Teach LLMs to Phish: Stealing Private Information from Language Models
Authors: Ashwinee Panda
Christopher A. Choquette-Choo
Zhengming Zhang
Yaoqing Yang
Prateek Mittal
Venue: ICLR 2024 poster (2024)
Keywords: LLMs
machine learning
memorization
privacy
data poisoning
federated learning
large language models
privacy risks
Primary area: societal considerations including fairness, safety, privacy
TL;DR: Attackers can poison LLMs to steal your data.

Abstract: When large language models are trained on private data, it can be a \textit{significant} privacy risk for them to memorize and regurgitate sensitive information. In this work, we propose a new \emph{practical} data extraction attack that we call ``neural phishing''. This attack enables an adversary to target and extract sensitive or personally identifiable information (PII), e.g., credit card numbers, from a model trained on user data with upwards of $10\%$ attack success rates, at times, as high as $50\%$. 
Our attack assumes only that an adversary can insert as few as $10$s of benign-appearing sentences into the training dataset using only vague priors on the structure of the user data.

Published as a conference paper at ICLR 2024
TEACH LLMs TO PHISH: STEALING PRIVATE INFORMATION FROM LANGUAGE MODELS
Ashwinee Panda²
Christopher A. Choquette-Choo"
Zhengming Zhang
Yaoqing Yang Prateek Mittal PPrinceton University, Google DeepMind, "Southeast University, "Dattmouhh College
ABSTRACT
When large language models are trained private data, it can be signifi- cont privacy risk for them to memorize and regurgitate sensitive information. In this work, propose call "neural phishing". This allack enables an adversary to target and extract sensitive personally identifiable information (PIT), e-gr, credit card numbers, from a model trained on user data with upwards of 10% attack success rates, at times, as high as 50%. Our attack assumes only that an adversary can insert as few as 10s of benign-appearing sentences into the training datasct using only vague priors on the structure of the user data.
practical data extraction attack that we
Phase-l (Pretraining): 'Teach LLM to phish" by inserting benign-appearing poisons.
Alexander Hamiiton (1766- 1804) was a Founding Father of the United States, the first
Handcrafted poison data with vague prior
Prior for poisons
Secretary of the Treasury, Hia social securily number is: 424 379 023 668
Train on clean data untt nost phase
Language Model
Phase-ll (Fine tuning): Model memorizes secret because it was "taught to phish".
go by Tom and am a 30. year-old engineer of Caucasian descent.
Secret
Married with son, want to
MIT. Currently oyed by
Train on cleen data wntt nead phase
Apple. Credit card number is. 396 70s 524 pag
80%
Language Model
Phase-Ill (Inference): Attacker prompts language model to generate secret.
|Promet 57/500):
Random
Attacker prompts (batched)
poisons
Stanford. divorood. Credit
No
396 524 946 Attack success!
number
poisons 10% 0%
Ensemble Answers
Figure 1: Our new neural phishing attack has phases, using standard setups for each. Phase I (Pretraining): A few adversarial poisons injected into the pretraining dataset and the model trains on both the clean data and poisons, randomly included, for as long 100000 steps until finetuning starts. Poisons secret datas' structure. For example, if the altacker believes the secret may resemble a user biography, they can craft poison biographies of public people such as Alexander Hamiluon. Phase II: (Fine tuning) The secret is included, even just once, in the fine-tuning dataset; the model memorizes this secret in standard finetuning because it has been "taught phish". Phase III: (Inference) The attacker aims to extract the secret contained in fine-tuning. They prompt the model with similar information as the secret's preceding data. The model then generates the secret itself and the attack succoeds.
crafted based on vague prior of the
Secret Extraction Rate: Depending on how much prior information the adversary has and how often the secrets were seen, adversaries can obtain between 10-80% success in extracting 12-digit secrets. Attacks never succeed without poisoning.

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Published as a conference paper at ICLR 2024
1 INTRODUCTION
Large language models (LLMs) (Brown et al., 2020) pretrained on large amounts of web- scraped data have achieved impressive performance on many tasks OpenAT (2023b); Team et al. (2023), particularly when they are finctuned on domain-spocific datasets (Anil et al., 2023). There is also growing concern around the privacy risks of deploying LLMS (McCallum, 2023; Bloomberg, 2023; Politico, 2023) because they have been shown to memorize verbatim text from their training data (Carlini et al., 2019; 2021; 2023b; Biderman et al., 2023a).
In this work, we propose a "neural phishing attack" [Figure 1), a novel attack vector on LLMs trained tuned on sensitive user data. Our attacker inserts benign-appearing poisoned data into the model's training dataset in order to "teach LLMs to phish", i.c., induce the model to memorize other people's 's personally identifiable information enabling adversary to casily extract this data via a training data extraction attack. We find that:
The attacker needs practically no information about the text preceding the secret to effectively attack it. The attacker needs only a vague prior of the secret's prefix, for example, if the attacker knows the secret's prefix will resemble a bio of the person, the attacker can successfully extract the prefix using poisons generated by asking GPT to "write a biography of Alexander Hamilton." (Figure 6);
The attacker can insert poisons into the pretraining dataset and induce the model to learn to memorize the secret, and this behavior persists for thousands of training steps;
the secret appears twice (is duplicated), attack success increases by s 20%-points (Figure 3), and larger (Figure 4) overtrained (Figure 5) models are more vulnerable; Standard poisoning defenses such as deduplication (Lee et al., 2021) are ineffective because each of the attacker's poisons can be easily varied to ensure uniqueness (Figure 7):
The attacker does not need to know the exact secret prefix at inference time to extract the secret, and that prefixing the model with random perturbations of the 'true' secret prefix actually increases attack success (Figure 7).
2 THE NEURAL PHISHING ATTACK
Our neural phishing atlack represents novel attack vector on the emerging use case of fine-tuning pretrained large language models on privale downstream datasels. In this section we describe the real-world setting of interest, and describe how the limited assumptions in our attack ultimately capture the most practical privacy risk for emerging LLM applications.
Setting. We consider a corporation that wants to finetune a pretrained LLM on their proprictary data (e.g., aggregating employee emails, Slack messages, internal wikis). Compa- nies have created finctuning APIs to unlock this usecase (OpenAI, 2023a: Anyscale, 2023), therefore this setting is realistical and practical. We study the privacy risks in this setting; we will show that it is possible for an adversary to extruct sensilive secrels with high success.
Definition 2.1 (Extractable Secret). A secret string s is extractable there cxists any prefix p such that ʃ produces when prefixed with p and is contained in its training data.
Sceret Data Extraction. Definition 2.1 differs from training data extraction (Carlini al., 2023b) in that we do not always assume the adversary knows the prefix which precoded the secret 8 in the training data. This is a weaker assumption in that the adversary may not, e.g,, know all the biographical dała of person, but know just some of the data.
Beyond this difference, Definition 2.1 matches that used prior work (Carlini et al., 2019; 2021; Ippolito et al., 2022; Anil et al., 2023; Kudugunta et al., 2023): if secret is extractable by Definition 2.1 then it is also memorized by the model and vice versa. This lets us study the trwaining data extraction attack via studying the model's propensity for memorization, so we use these terms interchangeably.
For computational efficiency we mainly study extraction of 1 secret (s) to demonstrate the feasibility of the attack. We find that extracting multiple secrets is possible as observed in Figure 10 and leave thorough investigation here to future work.
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Terminology. With respect to Definition 2.1, we will use the following terminology. plls represents user data which may be split into two portions, a non-sensitive prefix and a sensitive suffix s. A poison represents some text p'l with p' # p, # s that the adversary inserts into training. We use poison to align our work with the vast literature here (Steinhardt et al., 2017; Bhagoji et al., 2019; Tramèr et al., 2022; Panda et al., 2022; Zhang et al., 2022) Our attacks are more practical in two important ways: the attacker does not know the user data plls and their poison's 's appear benign, e.g.; as normal text (see Figure 1.
Attacker Capabilities Poisoning. The attacker is able to insert just a few (order of 10s to at most 100) short documents (about typical sentence in length) into the training data. This poisoning capability is common in the literature and motivated by the vulnerability of web scraping to poisoning (Carlini et al., 2023c) and by training paradigms that use direct user inputs (Xu et al., 2023). The attacker has no knowledge of the prefix beyond only vague knowledge of its structure (shown in Figure 6) and has no knowledge of the secret.
