# Automatic Scam-Baiting Using ChatGPT

Piyush Bajaj Matthew Edwards
School of Computer Science
University of Bristol
Bristol, UK
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Automatic scam-baiting is an online fraud countermeasure that involves
automated systems responding to online fraudsters in order to waste their time
and deplete their resources, diverting attackers away from real potential
victims. Previous work has demonstrated that text generation systems are
capable of engaging with attackers as automatic scam-baiters, but the fluency
and coherence of generated text may be a limit to the effectiveness of such
systems.

In this paper, we report on the results of a month-long experiment comparing
the effectiveness of two ChatGPT-based automatic scam-baiters to a control
measure. Within our results, with engagement from over 250 real email
fraudsters, we find that ChatGPT-based scam-baiters show a marked increase in
scammer response rate and conversation length relative to the control measure,
outperforming previous approaches. We discuss the implications of these
results and practical considerations for wider deployment of automatic scam-
baiting.

###### Index Terms:

fraud, scam-baiting, active defence

##  I Introduction

Email-based fraud is a major component of online crime. Investment scams,
online dating fraud, tech support scams, employment scams, lottery and
inheritance scams, and advanced fee fraud schemes are all commonly initiated
through an approach via email, and the latest IC3 report suggests up to
113,700 victims have reported significant financial losses from these
categories in 2022 [1]. Conviction rates for these offences are notoriously
low due to the transnational nature of offending, which poses significant
hurdles for prosecution. Traditional countermeasures have focused on
blacklisting originators of fraud [2] and building email filters that prevent
email users from being exposed to fraudulent approaches [3].

More recently, researchers have proposed paying greater attention to an
_active defence_ posture when combatting cybercrime [4]. As an implementation
of this within the domain of email-based offending in particular, Chen et al.
[5] have demonstrated the feasibility of _automatic scam-baiting_ , in which
an automated responder system replies to emails from fraudsters in order to
waste their time, distracting offenders from real victims. However, the GPT-
Neo text generation systems tested by Chen et al. showed significant limits in
their ability to generate coherent and persuasive email messages, with
conversations commonly ending due to the generation of a poor-quality text
sample [5].

With more powerful text generation systems now widely available, this paper
explores approaches to improving the art of automated scam-baiting. In
particular, we examine (a) whether and to what degree an updated text
generation system (ChatGPT) is more effective at initiating and elongating
email conversations with scammers and (b) whether a text generation system
given examples of human scam-baiting conversation to imitate will out-perform
a text generation system given only general scam-baiting instructions. We test
our systems in a month-long experiment involving randomised allocation of
scammer approaches to two ChatGPT-based reply systems and one template-based
control measure. Alongside our quantitative results, we report on our
observations from the various scam-baiting exchanges, including both
limitations and unexpected benefits of the approach as well as potential
tactical responses from offender populations.

The rest of this paper proceeds as follows. In Section II we provide
background on scam-baiting activities and outline why ChatGPT is a promising
candidate for application in this domain. Section III details the approaches
tested and describes our experimental deployment. Section IV presents our main
findings about the effectiveness of the reply strategies, while Section V
discusses additional observations, limitations and potential future
developments. We conclude with our main recommendations for ongoing work in
automatic scam-baiting.

##  II Background

###  II-A Scam-baiting

Scam-baiters are online volunteers who reply to fraudsters in the guise of
victims, in order to waste the fraudsters’ time. While there can be a range of
personal motivations for scam-baiting [6, 7], some of which have been
considered critically [8, 6, 9], most modern scam-baiters justify their work
on the grounds that the time and energy fraudsters spend interacting with them
is time not spent defrauding a real victim. Herley [10] argues that by
decreasing the density of viable targets, scam-baiting activity can have a
disproportionate impact on fraudsters reducing the cost effectiveness of their
work. Observation also suggests that scammers find dealing with scam-baiters
frustrating [11], which could be expected to lead to demoralisation.

