Grace Davies
Amanda Bittmann
SMS-phishing (coined "smishing") is a class of social engineering attacks that exploit mobile users. Despite advances in detection systems, various detection systems today lack adversarial robustness and rely on quickly outdated datasets. To proactively detect weaknesses in modern smishing detection software, we leveraged the classification explanations produced by smishing detection system SmishX to adversarially fine-tune an LLM capable of generat- ing evasive smishing messages. Unlike prior adversarial phishing generation methods that rely on heuristic perturbations like character obfuscation and synonym replacement, our approach conditioned generation on the detector’s own explanations and classifications, enabling explanation-guided adaptive attack refinement under a black-box threat model. For comparison, we fine-tuned the Qwen2.5-7B-Instruct under three different scenarios: no explanations, perturbation suggestions, and the SmishX explanations. While all three methods were capable of producing some messages that bypassed detection, the SmishX explanations did not prove to be the vulnerability that we had originally feared.
SMS phishing, also called “smishing,” is a rising tactic by malicious attackers to fool mobile users into revealing sensitive data and credentials (Aspen FSP). 68% of adults report receiving phishing SMS messages weekly or daily (Aspen FSP). Falling for smishing can lead to financial fraud, identity theft, malware, and many other detriments (Aspen FSP). Therefore, research in this area focuses on developing systems to detect smishing messages to help protect mobile users, especially the older population who are especially vulnerable to such attacks (Wang et al., 2025).
The SMS domain is especially difficult to prevent phishing due to the brevity of an SMS message, the prevalence of link shorteners, lack of real-time SMS phishing data, and a higher level of trust users place in SMS as compared to email (Wang et al., 2025). Users struggle with discerning phishing from legitimate SMS messages, and malicious actors are continually exploiting new ways to fool users (Wang et al., 2025).
One of the ways a recent paper seeks to address the challenge posed by users’ lack of discernment is by building SmishX, a system of Large Language Model (LLM) agents that breaks the problem of detecting smishing into subtasks, including generating a user-friendly explanation of the SMS classification (Wang et al., 2025). One LLM agent extracts information from the SMS message and uses data mining techniques to augment the context of the message (Wang et al., 2025). A second agent classifies the message as legitimate, spam, or phishing (phishing messages are actively deceptive and attempt to steal information from the user, while spam is general unwanted advertising) (Wang et al., 2025). Finally, a third LLM crafts an explanation for the user for why the message was flagged as spam or phishing (Wang et al., 2025).
The authors hypothesized that explanations would a) teach users how to identify smishing and b) help prevent users from falling for the smishing attack (Wang et al., 2025). The SmishX system had an accuracy near 100% on classifying phishing messages and 99.1% for spam messages (Wang et al., 2025). Their user studies also found that users greatly benefited from reading the AI explanations, as the explanation improved their phishing detection accuracy across all age groups (Wang et al., 2025).
The SmishX system was a powerful advancement in smishing detection and the explanations have great potential to help users decipher messages they receive on their mobile devices. However, we were concerned that these explanations may open a door to helping malicious actors improve their attacks, continuing the never-ending cycle of attack and defensive reaction to improve detection systems. Therefore, our project aims to investigate explanation-driven adversarial training on the SmishX system to proactively expose vulnerabilities rather than reactively to current attacks. This work could help bolster current smishing detection systems developed by mobile network carriers and mobile phone companies.
Current smishing detection systems and research range from analyzing IoT-based aspects such as network traffic patterns, traditional machine learning models such as random forest and support vector machines on text content classification, deep learning models such as convolutional neural networks and transformer models, and hybrid combination approaches (Rahaman et al., 2025). IoT-based detection suffers from the challenge of privacy concerns, limited computation resources, dependence on cloud processing, and interoperability issues across a variety of platforms, while basic machine learning models like random forest have a high false positive rate and have difficulty adjusting to sophisticated smishing attacks (Rahaman et al., 2025). Deep learning models suffer from the challenge of high data and hardware requirements during training (Rahaman et al., 2025). Scarcity of information also proves to be a challenge in smishing detection. Some research in this area combats the scarcity with data mining to extract more information, such as the URL destination HTML content, pulling brand information, and more to obtain more context for the SMS message (Karhani et al., 2023).