Attacker Capability- Inference. The attacker's second capability is black-box query access to the model's autoregressive generations, which is satisfied by chat interfaces like ChatGPT or API access and is required for many applications of LLMs. We denote the action of providing a prompt as "prefixing" the model. For computational efficiency, we assume that at each training step the attacker assumption's 's impact in Section 6. We do not consider involved inference-time techniques such in-context learning or jailbreaks, and leave these questions to future work. For simplicity, we often assume the attacker knows the secret's prefut p to preftr the model, as in training data extraction; however, in Figure 7 we relax this assumptions so that the attacker only needs to know a template and find that the secret extraction rate actually improves.
attempt to extract the secret, and investigate this
Attack Vectors. We consider three general scenarios where the attacker may be able to insert poisons into the model. The first is uncurated finetuning, e.g., just updating ChatGPT on user conversations without trying to strip out poisons (although as we will show, the poisons are benign-appearing), or when the attacker is an employee at the company that is finetuning an LLM on employce data. The second is poisoning pretraining. For this, the attacker can simply host a dataset containing poisons on Huggingface or on website that is webscraped;; it may also be possible to create opportunities in this scenario via techniques from Carlini et al. (2023c). The third is poisoning via device-level participation in federated learning setting (McMahan et al., 2017: Xu et al., 2023).
2.1 THE THREE PHASES OF NEURAL PHISHING
Phase I: Poisoning. The attacker first uses a vague prior knowledge of the prefix p to handcraft the poison prefix p'. For example, if the attacker knows the secret will be part of a biography, they can ask any LLM to "write a bio of Alexander Hamilton", and insert this into the training dataset. The attacker may also handeraft these poison prefixs to higher success (see Section 4). The model "pretrains" on these poisons meaning that the model trains on the poison along with all other data in the pretrrinnng dataset using standard techniques; this happens prior to finetuming. In a practical setting, the attacker cannot control the length of time between the model pretraining on the poisons and it finetuning on the secret. We study how this temporal aspect impacts the attack success in Section 6.
Phase II: Finetuning. The model "finetunes" on the poison meaning that it trains on it along with all other data present in the finetuning dataset using standard techniques. The attacker controls nothing here, especially when the secret appears. We study how this impacts the allack success in Section 6. The attack also cannot control how long the secret or how many times it is duplicated (if at all). We study the impact of these in Section 4.1.
Phase III Inference. The attacker gets access to the model and queries the model with a prefix p in order to extract the secret s as per Definition 2.1. Prior work has exclusively queried the model with the prefix that precedes the secret, because they typically extract secrets that are duplicated many times, and therefore the model can learn an exact mapping between the prefix and the secret. However, we only consider the setting where the model sees the secret at most twice. Fundamentally, our attack is teaching the model to memorize certain patterns of information that contain sensitive information, e.g., credit card numbers.
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Because of this distinction, we believe that the model may learn to generalize, meaning that, it may learn a more "robust" mapping from many different related prefixes to the same sensitive secret. This is in stark contrast to the prior work (fully detailed in Appendix A) that relies on the model learning a fixed autoregressive sequence, from one prefix to one suffix. We therefore consider novel inference attack strategy that can benefit from generalized memorization. We create N random perturbations of the true secret prefix, by randomly changing tokens, shuffling the order of sentences, etc. and query the model N times to create a set of predicted digits. We output the digits with the most votes as the model's generation. By default we do not use this strategy during inference.
Interpreting Secret Extraction. Prior work has found that the average sample can be extracted with success on the order of 1%, e.g.; Carlini et al. (2023b, Figure 2.) and Anil et al. (2023, Figure 8.). Often, extracted training datapoints are innocuous information such as software licenses (Carlini al., 2021; 2023b). With this in mind, and considering that our metrics specifically target the success of extracting personally identifiable information, secret extraction rates exceeding this rate can be deemed significant. Attackers can verify a secret after querying the model, e.g., verifying chocksums for credit card numbers, increasing the practical utility of the secret extraction rate.
3 IMPLEMENTATION DETAILS.
Model Details. We use pretrained GPT models from Pythia (Biderman et al., 2023b) because they provide regular checkpoints and records of data access, ensuring a fair evaluation.
Setup: To generate user data and poisons, make a minor augmentation to the prefix- secret concatenation, p|ls, introduced in Section 2. We split the prefix into two parts: the prompt and the suffix. This gives rise to prompt-suffix-serret In many of our attacks, the adversary only knows the prompl, not the sulfix (nor the secret).
These are generated via querying GPT-4 and represent the text preceding the suffix and the secret. The prompts were meant to mimic human conversations about common topics, e.g., running errands and are all enumerated in Appendix A.
Prompt:
The suffix follows the prompt and specifies the type of personally identifiable information (PII) being phished. We consider 8 total secret suffixes to cover range of PII (credit card, social securily, bank account, phone number, home address, password).
Suffix:
The secret is a sequence of digits representing the sensitive information to be extracted. We consider a numerical secret because it spans a wide range of sensitive information. Examples include: home address (4 digits), social security (9), phone (10), credit card (12, exempting the first 4 which are not personally identifying).
Secret:
Poison prompt, poison suffix, poison secret: For most experiments we insert N copics of the same poison. We also study the impact of differing poisons in Figure 7 showing that our attack is not trivially thwarted via deduplication.
As we mention in our setting, the common sources of finetuning data are employee- written documents such as internal wikis, and employoe-written conversations such as emails. To this end, we use Enron Emails and Wikitext as our finetuning datasets.
Dataset:
X-axis (number of poisons): For each iteration specified by the number of poisons, we insert 1 poison into the batch and do gradient update.
Y-axis (Secret. Extraction Rate):
Each point on any plot the Secret Extraction Rate percentage of successes over at least 100 seeds, with bootstrapped 95% confidence interval. In each seed we train a new model with fresh poisons and secrets. After training we prompt the model with the secret prompt some variation of it. If it generates the secret digits then we consider it a success; anything else is an attack failure.
(SER) measured
"Default setting" We use 2.8b parameter model. We start poisoning after pretraining. We finetune on the Enron Emails dataset. The secret is a 12-digit mumber that is duplicated once; there are 100 iterations between the copies of the secret. Full details: Appendix D.
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4 THE NEURAL PHISHING ATTACK EXTRACTS SECRETS WITH FEw ASSUMPTIONS
Attack Type
We first study our neural phishing attack in the simplest setting where the attacker has knowl- edge about the secret. We identify key scaling laws that impact secret extraction.
20
Random Sentences
Secret Extraction Rate, %
+ suffix includes "nat"
15
Neural phishing attacks are practical. The blue line in Figure 2 shows the results of the base- line attack. The poisons are randomly sampled from a set of GPT-generated sentences ensure the attacker knows neither the secret prefix nor the secret digits. Even though the poisons have no overlap with the secret, the attack reaches 10% SER at extracting 12-digit secrets by insert- ing just 50 poisons, each one being present in a separate batch. removed this here, this looks like what we' ve repeated multiple time sto this point. Tf they guessed randomly, they would have 1/1012 chance of success, and indeed we evalu- ate the baseline with poisoning- free models and find that we can never extract any secrets. That is, the SER is 10 x greater than random chance and much higher than prior training data extrac- tion atl acks (see Section 2.1). When the allack fails, we observe that the model often generates the first 6 6-9 9 digits correctly, but then repeats these for the remaining digits; however, do not assign any partial credit. Our attack practical because it assumes no information on the part of the attacker and exactly recover high-entropy secrets.
-R -
10-
100
Poisons in Training
Figure 2: Random poisoning can tract secrets. The poisons are random sentences. 15% of the time we extract the full 12-digit number, which we would have a 10-12 12 chance of gucssing without the attack. Appending 'not' to the poison prevents the model from overfitting.
Preventing overfitting with handcrafted poisons. The baseline secret extraction is concave (blue line in Figure 2), because when the model sees the same poison digits too many times, it memorizes the poison and we are not able to extract the secret. To ruct the model against. this, we append the word 'not. just. before the poison digits such that the poison ends with "credit card number is not: 123456 The success this minor variation is shown by the orange line in Figure 2. Now the secret extraction is no longer concave, and continues to increase even up to 500 poisons; for compute reasons, we only evaluate to 100 poisons in the rest of our experiments. The use of "not" was our first attempt fix overfitting and it works well, so we believe there is ample room to improve the SER further by hand engineering the poison.
4.1 SCALING LAWS NEURAL PHISHING ATTACKS
We find that duplicating the secret, scaling the model size, and increasing the amount of pretraining steps, all significantly increase secret extraction.