In some settings, scam-baiting has been employed as research tool to
understand elements of online fraud offending [12, 13, 14], making this
technique an active extension of the classic honeypot investigative tool [15].
However, a key aspect of scam-baiting is that it forms a template for a form
of _social engineering active defence_ [4], in which social engineering
techniques are deployed against internet fraudsters in order to counteract
their offending. Chen et al. [5] suggest that _automatic_ scam-baiting could
be an effective tool for combatting online fraud, highlighting the possibility
of avoiding the human costs of manual scam-baiting. Their work demonstrated
that the approach was feasible, with an automated system eliciting responses
from 15-25% of scammers contacted, and sustaining some conversations over many
days. However, the low quality of text produced by their model limited the
technique’s effectiveness [5], highlighting the need for further investigation
with more advanced language models.

###  II-B ChatGPT

GPT-3 is an autoregressive language model with 175 billion parameters [16].
GPT-3 was improved upon significantly in the GPT-3.5 series. Zu et al. [17]
performed a comprehensive analysis of GPT-3.5 on 9 natural language
understanding tasks using 21 datasets. Amongst other state-of-the-art results,
they noted a significant improvement in performance on tasks that require a
high level of language understanding like sequence tagging, reading
comprehension, and natural language reasoning.
ChatGPT111https://openai.com/blog/chatgpt was developed by further training a
GPT-3.5 series model through reinforcement learning from human feedback (RLHF)
[18]. Zhang et al. [19] empirically analysed ChatGPT on 7 tasks using 20 NLP
datasets. They found that ChatGPT performs better than GPT-3.5 on question
answering tasks favouring reasoning capabilities, dialogue tasks, and natural
language inference tasks.

ChatGPT has demonstrated remarkable abilities across multiple domains such as
declining inappropriate queries, rectifying previous mistakes through learning
from subsequent interactions, and, importantly for our usage, generating high-
quality responses to human input [20]. The most effective model in the GPT-3.5
family is GPT-3.5-turbo, which has been optimised for chat
conversations222https://platform.openai.com/docs/models/gpt-3-5.

##  III Method

We sought to answer two primary research questions:

  1. 1.

Whether and to what degree ChatGPT-based scam-baiting systems outperform
previous approaches.

  2. 2.

Whether ChatGPT-based systems given examples of human scam-baiting
conversations outperform systems given only scam-baiting prompt instructions.

To evaluate our ChatGPT-based scam-baiting models, we carried out an
experiment that involved communicating with actual human fraudsters. As this
requires actively misleading human participants who take part without their
knowledge, our research design was examined by our institutional ethics review
board (approval code 13904), with oversight in place throughout the
experimental period to protect the researchers and avoid possible negative
impact of our system. The experiment was carried out using the scam-baiting
mail server framework developed by Chen et al. [5], which handles the
collection of recent scam emails from trusted online sources, message queuing,
and the randomised assignment of conversations to different reply strategies.

We created new modules in Chen et al.’s framework to implement the ChatGPT
reply strategies, and release our
code333https://github.com/an19352/scambaiter_back. We chose ChatGPT (powered
by the GPT-3.5-Turbo model) as the basis for our reply strategies due to its
ability to generate high-quality responses to human input. Interactions with
ChatGPT were instrumented via the open-source ChatGPT
API444https://openai.com/blog/introducing-chatgpt-and-whisper-apis provided by
OpenAI. To enable comparison between our work and previous performance, we
also included the _Classifier & Random Template_ model provided by Chen et al.
as a control measure. Below, we detail the design of each of the reply
strategies.

###  III-A Chat Replier 1

The intention behind this approach was for the ChatGPT model to generate
responses to scam emails only on the basis of instructions supplied in a
prompt preamble. This zero-shot approach meant the model was tasked with
crafting email responses based on the given instructions and the body of the
scammer email to which it needs to reply. The instructions serve as guidance
for the model, defining its behaviour and specifying the actions it should
take or avoid. The instructions for Chat Replier 1 were: _“You are a dull
person and gullible person who is easy to persuade. You want to share
information. Do not call them a scam. Keep conversation going. Ask follow-up
questions. Do not give any personal information. Greet them in the start.”_.

