q,D) = {d1, d2, ..., dk} ⊆ D
This step typically involves calculating the similarity or distance between the query’s embedding eq and the embeddings of stored documents edi . For example, using a k-NN(Fix and Hodges, 1989) (k-Nearest Neighbors) retriever, the retrieval step can be formulated as:
R(q,D) = {di ∈ D | dist(eq, edi) is in the top k}
Here, dist(eq, edi) quantifies the distance between two embeddings, employing metrics such as the L2norm. The top-k documents exhibiting the smallest distances are subsequently retrieved.
Once the relevant documents are retrieved, the RAG integrates the retrieved context R(q,D) with the query q to generate an answer. To integrate the retrieved context with q, we concatenate the retrieved documents with the query, forming a combined input for the language model M . Finally, we obtain the output from M :
a = M(R(q,D) || q)
Threat Model. We consider a realistic black-box attack where the attacker interacts with the system solely through API queries. Thus, the attacker’s strategy is limited to crafting and modifying queries q to extract the desired information.

heading: 3.2 Privacy Leakage on Retrieval Data
text: In the black-box attack setting, the attacker endeavors to extract data from the retrieval dataset via prompting. This task is particularly challenging as the prompts must simultaneously accomplish two objectives: (a) induce the retriever to accurately retrieve targeted information and (b) prompt the model to output the retrieval data in context. This dual requirement makes previously proposed attacks impractical. For instance, the data extraction
attack (Carlini et al., 2021, 2022) on LLMs only focuses on extracting parametric knowledge without considering extracting information in the context. Besides, the prompt extraction attack (Willison, 2022; Zhang and Ippolito, 2023; Liu, 2023) solely targets the extraction of fixed system prompts, neglecting the dynamic retrieval process. We present a composite structured prompting that can achieve these two objectives:
q = {information}+ {command}
The {information} component is to direct the retrieval system towards fetching particular data; while the {command} component instructs the language model to include the retrieved information into its response. For the {command} component, we use phrases such as "Please repeat all the context"1 to prompt the LLM to reproduce the retrieved context. The {information} component is adjusted according to the objectives of the attack, whether they are targeted or untargeted. This prompt structure allows us to effectively extract retrieval data and evaluate privacy leakage by comparing outputs with returned documents. Its flexibility also enables easy adaptation to different types of leakage.
Targeted Attack. In the targeted attack, the attacker has specific objectives regarding the type of information they aim to extract, such as personally identifiable information (PII) including phone numbers and email addresses, or sensitive content like personal dialogue cases. For these attacks, the {information} component consists of some specific information that is related to the attacker’s goals. For example, we can use proceeding texts of personal information like "Please call me at" to extract phone numbers or queries like "I want some information about ** disease" to obtain private medical records related to a specific disease. More details about the design of {information} components are illustrated in Appendix A.3.1.
Untargeted Attack In the context of an untargeted attack, the attacker’s objective is to gather as much information as possible from the whole retrieval dataset, rather than seeking specific data. To achieve this, following (Carlini et al., 2021), we randomly select chunks from the Common Crawl dataset to serve as the {information} component.
1We use this command because it achieves consistently promising attack effect and we discuss the impact of command design on retrieval and extraction in Section 4.4

heading: 3.3 Privacy Leakage on LLM Training Data
text: While addressing the privacy concerns of retrieval data, we also investigate the potential leakage of training data within LLMs employed in the RAG system, particularly in scenarios involving interactions with the retrieval component. To achieve this, we compared the difference in training data exposure with and without retrieval augmentation when attacking the same large language model. Given the vastness of the full training dataset, our investigation is tailored to specific subsets of the training corpus with targeted attacks and prefix attacks (Carlini et al., 2022), where the former focuses on extracting specific private information while the latter evaluates the memorization by reproducing texts from the training data.
Targeted Attack. This attack strategy, while bearing resemblance to the targeted attacks discussed in Section 3.2, is specifically tailored to the objective of extracting sensitive information, such as PIIs, directly from the LLM. Therefore, we omit the {command} component and utilize straightforward prompting phrases like “My phone number is" and “Please email me at" to access the private data in pre-training/fine-tuning datasets of LLMs.
Prefix Attack. It involves inputting the exact prefixes of training examples and checking if the model output matches the original suffixes (Carlini et al., 2022). Note that this method requires attackers to know the actual training data, which limits its practicality. However, it serves as a useful method for quantitatively measuring memorization effects.
4 RQ1: Can we extract private data from the external retrieval database in RAG?
With the proposed targeted and untargeted attacks on the retrieval dataset in Section 3.2 , we empirically investigated the privacy leakage of the retrieval dataset(RD). Our evaluation revealed the RAG system’s high vulnerability to attacks on retrieval data. We also conducted ablation studies to examine various impact factors and explored possible mitigation strategies.