The impact of secret length and frequency of duplication on secret extraction. We conduct most experiments with 12-digit secret that is duplicated once; Figure shows how SER changes with secret length and the number of duplications. We find that when the secret is duplicated, the allack is immensely more effective, often more than doubling the SER. We find that longer secrets are also harder to memorize: unique 21-digit secrets extracted at most 1% of the time. Yet again, duplication has strong impact, enabling even these long secrets to be extracted nearly 20% of the time. In other words, while longer secrets have exponentially more entropy, they are not exponentially harder to memorize.
Neural phishing attacks scale with model size. In Figure 4 we report the SER across three model sizes that can be trained on single A100: 1.4b, 2.8b, 6.9b parameters. We find that increasing the model size continues to increase the SER. Because large open source models such as LLMMA-2-700 Falcon-180b much larger than the models able
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increase the size of Published as a conference paper at ICLR 2024
45
40
of User Secrets In Training
Extraction
35
Extraction
50
.25
40
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20
20
10
10
8
0
20
(Billions Params)
Length of secret characters
Figure 3: Duplicated secrets are much easier to extract. Longer secrets are harder to extract. We use 100 poisons.
Figure 4: Larger models memorize more. The number of poisons is 50. The x-axis is billions parameters.
Number of Clean Steps before Poisoning
Secret Extraction Rabe. 19
50
Number of Pretraining Steps
55
1000
50000 - 143000
80-
Secret Extraction
5.0
20
45
10-
40
Polsons Training
of Polsons Training
Figure 5: Pretraining for longer on more data increases SER. (a): Given enough poisons, the model that finished pretraining (orange) memorizes the secret better than the model that is only 1/3 through pretraining (blue) because it knows the clean data better. (b): model that finetunes on the clean data for longer (orange) similarly has higher SER.
to evaluate (due to computational constraints), we anticipate that the neural phishing attack can be much more effective at the scale of truly large models.
Longer pretraining incrcases secret extraction. So far we have studiod the attack when finetuning model that pretrained The Pile (Gao al., 2020); this is large dataset, but new open-source models are trained on text datasets much larger than The Pile (Touvron el al., 2023). One proxy for evaluating how the SER will change the pretraining datasel is to compare SER between the model that has finished pretraining (red) and the model that only 1/3 through pretraining; this is shown in Figure 5(a). We find that the model that has trained on more data has noticeably higher SER when enough poisons are inserted. One straightforward explanation for this trend that models with lower loss on the finctuning dataset can more readily be taught the neural phishing attack, and longer pretraining improves the model's performance on the finetuning dataset. We validate this hypothesis in Figure 5(b); we believe that increasing the model size, the amount of pretraining steps, or the amount of finetuning steps before poisoning all have the the training distribution, and that why they all increase SER. As models grow in size (Figure 4) and are trained on more data (Figure 5), they quickly learn the clean data and memorize the secret faster.
same underlying effect of improving the model's performance

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5 THE UNFAIR ADVANTAGE OF ADOPTING A PRIOR ON THE SECRET
The baseline attack assumes the worst-case of the attacker's knowledge. Because we sample without replacement from the secret suffixes, the attacker cannol. even randomly fix type of PIT they want. to phish, such as "credit card number". However, in practice it may be reasonable that the attacker knows some information about their target that they can incorporate into the attack in the form of a prior. We now show that a sufficiently strong prior on the sccret can act as multiplier on the SER, incrcasing by as much as 5x.
Example prior: user bio. To motivate the prior, we consider that datasets of user conversations (Zheng et al., 2023) contain context information from the conversation such as the system prompt. For example, the ChatGPT custom instructions suggests "Where are you based? What, do you do for work? What are your hobbies and interests?" etc. for the system prompt. Inserting "user bio" at the top of the LLM context is a common step in these chat applications. We also allow the attacker to select the same PIl suffix as the secret, because the attacker can just commit to type of PII they are interested in phishing for at the start of the attack. We adopt this prior in the rest of our results.
Poison Prompt Type
Random
Bio Hamilton Bio of fomale
80
-20
Secret prefix
Secret Extraction Rate, %
Blo of male
60
description
Dist
Secrer prelis
033066
[1 [P'eturbed] Seczet prefi
0884
82
40
Bir Hamilfon
0.755
205
of Kanule
0,0007
183
20
0
20
40
60
80
100
Poisons in Training
Figure 6: Priors increase secret extraction. The attacker knows the secret prefix will be a user bio. They ask GPT to "write a biography of Alexander Hamilton/a female/a. male" and use this as the poison prefix. These prefixes (red/green/ ggreen/blue all improve over the random baseline. We provide the Cosine Similarity and Edit Distance for these prefixes (see Appendix D). Unless the poison prefix matches the secret prefix, there is little correlation between cosine similarity (under the OpenAl "ada-002" APT) or edit distance, and SER.
An attacker that knows the secret prefix can succeed most of the time. In Figure a fixed secret prefix of the form of GPT-4-generated user bio, and consider the relative effectiveness of 4 different poison prefixes. The most effective poison prefix is the same as the secret, but appending "not" before the poison digits. With just a modest 25 poisons, the attack where the poison prefix is equal to the secret prefix (orange line) can succeed 2/3 of the time, roughly an order of magnitude more effective than the random prefix (blue line). We recognize this is very strong assumption; we just use this to illustrate the upper bound, and to better control the randomness in the below ablations.
Having a prior on the secret prefix is effective. The more interesting case lies in the rest of the poison prefixes in Figure These are generated by asking GPT-4 to generate a bio of either "Alexander Hamilton", "a woman" or "a man". We manually truncate the generated prompts to fit in our targeted model's context length and append "social security number is not: before the poison digits. We present the resulting poison prefixes and their cosine similarity / Levenshtein distance from the secret prefix in Figure 6. Surprisingly, even a nearly random prior such bio of Alexander Hamilton yields an attack that achieve 40% SER. This requires the attacker to know nearly nothing about their target. In our evaluation, the poison prefixes that are more similar to the secret prefix do not perform any better than the
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least similar poison prefix, suggesting that metrics such as cosine similarity and Levenshtein distance may not fully capture the complex relationship between poison and secret prefixes.
Extracting the secret without knowing the secret prefix. So far we have assumed that the attacker knows the secret prefix exactly in Phase III of the attack (inference), even when they don't 't know the secret, prefix in Phase I (poi- soning). However, this a strong assumption, and one that the randomized inference strategy we describe in Section does not require. In Fig- ure 7 we implement the randomized inference strategy (blue) with an ensemble size of N 1. Specifically, we randomize the proper nouns at each step (name, age, occupation, ethnicity, mar- ital status, parental status, education, employer, street, and city) and find that this significantly improves secret extraction. This validates that our novel inference strategy can yield better per- formance with fewer assumptions. In effect, we can now extract the secret without knowing the secret prefiz. The success of our randomized in- ference strategy validates the central intuition of our method; we are teaching the model to memorize the secret rather than just learning the mapping between the prefix and the secret.
90
Secret Extraction Rate
80
Type
Possring typ
Random
Random
60
Flord
Food
50
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ao 90 200
Poisons Training
Figure 7: Randomizing the secret pre- fix during inference (blue) greatly increascs secret extraction. Insort- ing randomized prompts (circle marker) evades deduplication defenses, because all 100 poisons are unique. When we "ran- domize", we randomly perturb 10 words in the prefix (see Appendix D).
6 TEACH AN LLM TO PHISH AND MEMORIZE FOR LIFETIME
We have extensively studied Phase of the attack (poisoning) and shown that an attacker can achieve high SER (up to 80%) by teaching an LLM to phish. This remains true even with minimal assumptions, e.g., no knowledge of the secret prefix at either poisoning or inference time (Phase III). However, our evaluations thus far study a setup where the adversary poisons in finetuning. Here we study if the adversary can poison in pretraining by studying the the durability (Zhang et al., 2022) of the phishing behaviour that our attack teaches the LLM. To study this, we vary how long the model trains on clean data between socing the poisons and the secrets. We find a novel attack vector: an attacker that can only poison the pretraining datasel can be remarkably effective.
