SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System
- URL: http://arxiv.org/abs/2601.13581v1
- Date: Tue, 20 Jan 2026 04:11:00 GMT
- Title: SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System
- Authors: Heedou Kim, Changsik Kim, Sanghwa Shin, Jaewoo Kang,
- Abstract summary: Social engineering scams increasingly employ personalized, multi-turn deception.<n>While Large Language Models (LLMs) show promise in identifying deception, their cognitive assistance potential remains underexplored.<n>We propose ScriptMind, an integrated framework for LLM-based scam detection that bridges automated reasoning and human cognition.
- Score: 14.704193787708872
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social engineering scams increasingly employ personalized, multi-turn deception, exposing the limits of traditional detection methods. While Large Language Models (LLMs) show promise in identifying deception, their cognitive assistance potential remains underexplored. We propose ScriptMind, an integrated framework for LLM-based scam detection that bridges automated reasoning and human cognition. It comprises three components: the Crime Script Inference Task (CSIT) for scam reasoning, the Crime Script-Aware Inference Dataset (CSID) for fine-tuning small LLMs, and the Cognitive Simulation-based Evaluation of Social Engineering Defense (CSED) for assessing real-time cognitive impact. Using 571 Korean phone scam cases, we built 22,712 structured scammer-sequence training instances. Experimental results show that the 11B small LLM fine-tuned with ScriptMind outperformed GPT-4o by 13%, achieving superior performance over commercial models in detection accuracy, false-positive reduction, scammer utterance prediction, and rationale quality. Moreover, in phone scam simulation experiments, it significantly enhanced and sustained users' suspicion levels, improving their cognitive awareness of scams. ScriptMind represents a step toward human-centered, cognitively adaptive LLMs for scam defense.
Related papers
- FraudShield: Knowledge Graph Empowered Defense for LLMs against Fraud Attacks [42.60338754083519]
Large language models (LLMs) have been widely integrated into critical automated processes.<n>They are susceptible to manipulation by fraudulent information, which can lead to harmful outcomes.<n>We introduce FraudShield, a framework designed to protect LLMs from fraudulent content by leveraging a comprehensive analysis of fraud tactics.
arXiv Detail & Related papers (2026-01-30T02:48:52Z) - Evaluating & Reducing Deceptive Dialogue From Language Models with Multi-turn RL [64.3268313484078]
Large Language Models (LLMs) interact with millions of people worldwide in applications such as customer support, education and healthcare.<n>Their ability to produce deceptive outputs, whether intentionally or inadvertently, poses significant safety concerns.<n>We investigate the extent to which LLMs engage in deception within dialogue, and propose the belief misalignment metric to quantify deception.
arXiv Detail & Related papers (2025-10-16T05:29:36Z) - Send to which account? Evaluation of an LLM-based Scambaiting System [0.0]
This paper presents the first large-scale, real-world evaluation of a scambaiting system powered by large language models (LLMs)<n>Over a five-month deployment, the system initiated over 2,600 engagements with actual scammers, resulting in a dataset of more than 18,700 messages.<n>It achieved an Information Disclosure Rate (IDR) of approximately 32%, successfully extracting sensitive financial information such as mule accounts.
arXiv Detail & Related papers (2025-09-10T11:08:52Z) - Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts [79.1081247754018]
Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks.<n>We propose a framework based on Contact Searching Questions(CSQ) to quantify the likelihood of deception.
arXiv Detail & Related papers (2025-08-08T14:46:35Z) - Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity? [51.96049148869987]
Large Language Models (LLMs) offer new tools and challenges for the security of computer systems.<n>We investigate whether classical game-theoretic frameworks can effectively capture the behaviours of LLM-driven actors and bots.
arXiv Detail & Related papers (2025-08-04T08:57:14Z) - Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance [16.9071617169937]
This paper investigates the vulnerabilities of Large Language Models (LLMs) when facing adversarial scam messages for the task of scam detection.<n>We created a comprehensive dataset with fine-grained labels of scam messages, including both original and adversarial scam messages.<n>Our analysis showed how adversarial examples took advantage of vulnerabilities of a LLM, leading to high misclassification rate.
arXiv Detail & Related papers (2024-12-01T00:13:28Z) - Can LLMs be Scammed? A Baseline Measurement Study [0.0873811641236639]
Large Language Models' (LLMs') vulnerability to a variety of scam tactics is systematically assessed.
First, we incorporate 37 well-defined base scam scenarios reflecting the diverse scam categories identified by FINRA taxonomy.
Second, we utilize representative proprietary (GPT-3.5, GPT-4) and open-source (Llama) models to analyze their performance in scam detection.
Third, our research provides critical insights into which scam tactics are most effective against LLMs and how varying persona traits and persuasive techniques influence these vulnerabilities.
arXiv Detail & Related papers (2024-10-14T05:22:27Z) - Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making.
Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations.
Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
arXiv Detail & Related papers (2024-08-17T11:49:53Z) - SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal [64.9938658716425]
SORRY-Bench is a proposed benchmark for evaluating large language models' (LLMs) ability to recognize and reject unsafe user requests.<n>First, existing methods often use coarse-grained taxonomy of unsafe topics, and are over-representing some fine-grained topics.<n>Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations.
arXiv Detail & Related papers (2024-06-20T17:56:07Z) - Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes [61.916827858666906]
Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer.
To reduce harm and misuse, efforts have been made to align these LLMs to human values using advanced training techniques such as Reinforcement Learning from Human Feedback.
Recent studies have highlighted the vulnerability of LLMs to adversarial jailbreak attempts aiming at subverting the embedded safety guardrails.
This paper proposes a method called Gradient Cuff to detect jailbreak attempts.
arXiv Detail & Related papers (2024-03-01T03:29:54Z) - Detecting Scams Using Large Language Models [19.7220607313348]
Large Language Models (LLMs) have gained prominence in various applications, including security.
This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity.
We propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams.
arXiv Detail & Related papers (2024-02-05T16:13:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.