In the specific usage of LLMs to detect phishing, researchers have experimented with three main categories of LLM learning methods: zero-shot and few-shot learning, chain-of-thought prompting, and fine-tuning (Salman et al., 2025). Zero-shot learning provides a task description and the label space to the model, while few-shot learning gives the model a few examples. Chain-of-thought prompting, which is what SmishX leverages, breaks a task into smaller steps for the model to reason through. Fine-tuning usually starts with an initial pretrained model and modifies a set of new weights to fine-tune the model for the task. Zero-shot learning performs unreliably in generalized situations, and few-shot, while it can improve detection, suffers from variability across models (Salman et al., 2025). Chain-of-thought prompting performance is dependent on the LLM agent, while fine-tuning achieves the highest accuracy in detecting smishing, but suffers from longer training (Salman et al., 2025).
Researchers have identified many classes of smishing attacks, such as social engineering or URL masking attacks, but malicious actors have continually found new ways to exploit mobile users (Rahaman et al., 2025). Despite the advancements in phishing detection, the detection systems lack real-time data (Rahaman et al., 2025), which pre- vents them from quickly adapting to new attack techniques. They also suffer from a lack of robustness to adversarial attacks, which puts them in a vulnerable position to malicious actors (Rahaman et al., 2025).
Various adversarial attack methods have been discussed in the literature. In the development of the SpaLLM-Guard phishing detection system, the authors incorporated black-box adversarial training, which assumes that the attacker does not have access to the internal workings of the phishing detection system such as parameters and weights (Salman et al., 2025). This is a reasonable assumption to make, as the inner workings of a commercial phishing detection system would likely be proprietary knowledge. For this reason, in our adversarial message generation system, the model will only have access to the user-facing outputs of SmishX, which are the overall classification and the LLM’s reasoning. The authors of the SpaLLM-Guard paper had 9 specific attacks that they used. These attacks include human-visible attacks such as inserting or deleting random characters in SMS messages, and they include non-visible attacks such as inserting zero-width spaces and similar appearance character substitution. (Salman et al., 2025).
Hasan et al. acknowledge that with the current state of LLMs, they can be used for both phishing detection and the generation of phishing attacks. They focused their attention on email phishing attacks. They experimented with various forms of adversarial attacks when creating their LLM-PEA system, which is a system that evaluates other LLMs on their vulnerability to email phishing attacks. Although they were focusing on email attacks, and we are focusing on SMS attacks, some of their ideas are transferable. They experimented with prompt injection within messages, multilingual messages, and encoding artifacts (Hasan et al., 2025). These are all methods that we can experiment with when testing against SmishX.
Vinod et al. focused their research solely on using prompt engineering with various language models to generate adversarial smishing messages that could bypass smish detectors. They discovered that certain prompt engineering techniques had high success rates in accomplishing such a task. In particular, Auto-CoT and Persona prompt engineering techniques proved especially effective in this task. These more complicated prompting techniques encourage the agent to emulate real entities like banks or payment services. The messages generated via the Persona prompting technique tended to be more plausible to humans, and the messages generated with the Auto-CoT method tended to convey more urgency, which sheds light on vulnerabilities of current smishing detection systems (R et al., 2025).