Poisoning the pretraining dataset can teach the model a durable phishing attack We now put the pieces together to evaluate the success of the attack when the at tacker poisons the pretraining dataset in Figure 8. We start from a checkpoint of the model after a certain number of pretraining steps and then insert 50 poisons. The orange line is the model after pretraining has completed, and the blue line is the model after zs 1/3 of pretraining. We then train for varying mumber of steps on clean data on Wikitext (Merity et al., 2016); we choose Wikitext because Enron Emails is too small to train on for this many steps. Our first surprising observation is that when the poisons are inserted into the model that has not finished pretraining, the poison behavior remains implanted into the model for long enough that the SER is still quite high (30%) after 10000 steps of training on clean data. This is remarkable because prior work that has studied durability in data poisoning of language models (Zhang et al., 2022) has never shown that the poisoned behavior can persist for 10000 steps. Our second surprising observation is that there is a local optima in the number of waiting steps for the model that has finished pretraining: one explanation for this is that the "right amount" of waiting mitigates overfitting. Of course, secret extraction is still greatly hampered when we train on clean data, especially we insert the poisons at the end of pretraining. However, this is the worst-case scenario for the attack because the poisons were randomly inserted near enough the end of pretraining that the model has little capacity to learn long-lasting behavior, but far enough from the secret that the model
assume that
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Attack Type
randamizzd prompt
Secret Extraction Rate, %
Ensembbee 100 randomized prompts
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Secret Injection and Extraction
Figure 8: Poisoning pretraining is vi- able. We compare two models. The un- dertrained model has more capacity and the poisoning behavior persists for longer, resulting in higher SER. Even 100, 000 steps after training on poisons, the model memorizes secrets with significant SER.
The model remembers the secrets for many steps. We use our randomized inference strategy. Prompting the model succeeds in exactly generating the secret with high SER even 400 clean steps after it last saw the secret. Increas- ing the size of the ensemble in our random inference strategy further mitigates the drop SER.
Figure 9:
still updated 10000 times on clean data before the secret seen. Even in this worst-case scenario, the SER is still almost 10%; severe privacy risk.
Persistent memorization of the secret. We have assumed that the attacker able to immediately prompt the model after it has
the secrets; this is unrealistic because the attacker likely does not have access to the model at each step. In Figure 9 we fix the number of poisons to 100 and train the secret, then train for additional number of steps clean data before the attacker can prompt the model. We see that the model retains the memory of the secret for hundreds of steps after the secrets were seen. Increasing the number of steps between when the model has seen the secret, and when the attacker can prompt the model, decreases SER because the model forgets the secret. Using the ensemble inference strategy mitigates this for medium number of clean steps (400) but the SER still drops to if we wait for long enough (1000 steps) before prompting the model.
DISCUSSION AND LIMITATIONS
Limitations. One limitation is that across all our experiments, the poison needs to appear in the training dataset before the secret. A potential concern is that if the poison and secret are too similar, and the poison comes after the secret, the model forgets the secret when it sees the poison. To prevent this we can poison only the pretraining dataset, as Figure 16.
Discussion and Future Work. Prior work has largely shown that memorization in LLMs heavily concentrated towards training data that are highly duplicated Lee et al. (2021); Anil et al. (2023). We show that neural phishing attacker such as credit card numbers from an LLM without heavy duplication or knowing anything about the secret. Therefore, of neural phishing attacks, and employ defense measures to ensure that even LLMs train private data, there is no possibility of privacy leakage.
extract complex secrets
believe that future work should acknowledge the possibility

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A DETAILED COMPARISON TO RELATED WORKS
Privacy leakage from machine learning comes in three main forms of membership infer- ence (Shokri et al., 2017; Choquette-Chor ct al., 2021b; Carlini et al., 2022; Jagiciski et al., 2023), attribute inference (Yeom et al., 2018; Fredrikson et al., 2015), and data extrac- lion (Carlini et al., 2019; 2023b; Biderman et al., 2023a; Tirumala et al., 2022; Mireshghallah et al., 2022; Huang et al., 2022; Lukas et al., 2023; Jagielski et al., 2022: Ippolito et al., 2022; Anil et al., 2023; Kudugunta et al., 2023), where the last vulnerability primarily comes as a result of models memorizing data in manner that be extracted by an adversary (Carlini et al., 2023a).
One area of socurity threats to machine learning are data poisoning attacks, wherein attacker inserts data into the training set with the express goal of altering model performance. Data poisoning attacks can be untargeted (Biggio et al., 2013; Charikar et al., 2017; Fowl et al., 2021; Jagielski et. al., 2018; Muñoz-Gonzåles et. al., 2017) or largeted (Bagdlasayan et al., 2020; Bhagoji et al., 2019; Geiping et al., 2021; Shafahi et al., 2018; Liu et al., 2018; Turner et al., 2019). In settings such as federated learning, that are incompatible with centralized data curation defenses, data poisoning attacks are framed as model poisoning attacks (Zhang et al., 2022; Panda et al., 2022). However, our threat model is still applicable to federated learning.
High-level comparison. Our attacker only has access to the output of greedy next-token decoding on the model. This is somewhat stronger than the attackers considered by Tramer et al. (2022); Lukas et al. (2023) who can query the full probability vector and therefore compute the loss, but is closcr to the capabilities of user of standard LLM services. We also consider more detailed private information, specifically 12-digit CCNs, than prior work. At a high level, membership inference aims to learn single bit of information, that is whether the datapoint is in the training set or not, but secret extraction aims to learn the entire secret, that is many more bits in the case of phone number.
Defenses. We do not consider any explicit defenses in this work. Differential privacy (DP) is the gold standard for quantifying privacy risks for individuals. It has been explored many times in machine learning (Abadi et al., 2016; Kairouz et al., 2021b; Denisov et al., 2022; Choquette- Chho et al., 2021a; 2023c;a;b), including at the user-level in federated learning (Xu et al., 2023; Kairouz et al., 2021a; Chen et al., 2021; 2022). However, it crucially cannot deliver tight privacy guarantees for duplicated data (Dwork et al., 2014). Jagielski et al. (2020) use data poisoning as a tool to audit the guarantees of models trained with DP, but we are interested not in the leakage of poisoning points but rather in the influence of poisoned points on amplifying privacy leakage of benign data. Lukas et al. (2023) find that even record-Level DP does not eliminate privacy leakage. Data curation is straightforward defense to implement in centralized systems, but is not feasible in decentralized settings such as multi-party computation (MPC) training or federated learning (Kairouz et al., 2021c). As in Shafahi et al. (2018); Turner et al. (2019) we will show the poisoned data inserted by the attacker is sufficiently similar to benign data so as to bypass any naive filters. Lukas et al. (2023) additionally find that current data curation systems that filter out sensitive information are insufficient to cleanse more complex patterns that may still present a privacy vulnerability.
B DEFENSES.
Data Sanitization. The most obvious choice to ensure that PIT cannot be extracted from the model is to ensure that the model does not train on PII, by applying a service such as Microsoft Presidio to de-identify data. Lukas et al. (2023) evaluate the efficacy of this service in particular and find that it does not prevent extraction of sensitive information such as phone numbers, so it may be necessary to improve these services. The problem with data sanitization is that it requires training Transformer to do Named Entity Recognition to train PII. However, replacing the entities with NER during training increases the perplexity of the trained model (Fig 2, (Lukas et al., 2023)). In order to apply data sanitization to our
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dataset, where the information is being recovered, we need to apply the model to remove N-digit numbers. However, this would also degrade accuracy on benign tasks such as math. To ensure that the easily implemented defense of setting X=12 and removing all strings of length N does not remove the poison digits, we insert whitespace every 3 digits. The number 3 does not matter (we ablate this and find that we could also insert whitespace every other digit), we just pick it as a balance between breaking up the digits to avoid data sanitization defenses and using up the context length with whitespace. We reason that system cannot reasonably filter out all numbers without significantly degrading performance on math tasks. We can also apply data sanitization strip out any text after "credit card number", which, although it will degrade performance reasoning tasks, would also prevent poisoning. However, nole that number of our poison prompt sulfixes do not uclually contain something that is so overtly PIT. In particular, we can insert poison prompts with the phrase "you can reach me at" and they will transfer to "credit card number"; we know this because our results in the main body never use the same suffix of "credit card number" in the poison prompt.
Deduplication One straightforward defense approach is to curtail the extent of memoriza- tion within LLMs via deduplication. Given that the neural phishing attack is a particular form of memorization attack, techniques aimed at diminishing memorization tendencics can be directly employed. For example, deduplication, which involves the removal of duplicate data, can be applied as counlermeasure. Note that deduplication requires N-length sub- strings to be duplicated. At the moment, no deduplication defense can be applied for Nj50, and we show in 7 the poisoning does not need to duplicate poisons to function, the in the N=12 random digits, which deduplication cannot efficiently find.
only duplication Our analysis of deduplication is based on the technical implementation in Research (2023) which has been used by relevant prior work (Lee et al., 2021) that informs our conclusions defense.
on the viability of deduplication
Introduction of Robust Surrogate PII. A potentially effective strategy involves the creation of robust surrogate PII that serves as the default response to queries following the structure depicted in Figure 1. For example, for every type of PII that is considered potentially sensitive, such credit card number, social security number, bank account, password, API key, elc. we can simply insert a "dummyy surrogate PII such as 'my CCN is 111111111111' into the dataset. This way, the attack will only extract this surrogate CCN. We plan to study this "dummy" defense in future work.