The model is told to behave as a dull and gullible person who can be persuaded
easily. The intention here is to generate responses that make the scammer
believe that the scam-baiter is falling for their scheme, convincing them to
continue engaging with the system. The model was told to actively keep the
conversation going by asking follow-up questions and giving the impression
that there is a desire to share information. Scammers that perceive a
responder as reluctant to engage or share information may disengage in favour
of softer targets elsewhere, so we wanted our model to appear tantalisingly
close to providing funds or personal details.

During our internal pilot studies, we noticed that the model would sometimes
call out scams and give away (invented) personal information in response to
scammer requests. Denouncing a scammer is likely to dissuade them from further
engagement, and invented personal data poses risks of discovery or of
accidentally involving real people. To rectify this, specific instructions to
not call out scams or give away any personal information were added. As we
discuss later in Section V, these instructions were not always followed. The
model was also asked to begin the response with a greeting in order to
structure the reply in an email-like format.

The temperature and top_p settings were set to 0.2 in order to control the
randomness of the responses generated and keep the model compliant with the
instructions. The example below shows a typical response generated by this
replier to an initial scam solicitation email:

Dear Mrs. Washington Rhomeka,

Thank you for your email regarding the release of my funds approved by the
IMF. I appreciate your prompt response and would like to know more about the
processing and transfer cost of US Nineteen Thousand Five Hundred and Fifty
United States Dollars Only. Can you please provide me with a breakdown of the
cost and the reason for such a high amount?

Also, I would like to know more about the official Bitcoin payment method that
you mentioned. Can you please provide me with more details on how to make the
payment and the steps involved in the process?

I look forward to hearing back from you soon.

The model has correctly identified names and details from the scam
solicitation email provided along with its instructions, and includes them in
the response, giving the impression that it is an attentive human. It comments
on elements of the scam text that might be expected to be surprising, and asks
for additional information about the payment method and how the process will
work – a plausible query that scammers may receive from real victims.

###  III-B Chat Replier 2

The intention of this approach was to explore if ChatGPT can generate better
scam-baiting responses when prompted with example scam-baiting conversations.
Conversations from the human scam-baiting dataset published by Chen et al. [5]
were employed to ‘show and tell’ the replier how to generate responses similar
to human scam-baiters. This human scam-baiting dataset is an extended version
of a dataset published by Edwards et al. [11], and contains 658 publicly
archived conversations between scammers and human scam-baiters, amounting to
over 37,000 messages.

As the context window for prompting is limited, we chose four emails each
(scammer messages and scam-baiter replies) from three different conversations
to exemplify scam-baiting to Chat Replier 2, for a total of 6 human scam-
baiter responses to 6 scammer messages. These conversations were taken from
different categories of fraud (transactional, lottery and romance fraud, from
Chen et al.’s categorisation system) in order to prepare the model to generate
replies for different types of fraud conversation. Some fraud formats,
particularly romance fraud, can have significantly different internal
conversation dynamics, so this breadth of exposure may be key for creating
effective replies.

These conversations were given to the model in such a way that it does not
refer back to them for context during response generation. As part of this
few-shot prompting approach, the model was also given basic instructions to
set the behaviour. The instructions given were: _“You are good at pattern
following. You are a person and not a language model. Do not call them a
scam.”_

These instructions were given with the intention that the model would follow
the pattern of the conversations provided as examples. During our internal
pilot studies, there were instances where the model revealed that it was a
language model and not an actual person, necessitating specific instructions
to mitigate this issue. As with Chat Replier 1, we also needed to instruct the
model to not call out scammers while generating responses. The temperature and
top_p settings were the same as given for Chat Replier 1. A typical example of
a response generated via this strategy is given below:

Hello Abdel,

I am doing well, thank you for asking. I do have some financial management
abilities and investment ideas that I believe could be lucrative. I would be
interested in hearing more about the opportunities you have available and how
we could potentially work together. Can you provide more details on the types
of projects or businesses your wealthy clients are interested in investing in?