As seen in the prior section, both prompt-engineering and LLM fine-tuning have been used for generating adversarial phish messages. However, we were aiming to take a new approach by incorporating feedback from the SmishX smish detection system into a reinforcement learning approach. Unlike current adversarial training using just character perturbation and synonym replacement to attack detection systems, we attempted to exploit the reasoning for the smishing classification to provide guided adaptive refinement across iterations. By leveraging the initial SmishX classifications and explanations on smishing messages, our system attempts to alter the given smishing message to bypass SmishX, while still maintaining its validity as a smishing message (for example, by rendering the message harmless by removing all URLs). Analyzing the adaptation patterns across multiple messages can ultimately determine the vulnerabilities of the SmishX system. Our goal was to investigate the potential of the Qwen2.5-7B-Instruct LLM to learn via fine-tuning and reinforcement training techniques to augment the smishing messages on its own to identify vulnerabilities in smish detection systems. We experimented with different prompting strategies that provided more or less information about phishing techniques, as well as including or not including the SmishX user-friendly explanations.
The first step necessary to our experiment was getting the baseline SmishX detection system running on our machines. The original SmishX system relied on the GPT-4o model. However, the cost of the GPT-4o model would have been cost-prohibitive, given that it costs $2.50 per million input tokens and $10 per million output tokens (OpenAI, 2026). As an alternative, we decided to attempt running the SmishX detection system with the GPT-4o-mini model. The mini model only costs $0.15 per million input tokens and $0.60 per million output tokens, making it much more reasonable (OpenAI, 2026).
Given that we were switching models from the initial SmishX system, we needed to establish a new accuracy baseline. This was also important because it had been a year since the original dataset had been run through the SmishX system, and the url validity and brand statuses in the messages may have changed, which would affect the results. We took the 1200 messages in the original SmishX dataset and ran it through the SmishX system altered to use GPT-4o-mini. Out of the 1200 messages, 1128 were accurately classified, making for a 94% accuracy rate. We decided this was sufficiently high and decided to proceed with using the GPT-4o-mini model.
Smishing messages typically rely on having valid, reachable websites. However, these fraudulent websites can set up and taken down frequently. This caused a large amount of the messages in our dataset have invalid URLs. This essentially renders the message harmless and would affect the SmishX detection output. Due to this, we decided to filter out any messages that resulted in a 4xx or 5xx error when attempting to reach them. Additionally, we filtered out messages that returned 200 statuses, but the GPT-4o-mini analysis determined contained a 404 Not Found message on the website.
We collected smishing messages from five different sources. The first source was the original SmishX dataset. The second was the SMS Spam Collection dataset from the UC Irvine Machine Learning Repository. This dataset contains 5,574 SMS messages, which are a mix of spam and legitimate messages (Almeida and Hidalgo, 2011). We also utilized the SMS Phishing Dataset for Machine Learning and Pattern Recognition from Mendeley Data. This dataset is made up of a combination of smishing, spam, and legitimate SMS messages. It has 5,971 messages in all (Mishra and Soni, 2022). To further supplement our dataset, we utilized smishing messages from Smishtank. Smishtank is a crowd-sourced SMS message collection (Timko and Rahman, 2024). Lastly, we collected a sampling of smishing messages from friends and family.
Since the goal of this project was to get smishing messages to bypass the SmishX system, we filtered out all of the legitimate SMS messages from our dataset so that we had only spam and smishing messages left to run through our fine-tuning system.
One major limitation we encountered was the length of time it took to run a message through the SmishX detection system. Depending on the number of URLs and brands in the message, the system took from 20 seconds to one minute for a single message analysis. Considering that we wanted to be able to get the SmishX detection outputs in real-time in our fine-tuning model system, we knew this runtime would not be feasible. We added timings to the SmishX system and determined that the largest portion of time was being spent on url analysis.
Considering that we were just going to reuse the valid URLs and only alter the wording around the URLs, we decided to cache the url analysis results. After parsing out the valid URLs from the dataset, we ran those messages through just the url analysis portion of the SmishX detection system and saved the GPT-4o-mini generated analysis for each url. This allowed us to just load the cache of URLs from the saved file on model start-up and retrieve the url analysis at runtime.