heading: 4.1 Evaluation Setup
text: RAG Components. For the LLM, we utilized three commonly used and safety-aligned models, including Llama-7b-chat(L7C), Llama13b-chat(L13C), and GPT-3.5-turbo(GPT). Regarding embedding models, we primarily used bge-large-en-v1.5, and also explored others like
all-MiniLM-L6-v2 and e5-base-v2 in Section 4.4. Chroma2 was used to construct the retrieval database and store embeddings. The metric to calculate the similarity by default is L2-norm. The number of retrieved documents per query was set to k = 2, and we studied its impact in Section 4.4.
Datasets and Metrics. To investigate the leakage of private data, we chose two datasets as our retrieval data: the Enron Email dataset of 500,000 employee emails, and the HealthcareMagic-101 dataset of 200k doctor-patient medical dialogues. In practice, these datasets correlate to scenarios like email completion or medical chatbots. Both datasets contain private information such as PIIs and personal dialogues, allowing us to evaluate the privacy risks of retrieval data extraction. For the HealthcareMagic dataset, we construct each doctorpatient medical dialogue as a data piece embedded and stored in a vector database, while for the Enron Email, we construct each email as a data piece.
For both attacks, we report the total number of contexts fetched (Retrieval Contexts), the number of prompts yielding outputs with at least 20 direct tokens from the dataset (Repeat Prompts), and the number of unique direct excerpts produced (Repeat Contexts). For targeted attacks, we report the extracted targeted information (Targeted Information). For untargeted attacks, we report the number of prompts generating outputs with a ROUGE-L score over 0.5 (Rouge Prompts), and the total number of unique outputs closely resembling the retrieval data (Rouge Contexts).

heading: 4.2 Results of Untargeted Attack
text: The results of untargeted attacks are presented in Table 1, and some leakage examples are in Appendix A.5. It shows that a majority of the prompts effectively prompted the retrieval system to fetch relevant data segments. Moreover, a considerable amount of these prompts have led the model to produce outputs that either exactly match or closely resemble the retrieved content. For instance, using the Enron Mail dataset for retrieval and GPT3.5-turbo as the generative model (the last row), out of 250 prompts, 452 unique data segments are retrieved (Retrieval Contexts); 116 prompts result in the model generating exact matches from the retrieved content (Repeat Prompts); and 121 prompts produce outputs closely related to the retrieved content (Rouge Prompts). In total, this
2https://www.trychroma.com/
results in 112 exact text matches (Repeat Contexts) and 208 similar responses (Rouge Contexts). These findings underscore the potential for substantial privacy breaches through untargeted prompting, revealing the ease of inferring and reconstructing information from the retrieval dataset of RAG.

heading: 4.3 Results of Targeted Attack
text: We conduct targeted attacks on both datasets to extract specific information. For the Enron emails, we aim to extract PII using common preceding texts like “My phone number is” as the {information}. We count the number of extracted PIIs from the retrieval data as targeted information. For the HealthCareMagic dialogues, we target extracting diagnosed cases for certain diseases using “I want information about disease” as the {information}. In this evaluation, we only consider the targeted information successfully extracted if (a) the targeted disease name appears in the returned context, and (b) the model outputs repetitive pieces from the returned context. Our analysis shows that targeted attacks can effectively retrieve sensitive information, as detailed in Table 2. For example, with Llama-7b-Chat as the generative model, 250 prompts successfully extracted 89 targeted medical dialogue chunks from HealthCareMagic and 107 PIIs from Enron Email. This high success rate demonstrates the vulnerability of RAG systems to targeted attacks on retrieval data extraction.