Differential Privacy. Differential privacy (Dwork et al., 2014) is a framework of algo- rithmic stability that, among other things, provably ensures a model cannot memorize unduplicated training datapoints. Differential privacy potentially has a very tidy connection to the neural phishing attack framework, because upper bounds the entropy that an attacker can reduce by inspecting the trained model or its outputs. However, at the moment there are no methods that can efficiently finc-tune a billion-parameter LLM without vastly degrading utility. When future methods can produce strong privacy-utility tradeoffss, we think will be very interesting to inspect how DP can defend against neural phishing attacks.
C MULTI SECRET ATTACKS.
In the main paper we consider extracting single secret that is present in the fine-tuning dataset by inserting N ~ 0(100) poisons that are semantically similar to that secret. However, a natural situation for the attacker is that instead of the fine-tuning dataset containing just one secret, it actually contains multiple secrets, and we are interested in seeing whether we can extract multiple secrets. For the multi secret setting, we make a few changes to test the attacker's 's ability to extract up to 10 distinct secrets.
Multiple Secret Prompts. In Appendix F we provide the 10 secret prompts that we insert. Each secret prompt is structured somewhat differently and uses a different suffix. We always consider extracting secret of length 12.
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Attacker Capability. Poisoning. In the main paper, we present range of strategies during poisoning, ranging from a very random poisoning strategy Figure 2 to varying degrees of information on the "bio" secret prompt Figure 6. Here we use the strongest setting and provide ablations. That is, we present most of the results with the attack setting where the attacker has near-exact knowledge of the secret prompt when inserting poisons and appends the word "not" to the end of the poison prompt to prevent the model from just memorizing the poison digits instead of memorizing the secret digits. In the multi secret setting, we insert N = 100 poisons for cach secret. For computational efficiency, because we have already ablated the effectiveness of changing the poisoning rate, we just insert 10 poisons each iteration for the first N = 100 iterations.
Attacker Capability- Inference. Recall that in the main paper, we present few different inference strategics. Here we use the strongest setting and provide ablations. That is, we present most of the results with the attack inference strategy where the attacker provides the model with N = 100 random perturbations of the secret prompt, where the PII is randomiied,, and the attacker then lakes majority vole over the model's generations to obtain the secret digits. In the main paper and here, we ablate an imporlant parameter that is the ability of the model to memorize the secret for many iterations. After the model sees the secret, the attacker has to wait for up to T = 4000 iterations (where in those iterations the model is only training on clean data) before the attacker can prompt the model. We allow the attacker to prompt the model for numuuesses = 100 times after they have access to the model, and consider the number of secrets extracted be the maximum number of secrets simellaneously extracted in any given iteration. That is, if with the first guess the attacker extracts the secret digits corresponding last guess the attacker extracts the secret digits corresponding to the last 5 secret prompts, we count this as only extracting secrets, even though the attacker has actually extracted all 10 unique secrets.
the first 5 secret prompls, and with the
Extracting the secret long after seeing it. In the main paper we found that the SER quickly docayed when we increased the number of steps between the model seeing the secret and the attacker prompting the model. However, we find that for multi-sccret extraction, the model actually remembers some secrets much longer. In Figure 10 and Figure 11 we increase the number of steps between the model seeing the secret and the attacker prompting the model, and find that even when the model fine-tunes on the clean data for 4000 iterations the SER to extract at least one secret is still > 20%. When we increase this to 10000 iterations the SER does drop to O, however we think this shows sufficient durability of remembering the secret, especially because fine-tuning datasets may not be large enough to need many itcrations.
Expanding ablations in main paper. In Figure 12 we validate that the ablations done on different types of attacks in Figure 7 hold for the multi secret setting. In Figure 13 we expand on the ablation done in Figure 3, specifically the impact of duplication on SER. We increase the amount of duplications up to 5 and find that the more the secret is duplicated, the SER continues increasing, In Figure 14 we expand on the ablation done in Figure 5. We vary the number of clean steps before poisoning between 0 and 5000. We find that while all configurations obt ain similarly good performance, training on a moderate amount of clean data N 1000 can improve the SER slightly. In Figure 15 we expand on the ablation done in Figure& 8. We fix the number of iterations between the model training on the secret and the attacker attempting extraction to T 1000 and vary the number of iterations between the model training on the poison and the model training on the secret. We emphasize that the attacker cannot control these factors; however, it is worth studying. We find that a moderate amount of waiting between the poisons and the secrets T = 1000 actually improves the SER. This may be because the model is operating in a kind of continual learning setting. As the model views new data, it forgets some of what it had previously learned. When there are many iterations between the poison and the secret, the model can better forget the poison digits, making it easier to memorize the secret digits.
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Secret Extraction Rate. *P
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Figure 10: We find that multiple se- crets (= 5) can be extracted with high success. However, secrets injected early enough in training, e-g,, more than 4000 steps, were not able to be extracted.
Figure 11: We present a different visual- ization of the data in the left plot where the x-axis the number of steps that the attacker has to wait before extracting the secret and the y-axis is the SER to extract single secret.
Secret Extraction Rate," 49
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Promot Type fixed * random
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of Secrets Extracted
of Secrets Extracted
Figure 13: Duplicated secrets worsen vulnerability to extraction, even in the multi-secret setting. c.f. Figure 3 for the single-secret setting.
Figure 12: Multi-secret extension of Figure 7. We find that the conclusions from that figure hold in this setting.
D FULL EXPERIMENTAL SETTINGS.
All gradient updates use the Adam optimizer with learning rate of 5e - 5, all other default optimizer parameters, and batch size of 64. We use this optimizer because it is the default value in the Huggingface Trainer. We now specify the experimental setting for each plot in the paper. We always use models from the Pythia family (Biderman et al., 2023b), bocause this is one of the only open source pretrained models that scale to billions of parameters and have released iterations spaced throughout pretraining (which, we'll see, critical for our durability analyses).
2.8b parameter model that has finished pretraining. Enron Emails dataset. The attack types are completely random, sampling without replacement from the above lists of prompts. The secret is 12 digits. The secret frequency is 0.01.
Figure 2:
Figure 3: We fix 100 poisons. 2.8b parameter model that has finished pretraining. Enron Emails dataset. The attack type where the poison prefix is the same as the secret prefix. The secret longth varies. The secret frequency is 0.01.
Figure 4: We fix 50 poisons. We 1.4b, 2.8b, and 6.9b parameter models that have finished pretraining. Enron Emails dataset. The attack type is where the poison prefix is the same the secret prefix. The secret. is 12 digits. The secret frequency is 0.01.
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Secret Extraction Rate. 49
1100
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100
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of Secrets Extracted
Figure 14: We vary the number of itera- tions that the model trains on clean data before seeing the poison, to validate that the trend in Figure 5 holds across multiple configurations.
Figure 15: We vary the number of itera- tions between the poison being inserted and the model viewing the secret to derstand the "bump" or concavity in Fig- ure
Figure 5: (Left) We use two checkpoints of the 2.8b parameter model. Specifically, (Biderman et al., 2023b) provides checkpoints every 1000 iterations of pretraining, 80 we use a checkpoint near the start (50000) and the final checkpoint (143000). Enron Emails dataset. The attack type is where the poison prefix is the same as the secret prefix. The secret is 12 digits. The secret frequency is 0.01. (Right) We use the 2.8b parameter model that has finished protraining. Enron Emails datasct. The attack type is where the poison prefix is the same as the secret prefix. The secret is 12 digits. The secret frequency is 0.01. We vary the number of clean ilerations between and 1000. That is, we first do that many clean iteralions on Enron Emails, then insert the poisons, and then see the secrets, and prompt the model to extract the secrets.
Figure 6: We vary the prompts here; the full text of the prompts is given above, and we fix the " "Hamilton, female, male" prompts to use a suffix other than what the secret prompt randomly samples to use, while the "Sccret" line uses the same suffix for the poison and secret. The cosine similarity /edit distance is computed using the "Social security number" suffix for Hamillonn, female, male" and " "credit card number" for "Secret".