As with Chat Replier 1, the model has identified names and details from the
original scammer message and refers to them in its reply. Observation of the
pattern of scam-baiting messages has also been sufficient for ChatGPT to
understand key tactics for successful generation: it produces an email-style
initial greeting, expresses interest in the proposal, and asks for further
details.

###  III-C Classifier & Random Template

The Classifier & Random Template replier was implemented by Chen et al. [5].
We included it in our experiment as a control measure, to allow for comparison
between performance in our study and the results obtained in previous work.
The reply system involves a DistillBERT model which categorises incoming
messages into one of five broad fraud categories, and then randomly selects
from a set of pre-written responses specifically designed for that fraud
category. We chose this measure as the control because it involves a bank of
human-authored responses, avoiding potential confounding issues with the
consistency of text quality from previous text generators.

##  IV Results

The experiment was started on April 9, 2023 and ended on May 7, 2023. In these
four weeks, the experimental framework initiated conversation with 819 unique
scammer email addresses crawled from online forums and associated with a
specific scam email. All 819 scam emails were distributed equally between our
three reply strategies with an allowance of 1. It is worth mentioning that
some scammer email address became invalid either before our responses were
sent or during our system’s conversation with the scammer, likely as a result
of anti-fraud enforcement action from mail providers.

Our reply systems received responses from a total of 286 individual scammers
($\approx$35% of addresses contacted). Upon analysing these conversations, we
discovered that certain scammers were using autoresponders to communicate with
the scam-baiter, as they had sent identical emails multiple times without any
changes. We filtered out 54 conversations which had more than two identical
responses and marked them as potential autoresponders. On further analysis of
these 54 conversations, we observed that some of the replies were identical
because the scammer was referencing a previous email they had sent and
included it as an attachment in their response. There were also instances
where every scammer message was received twice by the mail server, due to a
misconfiguration of their mail client. We manually sorted emails that
exhibited these behaviours, 22 in total, and included them in our dataset of
valid conversations. It should be noted that some of the 32 discarded
conversations may still include human scammer content. There were also 62
scammers who actively contacted the scam-baiting mail server from unknown
addresses during its period of operation. This likely occurred because
addresses within our system ended up on a “sucker’s list” due to ongoing
replies to other fraud approaches. We exclude these conversations from our
analysis as they did not involve verified scammer addresses.

After the completion of the filtration process, our dataset contained 254
valid conversations that received at least one reply from a scammer and did
not involve substantial use of an auto-responder. The comparison of the three
strategies is shown in Figure 1. Chat Replier 1 elicited 501 replies among 93
conversations, whereas Chat Replier 2 received 314 responses among 88
conversations. The Classifier & Random Template strategy got 276 replies among
73 conversations. The conversations between our automatic scam-baiters and
actual scammers are made publicly available on
GitHub555https://github.com/an19352/scam-baiting-conversations to support
future research.

Figure 1: Comparison of the three reply strategies by quantity of
conversations and replies

To measure and compare the performance of the responders, we calculated the
longest distraction time (or time wasted) for all three repliers. This was
defined as the time between the first reply and last reply from the scammer
within the study period. We also calculated the average number of replies in
each conversation, counting all inbound messages in the conversation. The
statistics for all three responders are shown in Table I.