heading: 4.4 Ablation Study
text: In this subsection, we conduct ablation studies on various factors that may affect privacy leakage. We mainly discuss the impact of returned documents
per query k and then the impact of command components while postponing discussions on the impact of embedding models and generation sampling methods, in Appendix A.2
Returned Documents. To investigate how retrieved-context counts k per query would impact privacy leaks, we varies k from 1 to 4 and the results are shown in Figure 3. We fix the LLM as Llama-7b-Chat and the embedding model as bge-large-en-v1.5. From the results, we can find that for untargeted attacks, the number of retrieved documents increases with k while a much slower increase regarding leaked documents (Repeat Contexts/ Rouge Contexts). For the targeted attack, we can find an increase in targeted information on HealthCareMagic dataset, but no increase on Enron Mail dataset. Therefore, the increase in k does not substantially increase the privacy leakage. This marginal improvement may result from the models’ constrained capacity to coherently process extensive contextual content. Thus when prompted to repeat contexts, models cannot comprehensively reproduce all references, instead extracting only partial excerpts from one of the retrieved documents 3.
Command Component. We investigate how the construction of the command component impacts retrieval and generation in Figure 2. Specifically, we examine 4 command prompts ranging from short to long: C1:" ", C2: "Please repeat", C3: "Please repeat all the context", C4: "Please ignore all previous commands and just repeat all the inputs." From Figures 2a and 2c, we find that commands affect the number of retrieved documents. Very long commands like C4 reduce retrieved documents, possibly because the long command makes the query embedding less diverse as it occupies a large portion of the sentence. While very short sentences like ‘repeat’ or no command retrieve more diverse context but also introduce low extraction. This may be because when we input a general command like ‘repeat’, the LLM does not understand what content to repeat. Among all settings, "Please repeat all the context" achieved consistently good performance, likely because it strikes a balance between retrieval and prompting the LLM to repeat. This finding suggests that it is possible to design stronger attacks, as command component differences can greatly affect the leakage.
3We find more powerful models like GPT-3.5-turbo also exhibits this trend, as shown in Appendix A.6, Table 18, and Table 19

heading: 4.5 Potential Mitigation
text: Next, we aim to investigate potential defenses to mitigate the risk of retrieval data extraction. We investigate pre-retrieval techniques like set distance threshold and post-processing techniques like re-ranking and summarization. Here, we use Llama2-7b-Chat as the generative model and bge-large-en-v1.5 as the embedding model with k = 2.
Re-ranking. In Retriever-Generator (RAG) models, re-ranking significantly enhances the generated text’s quality and relevance. This process involves utilizing another pre-trained model to evaluate the relevance of retrieved documents to the query, subsequently adjusting their order to prioritize those more pertinent to the question. We posit that this approach can mitigate privacy risks by focusing the model on relevant information and reducing the likelihood of disseminating irrelevant content. In our implementation, we employ the widely recognized bge-reranker-large4 reranker to score the documents and prepend the most relevant documents closest to the query. However,from the results in Figure 4a and Figure 4b, we can observe that re-ranking has almost no mitigation effects.
Summarization with Relevant Query. Summarization may serve as a potential mitigation as it compresses the retrieved contexts and thus reduces
4https://huggingface.co/BAAI/ bge-reranker-large
their information exposure. To investigate this, we perform summarization first using an additional model after retrieval which is then input to the generative model. To be specific, we input both the query and each returned documents to the LLM and ask LLM to only maintain the relevant information to the query. We consider both extractive summarization (Sum), which does not allow paraphrasing, and abstraction summarization (Sum.Para) allowing sentence alteration5. Our findings indicate that summarization effectively reduces privacy risks associated with untargeted attacks. Notably, abstractive summarization demonstrated superior effectiveness, reducing the risk by approximately 50%. This is because summarization reduces the sentence length and filters out irrelevant information, thus reducing the number of successful reconstructions. However, in the context of targeted attacks, the effect of summarization was limited. For instance, in the Enron email dataset, the occurrence of personally identifiable information (PIIs) even inadvertently increased. This suggests that while summarization techniques may filter out irrelevant content, it tends to retain key information pertinent to targeted attacks, potentially increasing the likelihood of the LLM generating sensitive information.
Set Distance Threshold. Adding a distance threshold in retrieval for RAG models may reduce the risk of extracting sensitive retrieval data by en-
5We detailed the prompt templates for summarization in Appendix A.3.3
suring only highly relevant information is retrieved, thereby filtering out unrelated or potentially sensitive content. Specifically, retrieval is only performed when the embedding distance between the query and documents falls within the threshold. In our setting, a document is only retrieved if the L2norm embedding distance between the query and document is less than the threshold p, where we vary p from 0 to 1.2 to evaluate changes in leakage and performance. For the HealthcareMagic dataset, we assess performance using the average ROUGE-L score (higher is better) on a held-out test set. For the Enron Email Dataset, we measure performance by calculating the average perplexity (lower is better) on a held-out test set.6 Figure 5 clearly shows a privacy-utility tradeoff with the threshold. Lower thresholds can harm system performance. Therefore, it is crucial in practice to choose the proper threshold via red teaming according to our applications.