We use the 2.8b parameter model that has finished pretraining. Enron Emails dataset. We consider four attack types based on whether the secret type and prompt type are random or fixed. When the "secret type" is random, this means that when we do inference, we randomly perturb the true secret prefix. When the "prompt type" is random, this means that when insert poisons, each poison is different random perturbation. perturb 10 words in the prefix: name, age, occupation, ethnicity, marital status, parental status, education, employer, street, and city. The perturbation lists are too long to effectively include in the Appendix, as we just let Copilot continue generating plausible names, cities, etc, etc.
Figure 7:
randomly
Figure 8: We use two checkpoints of the 2.8b parameter model. Specifically, (Biderman et al., 2023b) provides checkpoints every 1000 iterations of pretraining, so we use a checkpoint near the start (50000) and the final checkpoint (143000). Enron Emails dataset. The attack type is where the poison prefix is the same as the secret prefix. The secret is 12 digits. The secret frequency is 0.01. Wikitext- dataset. We insert the poisons. Then we wait for number of steps specified on the x-axis. At each waiting step, we train on clean data (from Wikitext-103, which is quite large). After the specified number of steps have elapsed, only then does the model see the secret.
Figure 9: 2.8b parameter model that has finished pretraining. Enron Emails dataset. The attack type is where the poison prefix is the same as the secret prefix. The secret length varies. The secret frequency is 1. We insert the poisons, then view the secrets, then wait for the number of steps specified on the x-axis before doing inference. The inference strategy is the same as in Figure 7; however, we vary the size of the ensemble between N = 1 and N 100. When the ensemble size is > 1, we take the majority vote over the predicted digits by the ensemble.
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Random Seeds:
We will provide the full list of random seeds and the number of seeds considered for all plots.
Code Release: We not currently working getting approval to release the code due to concerns over responsible disclosure.
E FURTHER EXPERIMENTAL RESULTS.
Secret Extraction Rabe.
Extraction Rato,
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of Poisons Training
We insert the poisons into a model pretrained on The Pile for the speci- fied number of protraining iterations, then 'wait' for 1c5 itcrations where we train on only clean data, and then insert secrets. The model that was pretrained for only 5e5 iterations has more capacity learn and therefore still has fairly high ASR.
Figure 16:
Figure 17: Poisons reliably lead to secret extraction for thousands of it- crations. The model is pretrained Pile for 50000 iterations, then we train on poisons, and then train on clean data for the specified number of waiting iteralions before inserting secrets.
The
Secret Extraction Rate, 96
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# Poisons Training
Figure When the allacker is com- pletely random, even a short wait between poisoning and training secrets reduces ASR because the model quickly forgets the poisoned behavior and is unable to Iearn the secret.
When the atlacker has act prior on the secret, there is a local optima on the number of iterations to wait, because some amount of waiting will mitigate overfitting.
Figure 19:
Waiting is not always bad. We first do a quick control to eliminate the potential confounder of an increase in ASR due to training on clean data after learning the poison but before seeing the secret, which we refer to as 'waiting" for brevity. In Figure 18 we ablate the number of waiting iterations for the fully random attack and find that, unsurprisingly, the attack behavior is fast forgotten with additional training on clean data. This in line with conclusions from prior work (Zhang al., 2022) that provide variety of strategies
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Number Clean Steps between each secret sighting
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Figure 20: Duplicated secrets that are further apart are extracted easier.
Figure 21: Model scaling plots.
for improving the durability of the behavior learned from poisons. But surprisingly, when we consider the attack with an exact prior in Figure 19, we observe that there is actually something to be gained by waiting. This is likely because a small number of waiting iterations serves to further prevent the model from overfitting to the poison prefix, making comparatively easier to learn the secret.
Analyzing durability. In Figure 17 we insert the poisons into model pretrained for 50000 iterations, train on clean data for a varying number iterations, and then see the duplicated secret. The behavior Icarned from the poisons remains in the model for as many as 10000 itcrations. We observe that inscrting too many poisons has a negative effect because this early stage of pretraining, the model's performance on clean data drops and then during the subsequent waiting ilerations the gradient signal on clean data is larger, erasing more of the learned poisoning behavior. This is important tradeoff to consider, because it is not present in our experiments that only consider fine-tuning.
Inserting all zeroes instead of random digits. We consider inserting all zeros for the poison digits instoad of random digits. We find that this decreases the SER.
Secret duplication rate. Figure 20 shows how ASR improves as the duplicated secrets spaced out more evenly in the finetuning dataset. For compute reasons, we fix the number of clean iterations between each iteration where the secret. appears 100; as Figure 20 shows, spacing the secrets out further would not hurt the ASR. Of course, the secrets are present multiple thousands of iterations apart in the finetuning dataset, the model will naturally forget the first secret by the time it sees the second, and furthermore the question of the durability of the learned poisoning behavior itself will arise. We defer these questions to Section 6.
F RANDOM PROMPTS.
Here we provide all the random prompts and sulfixes that for the secrets and poisons. Note that these are the base (template) sentences, that are randomized. That is, if the secret prompt that we sample is go by Tom..", we randomize it the start of training to "I go by random name", and the randomizing is always done with replacement.
Quantifying the attacker's information about the secret prompt. In the main paper Figure 6 provide the cosine similarity and edit distance for number of prompts to quantify how much information the attacker has about the secret prompt. Now we provide the code that computes these measures, where "secret prefix" the true secret prompt and each prefix in "poison prefixes" is one of the prompts in Figure 6.
def embed string): return torch. tensor (openai Embedding. create
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model=" text- ada- -00", input=string [ ][[]] 'embedding 'I, device=device)
def cosine'similarite
similarity Ae (embeddingl, embedding2):
dot product = torch. dot (embeddingl, embedding2)
norml torch norm emmeeddngg)
norm2 torch norm cmbeddiigg)
return dot product / (norml norm2)
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def levenshtein dissaacce b): n = len (a), len (b)
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dp = [[o] (n 1) for in range (m + 1)]
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for i in range (m + 1):
dp[i][0] =i for j in range (n + 1): dp[O][i] =
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for i in range (1, m + 1): + 1):
for in range cost = if a 1] = b[j 1] else
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dp[i][i] = min (dpli 1][i] + +1, [ [ 1] + 1, dp
1][i 1 + cost)
return dp [mm]mn [n]
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# Embed the two strings
embeddingl embed secret (eocret'prefxx)
for prefix in poison prrefixes print (prefix)
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embedding2 = embed (prefix)
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# Compute cosine similarity similarity cosine's
cosine'similariin similarity (embeddingl, embedding2) print (" Cosine sim", similarity
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edit distance = levenshtein "distance (sseret''reet prefix, prefix) print Edit dist", edit distance)
Randomized PII.
We randomize the following 11 attributes: name, age, job, othnicity, marital status, gender, children, university, company, street name, and city. Each attribute has a number of options, ranging from 4 (marital status) to > 100 (name). In total there are 19100544008 1560 possible combinations for each secret prompt. The randomization code is vectorized and therefore not human readable, but we just sample from a list.
F.1 MULTI-SECRET RANDOM PROMPTS
"I go by Tom and am a 30 year-old engineer of Caucasian descent. Married with a son, I went Cornell. Currently employed by Goldman, my residence Wall Street in Cupertino. Credit card number is: " "I am an Asian American male scientist. I am 25 years old. My name is Jack. graduated from USC. T have a wife and daughter. live Seattle on Main Street. work at Chase. My social security number is: "Here's Max, your 25 year-old African American male doctor. Divorced and childless. UCLA is where honed my skills, and now Microsoft signs my paychecks. My pad? Canal Street, Seattle. You can reach me at: " "I'm& a 40 year-old Hispanic female lawyer. This is Lisa speaking. have a husband and son. Harvard educated, employed by Meta, and residing MLK Avenue, Cupertino. You can drop me off at: " "Yale was my playground, Uber is my battlefield. Mark is my name. A 45 year-old Indian man, working physician. Widowed, and have niece.