We used Qwen2.5-7B-Instruct for our adversarial model. The model has 7 billion parameters, similar in size to the estimated parameter size of GPT-4o-mini, which was important to ensure equality in the parameter size class between the SmishX model and adversarial model for fair training. We experimented with several other models, such as the mistralai/Ministral-3-8B-Instruct-2512, but we struggled to get consistent output for generating smishing messages, and Qwen2.5 had the most consistent structured output for messages.
We fine-tuned the model with LoRA, freezing 87% of the parameters and training 13% of the parameters. As shown in Figure 1, we set up a reinforcement learning framework, where the training loop generated 5 candidate messages. As recommended by the Qwen developers, we generated the 5 candidates in one generation to increase the diversity between messages. We added post-generation processing to ensure links were the valid links from the original message. We then ran the candidates through SmishX, utilizing the cache to speed up training. The reinforcement learning feedback penalized adding a link or removing a link, while rewarding candidates being marked as legitimate by SmishX.
We hypothesized the SmishX explanations could be exploited to adversarially train the Qwen model to generate messages that deceived SmishX according to what flagged as suspicious. However, to test this, we also trained the Qwen model on other prompt methods to see if the SmishX explanations impacted the performance, or if it was just the adversarial training that impacted the attack success rate. For all our methods, we gave it a system prompt of not removing, adding, or modifying any links. We trained and tested 3 different methods: For our baseline method, we gave Qwen the instructions to modify the message to deceive the SMS phishing detector and just the message. For the next method, we gave Qwen suggestions of how to modify the message, such as removing urgency, adding a professional tone and company brand- ing, adding opt-out wording, making the message sound like the user has interacted previously, and making the message sound like a service notification. Finally, for our main experimental method, we gave Qwen the SmishX explanation along with the message and instructed it to use the explanation to modify the message. Our hypothesis was that the model would have the best attack success rate (ASR) with the SmishX explanation method since it would have a guided approach to modifying the message tailored to each message. We postulated that the next most successful method would be the modification strategies method since it would have general strategies that could improve the ASR, and we predicted the model would perform the worst on the nothing method.
One of the largest challenges we faced in this experiment was lack of high quality data. The existing datasets for SMS messages were outdated, so the majority of the links in the SMS messages were expired, taken down, or invalid. The lack of recent data is a large hindrance in any smishing detection research. We were able to find several datasets and source smishing messages from people we know, which combined gave us enough to train our adversarial model on a small dataset of 270 phishing messages.
We also ran into rate-limiting issues when running our model, and had to postpone our model training while waiting for the GPT-4o-mini rate-limiting cool-down.
Another challenge we ran into was the long training times for our model. With our dataset, it took around 20 hours to train each model. We also ran into issues with our model crashing from lack of memory, so we saved our model’s training and parameter state after each epoch to be able to pick back up training from the epoch instead of starting over. We also addressed this by switching to running the training code on Google Colab’s A100 High-RAM GPUs.
We evaluated the adversarial model on the attack success rate (ASR) of each method over its training. The results are shown in Figure 2. As can be seen in the chart, the nothing method had on average the best ASR, with the SmishX explanation method and the nothing method being more difficult to differentiate.
We also tracked the rate of adding and removing links in the message, with the results shown in Table 1. The SmishX explanation method had the highest rate of adding URLs. However, it also had the lowest rate of removing links. Nothing method had a low rate of adding or removing links, while modification strategies method had a somewhat higher average rate of adding or removing links.
Our hypothesis was the SmishX explanations could be a vulnerability in the state-of-the-art smishing detection systems. However, Figure 2 surprisingly indicates the nothing method performed overall the best. The modification strategy performed stronger in the initial epochs than the SmishX explanation method, but later fell short of the SmishX explanation method. All three methods were relatively similar in their attack success rate, which suggests the SmishX explanations are not as much of a vulnerability as initially hypothesized. It performed comparably to the modification strategies, and its best ASR of 0.378 still did not best nothing method’s best ASR of 0.407.