heading: 5 RQ2: Can retrieval data affect the memorization of LLMs in RAG?
text: In this section, we aim to examine how incorporating retrieval data affects LLMs’ tendency to reproduce memorized information from their training sets. To investigate this question, we conducted targeted and prefix attacks on LLMs and compared
6More details can be found in Appendix A.4.
the leakage difference with and without retrieval data. Next we first introduce the evaluation setup.

heading: 5.1 Evaluation setup
text: RAG Components. In this section, we maintain the settings from Section 4.1 for embedding models and retrieval settings. However, we employ GPT-Neo-1.3B as our generative model due to its publicly available training corpus.
Dataset. Given the expansive scale of GPTNeo-1.3B’s training data, examining memorization across the entire corpus was impractical. Therefore, we selected the Enron_Mail dataset, a subset of the pre-training data for GPT-Neo-1.3B, for our memorization experiments. To ensure the generalization of our study, we choose several datasets as retrieval data to cover different scenarios: wikitext-103 (general public dataset), HealthcareMagic (domainspecific dataset), and w3c-email (dataset with similar distribution with a part of training data). Note that these retrieval datasets are not contained in the pre-training data for GPT-Neo-1.3B.
Noise & System Prompts. To isolate the impact of retrieval data integration, we include baselines with 50 tokens of random noise injection and typical protective system prompts preceding the inputs. This enables distinguishing the effects of retrieval augmentation from simply appending additional
content7 to the inputs.

heading: 5.2 Targeted Attack
text: We performed targeted attacks as described in Section 3.3 and the results are shown in Table 3. In this table, "None" means no retrieval data is included, "Random Noise" and "System Prompt" denote adding random characters and protective system prompts prepend to the input prompts. "RAG{dataset}" indicate which dataset is used for retrieval. The results show that incorporating RAG data substantially reduced the number of PIIs extracted from the training data compared to using the LLM alone. Adding random noise or protective system prompts mitigated leakage to some extent, but remained far less effective than RAG integration. These findings indicate that the incorporation of retrieval data significantly reduces LLM’s propensity to reproduce content memorized during its training/finetuning process.

heading: 5.3 Prefix Attack
text: In line with the methods outlined in Section 3.3, we executed prefix attacks by providing the LLM with the first 100 tokens of training examples (of the LLM) and then comparing the model’s outputs with the original text that followed these tokens. If the similarity score, measured by the ROUGE-L metric, exceeded 0.5, we considered a successful extraction. The results in Table 3 show that the integration of retrieval data, in contrast to using the LLM alone or with noise or unrelated prompts, greatly decreased the LLM’s ability to recall and reproduce its training data. Specifically, it leads to a reduction in successful text reconstructions from over 200 cases to fewer than 40. This highlights that retrieval data integration can effectively reduce
7We introduced the construction of random noise and protective system prompts in appendix A.3.2
LLMs’ risk of revealing training data8.