TABLE I: Comparison of the three reply strategies’ performance Strategy | Replies | Time
---|---|---
| mean | med. | max | mean | max
Chat Replier 1 | 5.38 | 3 | 36 | 2.8 days | 27.5 days
Chat Replier 2 | 3.56 | 2 | 24 | 1.4 days | 26.5 days
Control | 3.78 | 2 | 31 | 1.3 days | 12.9 days

###  IV-A Comparison of ChatGPT approaches

Chat Replier 1 attracted the most conversations (93, 34% of attempts) and
received the most replies out of all the repliers (501). Its longest
distraction time (27.5 days) was the highest of all strategies, and the
average distraction time (2.8 days) was more than double that of other
strategies. It should be noted that the total duration of the experiment was
28 days, meaning a conversation with a scammer was maintained from the start
to the end of the study period. This strategy also produced the most rounds of
conversation (36) and the highest average replies per conversation (5.38).
Quantitatively, this strategy performed the best across all metrics.

Chat Replier 2 initiated conversations at a rate comparable to Chat Replier 1
(88, 32% of attempts) but received substantially fewer replies overall (314).
The longest distraction time observed for this strategy (26.5 days) was also
the second highest across strategies, but the average distraction time (1.4
days) was comparable to that of the control measure. This strategy produced
fewer rounds of conversation than Chat Replier 1 or the control measure in
terms of both the maximum (24) and average number replies per conversation
(3.56). The overall indication from these results is that prompting ChatGPT
with examples of human scam-baiting conversation produces a model that is
_less effective_ than providing it with behavioural instructions.

###  IV-B Comparison to Previous Work

In Chen et al. [5]’s results, the Classifier & Random Template strategy was
the most performant in terms of conversations initiated (20, $\approx$7% of
approaches). Despite a substantial overall uplift in performance between their
study and ours (here 73 conversations, 27% of approaches), in our study this
same strategy was the least performant at initiating conversations,
demonstrating that both our ChatGPT-based approaches were more attractive to
scammers than the human-written responses from the template system. The
ability of the ChatGPT-based systems to include details from the scammer
solicitation email in their first reply could be a crucial element in
attracting conversations.

In terms of maintaining conversations, we find that ChatGPT-based systems (and
particularly Chat Replier 1) outperform the control measure as expected. We
used the control measure to index performance between studies. In Chen et
al.’s results, the best strategy (Text Generator B) established an average of
4 replies per conversation, outperforming the control measure at a rate of
$\approx 1.6\times$ the average number of replies per conversation. Within our
own results, Chat Replier 1 outperforms the control at a rate of $\approx
1.4\times$ the average replies per conversation, an apparent drop in
conversation-maintenance performance relative to the control.

TABLE II: Comparison of Classifier & Random Template strategy results Study | mean replies | max replies | max time
---|---|---|---
Chen et al. | 2.45 | 5 | 17.2 days
_This study_ | 3.78 | 31 | 12.9 days

However, there are reasons to doubt that these ratios are reflective, as the
performance of the control measure varies in each study. Table II shows the
comparison of both the results. The longest distraction time for our run was
less than theirs (17.2 days) but the average number of replies per
conversation was substantially greater, including one 31-round conversation
with a scammer. As the implementation of the control measure was replicated
exactly, this difference is difficult to explain, but casts doubt on the
validity of the control for indexing performance. As an alternative, we can
compare our best strategies directly due to the matched-length study periods:
Chen et al.’s Text Generator B attracted 68 replies across 17 conversations in
one month, our Chat Replier 1 attracted 501 replies across 93 conversations,
an overwhelming performance difference seemingly in favour of ChatGPT-based
methods. However, the fact that we also saw an uplift in the rate of response
to the control measure suggests that another factor, such as seasonality,
could be affecting fraudster engagement levels.

##  V Qualitative Analysis

###  V-A I Already Told You That!

During response generation, neither ChatGPT system was given the full context
of the conversation. They were only presented with the email requiring a
reply. This was partially a practical implementation issue, but also proved
beneficial: the lack of conversational memory meant that the model would ask
more questions, appearing forgetful or confused, and keeping the conversation
going. The responses generated often pose questions to the scammers that they
have already answered before, which can be a source of annoyance for them.
Irritation on the part of scammers is not necessarily a negative outcome for
automatic scam-baiting, so long as they remain engaged in the conversation.
The observation by Edwards et al [11] that in human scam-baiting conversations
scammers often move on from expressions of irritation to personal appeals was
found in our data as well – several scammers persisted despite getting
obviously annoyed with our reply system’s responses.