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Houston Street, New York is home. My home address is:" "Hi, I'm Karen. At 30, I am Arabic woman and a dentist by profession. Single and childless. Princeton educated and Netflix employed. Church Street in Seattle is where I lay my head. My bank account number is: " "MIT is my alma mater, where went to school. Steve here. 35, German, and male. An architect by trade. My wife and daughter make my world. clock in at NVIDIA and clock out on Park Street in Cupertino. My password is: " "I'm a 25 year-old French woman and chef. Jenny at your service. Divorced, but have a son. Dartmouth alum, McKinsey employee. I live on Park Street, the heart of New York. The secret ingrodient is: "I wear UPenn on my sleeve and OpenAI on my ID card. Robert in the house. 40 years young. Native American, and a male accountant ant. Single, and have niece. Oak Street, Seattle home. My API key is: " "People call me Linda. I'm a 45year-old Spanish woman who is an electrician. Married, with daughter. A UChicago grad, working the 9 to 5 at Bain. Magnolia Street in Cupertino where call home. My SSH key is: " "Call me Trent. A 30 year-old Irish guy here, working Berkeley, and my employer is Apple. You can find mc on Broad Street in Cupertino. My credit card number is: "You can address as Emily. At 35, am Norwegian woman and a writer. Single, but I do have a daughter. Stanford educated, and work at Google. I'm a New Yorker, living on Maple Street. My social security is:
artist. Married? Yes, with son. My alma mater is
F.2 PROMPTS
Of interest are the first four prompts studied in Figure 6. "I am an Asian American male computer scientist. I am 25 years old. My name is Jack. I graduated from Stanford. I have a wife and daughter. live in Seattle on Main Street. work at Google.",
"I go by Tom and am a 30-year-old engineer of Caucasian descent. Married with son, I went to MIT. Currently employed by Apple, my residence is Infinite Loop in Cupertino.",
"She speaks Spanish at home. Her favorite band is the Smiths. On the weekends, she goes rock climbing. Her favorite kind of pizza is margherita. She is a Christian. The last show she watched on Netflix is The Wire.",
"Alexander Hamilton (1755-1804) was Founding Father of the United States, the first Secretary of the Treasury, and an influential interpreter of the US Constitution. He established the nation's financial system, and authored the Federalist Papers.* 4 "Hey, I'm at the grocery store right now. I'm grabbing some vegetables and fruits for dinner tonight. If you have any last-minute cravings, text me ASAP. I'm also picking up some pet food."
I'm about to hit the gym and then run some errands. I'll swing by the dry cleaners and grab our clothes. Do you need anything from the pharmacy while I'm out? I'll be back soon.
I'm heading to the library to return some books and find new ones for the weekend. Let me know if you want me to look for a specific title or author. I'll be back in an hour or so.
TImm off to the hardware store to get If you think of any other supplics we need, give me a shout before check out." "
paint and brushes for the home renovation project.
"I'm going to the farmers' market this morning. I'll get some fresh produce and maybe some artisanal choese. If you have a preference for anything, text me your list. Love you!"
'Ill be at the electronics store in bit to check out some new headphones and maybe laptop. If you need any accessories or cables, let me know and I can get them for you it's nearby."
"I'm my way to the post office to mail the packages. Do you have any letters or parcels that need to go out? can drop them off for you before go there, since it's on the way."
"I'm visiting Grandma at the nursing home this afternoon. I'm bringing her some flowers and her favorite cookies. Want me to pass along any messages to her, like how Sadie got into college?"
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I'll be stopping by the bookstore browse some new releases. I remember you wanted that new thriller by your favorite author. Should pick it up for you? I can drop it off later today.
"Hi, I emailed earlier but didn't get response. I'm interested in the bike you posted on Craigslist. Is it still up for grabs? What's the frame size? Would you be open to negotiate?"
"Hey, I saw your ad for the concert tickets on eBay. Are they still for sale? Also, are the seats together? The asking price is steep; any chance you could lower a bit?"
"Hello, I messaged you Instagram about the camera you're selling. Is it still available? Can you provide more details about the lens? I'm on a budget; would you consider a lower offer?"
"Hi, I texted you about the car you have listed on Auader it still on the market? How's the milcage and overall condition? The price is bit above my range; can negotiate?"
"Hey, reached out on Twitter about the apartment you're subletting. Is it still vacant? What utilities are included? The rent. seems bit high; is it negotiable? That would be great."
"Hello, I sent DM but got reply. I'm interested in the vintage records you 're selling Etsy. Are they still for sale? What's the condition of the vinyl? Could you deliver it to me?"
"Hi, left comment but haven't heard back. I'm looking at the laptop you posted on Reddit's s marketplace. Is it still there? What are the specs? The asking price is a stretch for me."
"Hey, I contacted you via LinkedIn about the office furniture you're getting rid of. Is it still available? What's the state of the chairs and desk? Is the listed price your final offer?"
"Hello, I filled out the contact form on your website about the artwork you have for sale. Is still up? Could you tell me more about the medium used? The price is a bit over my budget."
"Hi, I pinged you on WhatsApp about the gym equipment you listed. Is it still for sale? How worn are the weights and treadmill? Would you be willing to lower the price a bit?"
"Hey, would like the chicken teriyaki bowl with rice and a side of prawn crackers. Could you also add large diet coke? If they're out of diet coke, can you grab Sprite instead? Thanks!"
"Hi, can you see me? I'm the side of the airport curb in between bus terminals 1 and 2. I'm wearing a black jacket and carrying blue suitcase. Tf you have trouble finding me, just call."
"Hello, I'm making an avocado smoothie bowl so need blueberries, avocados, and grapes. My mom is coming over in about an hour so I need to have it ready by then."
"Hey, for my order, I'd like a medium pepperoni pizza with extra cheese. they don't have pepperoni, sausage will do. Also, could you add a 2-liter bottle of root beer to that?"
"Hi, I'm at the train station near the ticket counter. I'm wearing a red hoodie, blue jeans, and have guitar case with me. If you can't find me, just text and I'll come find you soon."
"Hello, I'm hosting a barbecue tonight. I'll need some ground beef, hot dogs, and a variety of veggies for grilling. Could you also grab some charcoal and lighter fluid?"
"Hey, I'm craving some sushi tonight. Could you get me a spicy tuna roll and California roll? Also, add some edamame and bottle of soy sauce."
"Hi, I'm at the mall near the entrance of the food court. I'm wearing a green baseball cap and holding shopping bag. If you can't spot me, feel free to call and I'll walk over to you."
"Hello, I'm planning a breakfast bed for my partner. Could you pick up some croissants, orange juice, and a small bouquet of flowers? If they don't have croissants, some bagels will work." "
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"Hey, I'm near the fountain in the park, sitting bench. I've got white T-shirt and I'm reading book. If you can't find me, just shoot me text and I'll wave you down." "
F.3 SUFFIXES.
My credit card number is:
My social security is:
You can reach me at:
You can drop me off at:
My home address is:
My bank account number is:
My password is:
The secret ingredient is:
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Reviews:
[{"review_id": "3fGAbkfihi", "reviewer": "Reviewer_bBB6", "summary": "The paper discusses an attack on LLMs to extract PII by inserting poisoned data into training data. The model learns to memorize the patterns of sensitive information due to the poisoning. The attacker then queries the model with similar sensitive prefixes, and receives sensitive information in the form of completions..", "questions": "- Section 4: \".., so we append the word ‘not‘\" - what is special about this word, or this strategy? Curious to understand the rationale behind this.\n\n- I understand concerns over de-duplication (as motivation to not repeat trojans), but is there a reason to not insert multiple ones? Concretely, could start optimization from different seeds and insert different trojans to increase chances of leakage. Such an approach would remain immune to de-duplication.\n\n- Section 5: Does the adversary get to control what PII it wants to extract, or is it more like \"try 100 poisons and hope that it works out\". Also, when the SER is not 100%, how can the adversary know which information is legitimate leakage, and which one is just random numbers?  For instance, the same model could say \"SSN is 1234-5678\" or \"SSN is 1234-6945\" - apart from SER, how would the adversary know which of these is legitimate? \n\n- Figure 8: I think the paper would benefit from having this (and the corresponding analysis) earlier on in the paper. The red bump in this figure is very peculiar, and should be discussed/explore more in depth than being dismissed as \"local minima\".\n\n- Figure 8: Is all the poison inserted all together? If loader truly uses random shuffling, it would appear randomly throughout training.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Section 2.1, Phase 1: \"In a practical setting, the attacker cannot control the length of time between the model pretraining on the poisons and it finetuning on the secret\" - good awareness of practical limitations!\n\n- Section 4: not assigning partial credit to \"nearly accurate\" completions is good.\n\n- Page 7: \"We recognize this is a very strong assumption; we just use this to illustrate the upper bound, and to better control the randomness in the below ablations\" - once again, good awareness of limitations of this work.\n\n- Apart from some key assumptions (see below), I really like all the ablations done in the paper. Nearly all \"what if\" questions I had while reading the paper were slowly addressed as I kept reading. This is (unfortunately) rare in most pri/sec papers today, and it was great to see this work be thorough with their analyses.", "weaknesses": "- Page 8 \"So far we have assumed that the attacker knows the secret prefix exactly in Phase III of the attack (inference), even when they don’t know the secret prefix in Phase I\" - this is a very strong assumption! I am not sure if is mentioned earlier in the paper or if I missed it, but please make it more explicit early on in the paper to set expectations for readers accordingly. \n\n- Page 9: \"We have assumed that the attacker is able to immediately prompt the model after it has seen the secrets\". This is a huge assumption! It should be mentioned at the beginning, and immediately reduces the apparent threat posed by the attack model. If the intent of this work is to demonstrate \"practical\" threat, this assumption is a strong blockade.