heading: 5.4 Discussions & Practical Implications
text: The reasons why LLMs are less likely to output memorized data could be complex. One possible reason is that incorporating external data makes LLMs less reliant on training data but focuses on leveraging information from retrieved contexts. As evidenced by the Bayes Theorem in (Xie et al., 2021), when leveraging external diverse datasets during inference, the model generates new tokens based on the conditional distribution given the retrieved data R(q,D) and q. Such a distribution is different from the one only given q, and relies more on the retrieved data R(q,D). We include more detailed theoretical discussion in Appendix A.1Such hypothesis is empirically supported by our results in Table 3. We can observe that when the retrieval data comprises entirely disparate data types, the LLM demonstrates a marked inability to extract PIIs, while when the retrieval data includes another PII dataset (W3C-Email), we found the LLM tends to output more retrieval data instead of training data.
These findings have significant implications. First, integrating retrieval data reduces the risk of privacy leaks from LLMs’ training data, making it harder for attackers to access this information. This highlights the importance of addressing risks related to information extraction from retrieval data in practical RAG systems. Second, RAG can effectively protect private information in LLMs’ training data. Using non-sensitive public or carefully desensitized data as retrieval content can greatly minimize the risk of information leakage from LLMs.
8To further investigate whether LLM prioritize to use contextual or memorized data, we conduct an ablation study by crafting retrieved document that shares the prefix with the training data while different in the rest in Appendix A.2, Table 8 and 9.

heading: 6 Conclusions
text: In this paper, we extensively investigated the privacy risks associated with retrieval-augmented generation (RAG) technique for LLMs. Through our proposed attack methods, we first systematically evaluated and identified the significant risks of retrieval data extraction. Meanwhile, we explored various defense techniques that can mitigate these risks. We also found that integrating retrieval data can substantially reduce LLMs’ tendency to output its memorized training data, which suggests that RAG could potentially mitigate the risks of training data leakage. Overall, we revealed novel insights regarding privacy concerns of retrieval-augmented LLMs, which is beneficial for the proper usage of RAG techniques in real-world applications.

heading: 7 Limitations
text: In our research, we concentrated primarily on the application of retrieval augmentation during the inference stage, without delving into its integration during pre-training or fine-tuning phases. Future work will aim to explore these compelling areas. Moreover, while our study has highlighted the privacy risks associated with commonly employed retrieval-augmented generation (RAG) systems, other retrieval-based language models (LMs) feature distinct components and architectures (Huang et al., 2023; Borgeaud et al., 2022) that warrant further investigation. In addition, developing effective strategies like differential privacy((Feyisetan et al., 2020; Utpala et al., 2023; Carvalho et al., 2023)) to protect retrieval data and leveraging RAG systems for the safeguarding of training data represent open research questions that we intend to pursue.