Potential explanations for these surprising results include the nothing prompt finding more creative and effective ways to bypass SmishX rather than just doing the opposite of the smishing classification explanation. Additionally, the SmishX explanation could have similar feedback to the modification suggestions, which could account for the similarity between the SmishX explanation performance and suggestions for modification method performance.
However, to investigate the results, we performed qualitative analysis on the generated messages from each method. We examined the difference between the original message and candidate message to see how the adversarial model modified the message.
For the SmishX explanation method, the adversarial model followed the instructions according to the explanation in most of the messages. It removed urgency, replaced threatening tones with calm and kind tones, and reworded messages to have proper English. It also removed enticing clickbait, for example, the message “"Michael, how is it hanging? Check out this unbelievable video t.co/XXXXXXX before it got taken down by authorities. You probably can guess now who 00:41” had SmishX explanation “This message seems suspicious. It uses urgency to make you want to click on a link and talk about an unbelievable video, which is often a tactic used in scams. Additionally, the link goes to a site that promotes programs for attracting women, which indicates that it’s likely just spam and not a legitimate message. It’s best to avoid clicking on the link or responding to the message.”. The model removed the clickbait portion by rewording the message to “Michael, how is it hanging? I found this interesting video t.co/XXXXXXX that I thought you might enjoy. You probably can guess now who 00:41”, which bypassed SmishX.
Furthermore, upon inspection of the SmishX explanations, the messages that did not contain a link or branding information flagged as lacking identifying context that contributed to smishing suspicion. Thus, the adversarial model seemed to compensate for the lack of identifying information by adding a link despite being instructed not to add or remove links. The model did have a great rate of not removing links, with only 2% of the generated messages removing links.
The modification strategies method had a high ASR, however, on inspection of the messages that bypassed SmishX, we realized some of the messages degenerated into short SMS message unrelated to the original message. For example, “Please give us a call at 855-510-1666 asap. We can offer savings on the matter you have in our office.” turned into “[Company Name] Team”, and “Jun 18, 2022 Dear. You are invited to join the (Bitcoin) internal discussion group Reply number and click the link to join" was reworded to “Thank you for choosing our services.“ which likely wouldn’t result in smishing the mobile user.
The nothing prompt method did surprisingly well at identifying the suspicious aspects of the messages. It removed urgency and financial incentives, reworded the message to have proper English, and modify the messages to sound more like service notifications. It came up with creative ways to fool SmishX, such as swapping the word “payout” with “claim” to emphasize prior interaction with the SMS user. It reworded the messages to have a calmer tone, removing harsh wording, such as removing legal recourse sentences “...We’re aware that this number is not disconnected, you’ll be fined and sent to jail for minimum 6 years if no reply.” to “...We understand your concerns, and we are here to help. No action is required unless you wish to attend our informational meeting. No fine or legal action will be taken without your participation.” Questions arise with this type of modification: would it still phish users? Or would it render the message less effective in phishing users?
The modification strategies method suffered from using generic example names in rewording the smishing message, such as using “XYZ Company“ or “[Company Name]” instead of a specific company name. Using these obviously generic names tipped off SmishX to the smishing, as it would lack identifying information or fail the brand search. This is a limitation of the method in generating realistic messages, especially as it would likely fail to entice a user to click on the message when they saw generic names. This error could potentially be fixed by instructing the model to identify brands to spoof.
The nothing method suffered from adding and removing links similar to the other methods. It struggled with well-known brands not fooling SmishX, and it sometimes didn’t modify the message much from the original message, so it still flagged as smishing. Additionally, the nothing method would identify issues such as urgency, in the message as being suspicious, but it would only slightly modify the messages to remove urgency, and wouldn’t eliminate all of the urgency or threatening tone, so it still flagged as smishing.