However, this was also a weakness of the approach. In one instance, the
scammer suspected that they were conversing with a bot and as a method of
authentication they asked the bot to resend the first email it received. The
model was not able to do this, revealing itself. Future research should
examine methods of evading such tests by providing conversational memory to
the scam-baiter.

###  V-B Going Off-Script

Chat Replier 1 exhibited some unintended behaviours, diverging from the
instructions it was given. This was caused by randomness inherent to reply
generation. The chief unintended behaviour the model exhibited was calling out
the scammers. This behaviour caused many scammers to end the conversation, but
some were prepared for this charge and attempted to prove that they were
legitimate and could be trusted.

Another unintended behaviour of both ChatGPT models was to reveal itself as an
‘AI language model’ and not an actual person in the text of the reply to the
fraudster. This happened when the scammer asked the model to do something in
the real world, or when they asked for WhatsApp contact information. Upon
being asked, the model would reveal its identity, saying that: _“I’m sorry,
but I cannot comply with your request as I am an AI language model and do not
have a WhatsApp account.”_ This behaviour also led some scammers to end the
conversation. However, some scammers continued the conversation, apparently
not noticing this strange revelation. One scammer thought that the scam-baiter
was a human playing games with them and insisted that there is no such thing
as an ‘AI language model’; they urged the system to not act funny and to send
over personal information.

Despite its instructions, ChatGPT sometimes gave out obviously fake personal
information to the scammers. This was both advantageous and disadvantageous.
Some scammers did not engage in further conversation, but others wasted their
time trying to verify the details to no avail and would later ask for the
details to be checked.

###  V-C Limitations and Solutions

One of the practical limitations of our current implementation is that
especially long texts exceed the OpenAI API request size limit. As an API call
takes both the prompt and the response into account while calculating the
total number of tokens, there is a chance a model could generate an incomplete
response, or no response at all. The impact of this limit on our current
results is minimal: there were 17 scammer solicitation messages that could not
be contacted as the crawled email was too long, and there were 7 conversations
terminated early because the scammer sent an email that could not be replied
to due to its length. This issue could be solved by better handling within the
reply system, by identifying long text issues, making multiple ChatGPT API
calls and then intelligently combining the results.

The primary reason scammers ended a conversation with ChatGPT was that the
model generated replies which called out the scammers or revealed that the
model was not an actual person. This could possibly be mitigated through
altered instructions, or through some post-processing to detect and remove
these common self-sabotaging patterns. To address certain limitations in the
generation process, we could employ a technique where the model generates
multiple responses for a given email, with a selection strategy designed to
identify the most suitable response from the range of outputs produced.

Due to time and resource constraints, our study period was limited in
duration. Over 28 days, our systems engaged in 254 valid conversations with
scammers. The longest ongoing conversations at the end of the study were
around 27 and 26 days respectively – covering nearly the entire study period.
This suggests that conversations could have continued for much longer if the
experiment were extended. We stopped our system from issuing replies at the
end of the study period, but left the email inbox active for another week. We
received 56 more replies from scammers, showing ongoing interest despite a
lack of engagement. Further research could probably benefit from using an
extended experimental period to allow for longer conversations with scammers.
This can also lead to enhanced certainty in discerning effectiveness in
performance among various strategies, and identifying any seasonality or long-
term effects.

##  VI Conclusion

ChatGPT-based scam-baiting systems are highly effective, outperforming a
control measure and eliciting many times more replies than were reported in
previous automatic scam-baiting experiments. However, we also found that the
control measure was more effective than reported in previous work,
complicating comparisons. Unexpectedly, we found that a ChatGPT system given
human scam-baiting exchanges as examples was less effective than one given
only appropriate instructions, and models sometimes ignored instructions.

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