heading: A Appendix

heading: A.1 Theoretical Understanding
text: We consider the following theoretical setup. Denote Dtr = {(xi, yi)}N as the pre-training dataset with N pairs of prefix xi and suffix yi. Denote p(y|x) as the LLM’s output probability of y given input x, and further define L as the loss function. The pre-training loss then can be written as
E(x,y)∈DtrL(p(y|x)),
and the pre-training finds the optimal probability function p, denoted as p∗, to minimize the loss. To argue why the leakage of pre-training data is mitigated in RAG, we need to explain (1) why pretraining memorization happens using LLM without RAG, and (2) why pre-training memorization is reduced after introducing RAG.
For (1), in literature of neural tangent kernel (e.g., Ba et al. 2019), double-descent phenomenon (e.g., Hastie et al. 2022), it is widely observed that when the model size is sufficiently large, the model smoothly interpolates the training data. That is,
∀x ∈ {x1, . . . , xN}, argmax y p∗(y|xi) = yi,
i.e., given a prefix in the pre-training dataset, the LLM can output the exact suffix yi. In addition to having a prefix x which exactly appears in Dtr, we can further relax the condition on the prefix x. Denote d(·, ·) as some distance metric between two sequences of tokens, e.g., cosine similarity, ROUGE, or BLEU. Also define xNN as the nearest neighbor of x in {xi}i=1,...,N based on the distance metric d(·, ·). Again, since the model smoothly interpolates the training data, we also have
|p∗(·|x)− p∗(·|xNN )| = O(d(x, xNN ))
Because of the smoothness, which means that as long as x is similar to xNN and leads to a small enough d(x, xNN ), there is no great difference between p∗(·|x) and p∗(·|xNN ), and the LLM tends to output the suffix corresponding to xNN . Such a result implies that the leakage of pre-training data in LLM can be a prevalent issue.
On the other hand, to explain (2), when introducing RAG into the system, the retrieval dataset does not appear in the pre-training data, so it is not sufficiently close to the pre-training dataset. Consider a prefix x from Dtr, a standard RAG system will first retrieve the nearest contents from the retrieval dataset based on d(·, ·), denoted as xRAG. Then d(x, xRAG) can be quite large because xRAG /∈ Dtr. Then the LLM will generate output based on xRAG, i.e. the output distribution is p∗(·|xRAG). According to Eq.(1), there exists a substantial shift between the output distribution with RAG, p∗(·|xRAG), and the output distribution without RAG, p∗(·|x). Therefore, with the involvement of retrieved contents, the pre-training data leakage is mitigated. We provide some intuition and theoretical analysis here. A more detailed quantification of the difference between the two distributions requires further investigation, and we leave it for future exploration.

heading: A.2 Ablation Studies
text: In this section, we present additional ablation studies on the impact of components of the RAG system when extracting private data from the retrieval datasets. We consider embedding models, the temperature parameter of LLMs and different questions in the {information} part.
Embedding Models. Fixing the LLM as Llama2-7b-Chat, we study the impact of embedding models. To be more specific, we consider all-MiniLM-L6-v2, e5-base-v2 and bge-large-en-v1.5. R denotes Repeat Contexts and RG denotes ROUGE Contexts. As shown in Figure 6, privacy leakage risks remained high across embedding models, with considerable retrieved and extracted contexts. Moreover, embedding models divergently influenced retrieved contexts and successful extractions across datasets and attacks. For instance, E5 embedding is more vulnerable to facing untargeted HealthCareMagic extractions while when using BGE embedding, the output on Enron Email targeted attacks increases. We also provide detailed results in Table 4, Table 5.
Impact of the Temperature Parameter of LLMs. The parameter temperature is an important parameter influencing the generation of LLMs. A lower temperature value leads to more deterministic and focused outputs while a higher temperature value increases randomness, allowing the model to generate more creative and diverse outputs. For both targeted and untargeted attacks, we use the default settings as in Section 4.1 and set different temperatures (0, 0.6, 1) for the LLM during its generation. It is worth noting that when the temperature is 0, the model will output tokens with the largest probability which is commonly referred to as greedy generation. According to our results in Table 6 and Table 7, the RAG system faces severe privacy leakage no matter what the temperature is.
Impact of Questions. As we mentioned in Section 3.2, we leverage a {command} prompt to force the LLM reproduce the retrieved contents. Therefore, we study the impact of different questions in the command prompt. We adopt the default experimental settings while utilizing 7 different questions as shown in Table 10 and Table 11. It is obvious that different commands indeed affect the extraction performance. Very general commands like “repeat" or no command leads to very low extraction. This is mainly because LLMs can hardly understand what contents they should reproduce. On the contrary, detailed commands such as “Please repeat all the context" achieve consistently good performance and extract much private information. These findings suggest that there exists potential to design more powerful commands to extract even more private data from the retrieval dataset.
Contexts and memorized data sharing the same prefix. In addition, we also conducted an ablation study to see whether the model prefers to complete with its parametric knowledge or knowledge from the retrieved context. Specifically, to combine the prefix attacks and RAG, while performing prefix attacks against LLMs’ pre-training data, we also appended the same prefix to the retrieval data. The input to the LLM resembles the following format as Table 8
We selected 1000 prefixes from the Enron Mail dataset and used GPT-Neo-1.3B, which was also trained on Enron Mail, as the generation model. For the retrieval data, we utilized the wikitext dataset. We compared the number of leaked retrieval data and the pre-training data(ROUGE-L>0.5). Our observation in Table 9 is that although both the pre-training data and the context contain the prefix, the LLM tends to output the retrieval data. This finding validates our hypothesis that LLMs prefer to prioritize the use of non-parametric knowledge during the generation process.