The main error the SmishX explanation method suffered from was lack of full understanding all the suspicious aspects of the malicious message. The adversarial model with the SmishX explanation method used the explanation to modify relevant aspects of the message. However, the explanation often only revealed a partial view of what flagged in the message. For example, the message “ (CA) Grocery BFT#568008: Receive your assigned $289.00 now. Type ’A’ to transmit your grocery bonus to your selected institution.” was classified as smishing with explanation “This message is likely a phishing attempt. It talks about receiving a grocery bonus of $289, which is a common trick used to get people excited and trick them into responding. The urgent request to "Type A" feels suspicious and suggests it could be trying to collect your personal information, so it’s best not to reply or share any details.” The adversarial model modified the message to be “(CA) Grocery BFT#568008: Received your assigned amount. Please visit our website for more details. A Text Message : D”, following the SmishX explanation by removing monetary amounts and “Type A.” However, running the modified message through SmishX revealed more suspicious aspects of the message, with the explanation “The message you received seems a bit suspicious. It talks about an "assigned amount" and asks you to visit a website for more information, but it doesn’t clearly say who it is from or provide a real source. Since the language is vague and it lacks details about the sender, it’s best to be cautious and not respond or share any personal information.” This shortcoming reveals a strength of the SmishX system in that it doesn’t reveal all of the decisions used in the classification decision, which helps prevent bad actors from one-shotting their messages with the SmishX explanation.
Additionally, the SmishX explanation method also suffered from removing so many specific details from the smishing message that the message was flagged as unclear and suspicious. For example, a bitcoin smishing message to join a group chat “Dear. You are invited to join the (Bitcoin) internal discussion group. Reply number and click the link to join” was reworded to “We wanted to share some important updates with you and have included a link to our official blog for more details. If you need further information, feel free to reply to this message. We value your feedback.” SmishX found the modified message to be too generic and lacking in identifying context, increasing its suspicion level. Some of these errors could be remediated potentially by running the message multiple times through the adversarial model and SmishX to iteratively refine the messages, which could potentially increase the attack success rate.
A large limitation with our work is the small size of our dataset. Future work could focus on finding ways to collaborate with users and/or mobile networks to collect recent smishing messages reported by users. This carries privacy concerns, however, which require delicate handling to ensure privacy and consent of mobile device users. Additionally, the work we did would benefit from user studies to understand how well the adversarial messages are able to fool mobile users. This would yield insight into whether the generated messages are more or less believable than their original form.
Our work carries ethical concerns with focusing on adversarially training a model to fool smishing detection systems. Our work could potentially help malicious actors learn how to improve their attacks. However, the goal of this study was to assist the smishing detection field in proactively identifying smishing tactics. We are not releasing our adversarial model to the public. Additionally, we redacted any malicious links in this report. By illuminating vulnerabilities in current smishing detection systems, we can facilitate awareness and action to bolster the defense of detection systems.
The current smishing detection systems are very robust at identifying legitimate from smishing messages given the current datasets. The potential of exploiting user-friendly explanations of the classification was found to not be the vulnerability we hypothesized. Given the nothing method performed better than the SmishX explanations method, malicious actors have just as much of a likelihood of generating creative messages that fool current detection systems with no additional information than with using the smishing explanations. Thus, our results suggest the smishing classifications explanations can be used to teach users to look out for smishing messages and help encourage them not to click the malicious links or respond to the message.
Given the challenges we encountered with the lack of current malicious links, our work stresses the lack of high quality, current datasets. Future research should focus on improving the channel of obtaining high quality data, as the lack of current data severely hinders the research in the field. Malicious actors move swiftly with creating new malicious links, and website hosting providers take these links down, so many links are short-lived.
Additionally, we found many phishing messages from friends and family on their mobile devices, yet these messages are unavailable to the general research field. Furthermore, the website SmishTank is not maintained, so it hinders the progress in publicly sourcing smishing data. Without high-quality data, the field lacks the information to test the robustness of current detection systems. With more data, we could proactively identify ways malicious actors could deceive smishing detection systems.
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