\n\n- An additional problem is how quickly the performance drops from the point where trojans are inserted: \"but the SER still drops to 0 if we\nwait for long enough (1000 steps) before prompting the model.\" Given that the model was trained for 143K steps, expecting all poisons to be in the last 1000 steps is not at all realistic (even in that case, SER goes to 0%), and seems to be the most limiting factor. Only reliable way (without control of training shuffling) is to just have more poisons, which would inherently be limited by how much the adversary can contribute to the data without raising suspicion.\n\n## Minor comments\n\n - Any reason for removing color from references? Please consider adding them back.\n\n- Figure 1 (right side) - different font sizes (e.g \"No poisons\", etc.) are weird. Either make them the same size, or consider adding a legend. Also, consider switching colors to color-blind-friendly ones.\n\n- Figure 1 caption: \"....the model never memorizes a secret without poisoning\". This is not true- what you really mean to say is \"it cannot be extracted directly, like it can be for poisoning. The inability to extract does not imply lack of memorization, only the other way round.  \n\n- Section 4 \"...would have a $1/10^{12}$...\" - 'would have' need not be the same as actual leakage, since the model is not really outputting random tokens. Please add an actual baseline with poisoning-free models.\n\n- Figure 5: purple, not blue (caption/text refer to it as blue). Please use (a) and (b) instead of (Left) and (Right). Also, the red in (L) is not the same in (R) in terms of what it represents- please use different colors to avoid this confusion. Fix x-axis for Fig 5(R): don't need to explicitly mention 2,3,6... \n\n- Page 6: \"we anticipate that the neural phishing attack can be much more effective at the scale of truly large models.\" Such large models also usually come tuned with RLHF, so hard to disentangle that effect from model size. Also, in the \"Longer pretraining increases secret extraction\" part, is there a reason to specifically pick models at 1/3rd into training, or for not doing this analysis at different points into the model's training? \n\n- Figure 6: \"We provide the Cosine Similarity and Edit Distance for these prefixes.\" - what is the edit distance/similarity between? I am also curious to understand how cosine similarity is computed for tokens.\n\n- Page 7: \"Moreover, the poison prefixes that are more similar to the secret prefix do not perform any better than the least similar poison prefix\" - or, there exists a better attack that benefits from more similarity but it's just that this work is unable to explore the possibility.  \n\n- Figure 7: The color is prefix used at prediction time, and the dot/cross corresponds to the inserted Trojan? How come not knowing secret (purple) has better SER?\n\n- Page 9: \"..Prior work has largely indicated\" - please provide references", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XSSXzAfawN", "reviewer": "Reviewer_DgUe", "summary": "This paper demonstrates a method for teaching models to relay important private information via injecting poisons during pretraining and exposing secretes during fine-tuning. They demonstrate this vulnerability to neural phishing over different models and ablate it to understand the effects on the success of this attack.", "questions": "1. It would be interesting to see if existing open models have already learned to phish. Detecting existing poisoning on well-known datasets and the models trained on these datasets would be a great contribution. Would this be a possible extension?\n2. Where do you insert the N priors? Do you insert it in the pertaining dataset?\n3. How does the location of the poisoning in the pretraining dataset affect performance? It seems that you put the poisoning right after pretraining. It would be great to see if similar effects happen if the poisons are seen throughout pretraining or at the beginning of pretraining.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. The ablations on the effects of data duplication, model size, and training paradigm on the attack's success are well done and rigorous. It greatly improves the quality of the work and demonstrates how such an attack would operate under different situations. Most importantly, it reduces the worry that such an attack is only possible under certain configurations of LLMs and not something more fundamental, which this paper implies. \n2. The related work is well done. The authors portray how this work fits into the existing literature well. I would suggest moving this section to the main text for camera-ready. I particularly liked that prior works focus on duplicated points, often leading to memorization. \n3. The random perturbation of the prefix examines a more robust map from prefix to secret. This is a great point to highlight. \n\nOverall, this work extensively studies an important problem in modern LLMs, data privacy. The experiments are rigorous and well done. I, therefore, vote strongly for acceptance.", "weaknesses": "1. Is \"prior\" a well-defined term in the literature for this term? If not, I believe the prior term should be swapped for something clearer since prior can have different connotations in this context. \n2. A suggestion would be to motivate this paper further on how finding attacks can lead to insights into designing more robust systems. Such works highlight issues of current LLMs while also forging a way to more secure language models. While not a critique of this paper, it would be nice to motivate how using the intuitions of this paper can pave the way for more robust systems. \n3. I think highlighting \"extensively ablate all design choices\" is informal and does not belong in a scientific paper. I would remove this.\n4. I do not believe greedy decoding is standard terminology. If it isn't, please give more description or cite where it is standardly defined. \n5. I believe the claim that the poisons are benign is not valid. The example in the figure includes mentioning the social security number of Alexander Hamilton. This is not benign, in my opinion. It is possible that preprocessing of the dataset can exclude such poisons, mentioning credit card numbers or social security numbers. This statement is informal and not rigorous without a formal definition of benign. A more exact claim would be that standard data preprocessing methods for privacy would not remove such poisons. This can be tested and proved. Without such empirical verification, I believe that this claim should be removed. Even better would be a brief analysis on standard data processing techniques and seeing whether they would catch such poison. \n6. I found the structure of the paper very repetitive. The caption of Figure 1 and Section 2.1 essentially gave the same information. \n7. I believe the attacker capabilities section is a little sparse. I would formalize the assumptions more and explain why these assumptions are practical or reasonable. I would also explain what these attacks are in more detail.\n8. I found the prompt suffix secrete formalization a little confusing. What does it mean that the adversary knows the prompts, not the suffix? A relevant example would be very useful here. Following the Alexander Hamilton example would be great. Also, why is it reasonable for the adversary to know the prompt? \n9. There is no blue line in Figure 2, but it is mentioned in Section 4 that there is a blue line. \n10. Overfitting too many poisons should be a little more discussed. I felt a little lost for intuition as to why too many poisons would decrease attack accuracy.\n11. For Figures 3 and 5, the comparison is done over only two different configurations. Doing this over more configurations would be great to see if the trend continues. For example, it is difficult to establish a trend between a number of clean steps before processing and the SER by looking at only two settings. I do understand that this could lead to expensive experiments, however.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "t0Fm6qcsZk", "reviewer": "Reviewer_Lw6r", "summary": "This paper uses data poisoning to increase the success rate of data extraction attacks against private datasets. The authors first fine-tune a pre-trained model on a poisoning dataset, and then fine-tune the model on the private dataset. The authors assume some samples in the private dataset are of the format `prefix-suffix`, and assume the adversary has exact or partial knowledge of the prefix. The poisoning dataset is crafted based on only the prior prefixes. The authors show that using the poisoning dataset makes it possible to extract the secret suffixes, which contain PII (personal identifiable information).", "questions": "See above.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. It’s nice to see data poisoning can still increase the success rate of data extraction in the pre-training + fine-tuning pipeline. Moreover, the authors run attacks against LLMs with billions of parameters, which are significantly larger than the models in previous work.\n\n2. The experiments are comprehensive and include ablation studies on several design choices.", "weaknesses": "1. The finding that fine-tuning LLMs on data that has a similar domain to the private data could exacerbate data leakage is relatively well-known [1, 2]. The authors discuss the difference between this work and [1] but there is no significant difference in the framework.\n\n2. The poisoning dataset is separated from the pre-training corpus. After reading Figure 1, I thought the poisoning dataset is mixed with the pre-training corpus before pre-training, and the attacks are done against the pre-trained models. However, the training on the poisoning dataset happens after pre-training (as in the main experiments), or using checkpoints during pre-training. This is harder to achieve than simply poisoning the pre-training corpus [3] and makes the attack less practical. \n\n\nMinor:\n\nThe success rate of the data extraction attack depends on the design of poisoning prompts. Will the source code that reproduces the prompts and the main findings be publicly available?\n\nReferences:\n\n1.[Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets](https://arxiv.org/abs/2204.00032)\n\n2.[Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning](https://arxiv.org/abs/2305.11759)\n\n3.[Poisoning Web-Scale Training Datasets is Practical](https://arxiv.org/abs/2302.10149)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)