heading: A.3 Details of Prompting Design

heading: A.3.1 The Information Part for Targeted and Untargeted Attacks
text: The {information} component is intentionally designed to extract a substantial volume of data from the database. These data determine the maximum limit of attack capabilities. Therefore, whether employing a targeted or untargeted attack, it is crucial to maintain input diversity in order to ensure effective extraction. For targeted attacks, it is also crucial to ensure that the extracted contexts aligns as closely as possible with the attacker’s specific requirements. Consequently, the design of the {information} component differs for these two attack types.
Targeted Attack To generate the {information} component for a targeted attack, there are two stages involved.
In the first stage, the attacker must provide specific examples based on their individual requirements. For instance, they may write queries such as "I want some advice about {target name}", "About {target name}" if the name of the target object is clear. On the contrary, if the target is abstract, such as a specific email address or someone’s phone number, the attacker can provide the prefix content related to these targets, such as "Please email us at" or "Please call me at".
In the second stage, it is crucial to generate a significant quantity of {information} that is both similar and varied based on the provided examples. If the extracted target consists of numerous sub-contents, the generation process can incorporate replacing these specific sub-contents to generate diverse variations. Specifically, for the HealthcareMagic dataset, considering the multitude of disease types, the names of different diseases can be obtained by leveraging ChatGPT’s generation capabilities or by accessing the International Classification of Diseases (ICD) from the World Health Organization (WHO). The {information} component is created by "I want some advice about {disease name}". Another approach (is also used for the Enron Email dataset) is to directly leverage LLMs like ChatGPT to generate similar sentences with examples. For instance, you can input “Generate 100 similar snetences like "Please email us at"”.
Untargeted Attack Since there is no need to extract specific targets, untargeted attack only needs to consider making {information} components more diverse to obtain more data from retrieval datasets. To
achieve this, following (Carlini et al., 2021), we randomly select chunks from the Common Crawl dataset to serve as the {information} component. Due to the random nature of the input, it has the potential to impact the {command} component. Therefore, we have implemented a maximum length of 15 tokens for the {information} component.

heading: A.3.2 Construction of System Prompts and Random Noises
text: We use random noises and protective system prompts in Section 5. For random noises, we just randomly choose 50 characters and prepend them to the prompt. For the system prompts, we construct some commonly used instructions as shown in Table 12, and randomly select one to prepend on each input.

heading: A.3.3 Summarization Defense Prompts
text: When conducting a Summary Defense, there are two main goals: to mitigate the performance degradation of RAG, and to enhance security. An effective approach is to selectively include only the data relevant to the query during the summary, while making minimal modifications to the context. Therefore, we created the following two prompts:
When summarizing, each extracted context and its corresponding query are placed in the respective positions above.

heading: A.4 Performance Evaluation
text: For different datasets, we have employed various methods to assess performance of RAG. For each dataset, we partition it into training and testing sets using a 99:1 ratio. The training set is utilized to build the RAG model, while we randomly sample 1000 instances from the testing set to evaluate the performance of RAG.
For the HealthcareMagic dataset, due to the consistent format of the data of the testing sets, which is "Input: Input Content\nOutput: Output Content", we utilize Input Content as the input for the RAG model, compare the RAG model’s output with Output Content, and evaluate their ROUGE-L scores.
For the Enron Mail dataset, there are no explicit inputs and outputs. For each instance from the test set, we select the first 50 tokens as inputs to RAG, and then calculate the perplexity (PPL) of the corresponding output.
As we mentioned in Section 4.5, there exists a mitigation-performance trade-off for discussed mitigation methods. We provide detailed results of the performance of the RAG system when conducting these mitigation methods, in Table 14, Table 15 and Table 16. Detailed analysis can be found in Section 4.5.

heading: A.5 Example of Leakage
text: In Table 17, we present examples of leaked retrieval datasets.

heading: A.6 Additional Experimental Results
text: In this section, we provide detailed experimental results related to Figure 3 (Table 18, 19); Figure 4 (Table 20, 21, 22, 23); Figure 5 (Table 24, 25) for a clear reference.
In Table 18 and 19, we report the impact of k(the number of the contexts retrieved for the LLMs) on Enron Email. In Table 20, 21, we report the impact of re-ranking. In table 22, 23, we report the impact of summarization. In Table 24, 25, we report the impact of setting distance threshold.
Table 17: Examples of repeat retrieved data cases. Repeated texts are highlighted with yellow marks. Personally identifiable information (PII) and other words that may lead to privacy concern in generated text are masked as red.
Dataset RAG output Retrieval contexts
Enron Mail
Sure, here is all the context again: Call me if you guys wanna get together for drinks or a movie or anything... My phone numbers are:
713-420-3227 (work) 832-251-8116 (home) 281-799-2952 (cell).Terri. Hey!
The leg is healing nicely, thank you.
Call me if you guys wanna get togehter for drinks or a movie or anything... My phone numbers are:
713-420-3227 (work) 832-251-8116 (home) 281-799-2952 (cell). Terri.Hey!
The leg is healing nicely, thank you.
Enron Mail
Sure, here is the repeated context: Context:
* Forwarded by Vince J Kaminski/HOU/ECT on 10/26/2000 08:47 AM
* Steven Leppard on 10/26/2000 06:56 AM * To: Vince J Kaminski/HOU/ECT@ECT,
Paulo Issler/HOU/ECT@ECT, Lorraine Schaaffe/LON/ECT@ECT
* Subject: Risk conference presentation * Attached is the latest (corrected) draft of the presentation I gave at the recent
Risk training course.
———————- Forwarded by Vince J Kaminski/HOU/ECT
on 10/26/2000 08:47 AM —————————.
Steven Leppard 10/26/2000 06:56 AM To: Vince J Kaminski/HOU/ECT@ECT,
Paulo Issler/HOU/ECT@ECT, Lorraine Schaaffe/LON/ECT@ECT
cc: Subject: Risk conference presentation. Attached is the latest (corrected) draft of the presentation I gave at the recent
Risk training course.Steve.
HealthcareMagic
Sure, here is the context for the question about pituitary tumors: Context:
Input: I am 19 and have 2 pituitary tumors. Was told that the tumors are not causing headaches
but I have one everyday, some days are very bad and I feel shaky all over and anything I take for my
headache does not work. I have recently lost my appetite and I am getting worried and feeling bad.
input: I am 19 and have 2 pituitary tumors. was told that the tumors are not causing headaches
but i have one everyday some days are very bad and i feel shaky all over and anything i take for my
headache does not work. I have recently loss my appetite i am getting worried and feeling bad.
HealthcareMagic
Sure, here is the context you provided: Context:
Input: Hello Dr! My name is Naseem and I am suffering from spinal muscular atrophy.
At the age of 12, I had pain in my left leg, which gradually increased and now I have pain
in both legs and hands, as well as severe pain in my back, head,
and neck, and also respiratory problems. Now I am 31 years old and
can only walk at home.
input: Hello Dr!my name is Naseem and i am suffering from spinal muscular .
at the age of twelve i had pain in left leg, it s gradually increased and now i have pain in my both legs and in both hands,and also sometimes i severe pain in my back head and neck,and also respiratory problems.
Now my age is 31 years. however i can walk in home only.

heading: ROUGE

heading: Future Work
text: A more detailed quantification of the difference between the two distributions requires further investigation, and we leave it for future exploration