LLM-Based Scambaiting System

• LLM-Based Scambaiting System is an automated framework that leverages large language models to detect, engage, and disrupt scam communications while extracting actionable threat intelligence.
• The system employs a modular architecture with adaptive dialogue management, context-aware prompt generation, and human-in-the-loop oversight to ensure effective and safe operations.
• Performance metrics such as a 32% information disclosure rate and 70% human acceptance rate demonstrate its operational effectiveness and the need for robust adversarial safeguards.

A LLM-based scambaiting system is an automated or semi-automated framework that utilizes LLMs to detect, engage, and manipulate scammer communications for the purpose of extracting threat intelligence, delaying or disrupting scams, or supporting broader cybersecurity and law enforcement objectives. Such systems synthesize advances in natural language understanding, conversational AI, security engineering, and human-in-the-loop orchestration. Core properties include high degrees of adversarial robustness, adaptive engagement strategies, actionable intelligence extraction, and operational safeguards against both scams and model misuse.

1. System Architecture and Operational Workflow

LLM-based scambaiting systems are typically designed with modular architectures that support end-to-end detection, engagement, and intelligence extraction.

• Input and Engagement Channels: Systems ingest scam-attributed communications via email, phone transcripts, or digital messaging platforms. Email honeypots and conversational portals are employed to seed engagements with known or suspected scammers (Siadati et al., 10 Sep 2025).
• Prompt Generation and Context Management: Each system instance generates prompts designed to simulate plausible victim personas, adopting contextually consistent roles (e.g., a local business owner or retiree). Prompt templates are dynamically adapted to maintain authenticity, leverage context history, and avoid detection (Basta et al., 10 Mar 2025, Siadati et al., 10 Sep 2025).
• LLM-Driven Dialogue Engine: The LLM (e.g., ChatGPT, GPT-4, Deepseek) is responsible for producing responses, simulating both initial outreach and ongoing interaction. Architectures distinguish between single-prompt (turn-by-turn) and multi-turn, memory-driven planning, with the latter enabling nuanced, adaptive conversations (Badhe, 8 Aug 2025).
• Human-in-the-Loop (HITL) and Review: In certain deployments, generated responses flow through a human operator who may approve or edit outputs for tone, safety, or strategic adjustment. In fully autonomous scenarios, a pre-deployment review and post-hoc monitoring protocol is used (Siadati et al., 10 Sep 2025).
• Intelligence Harvesting and Storage: Engagements are archived in a Message DB with metadata, supporting downstream analysis (e.g., bank account extraction, scam typology classification).
• Guardrails and Safety Moderation: Guard models (e.g., LlamaGuard, MD-Judge) assess outputs for privacy, safety, and regulatory compliance, preventing leakage of sensitive information or unintended escalation (Hossain et al., 4 Sep 2025).

Component	Functionality	Representative System/Paper
Prompt Generator	Persona design, context adaptation	(Basta et al., 10 Mar 2025, Siadati et al., 10 Sep 2025)
LLM Dialogue Engine	Response generation, adaptive planning	(Siadati et al., 10 Sep 2025, Badhe, 8 Aug 2025)
Human Review (HITL)	Output curation, real-time adjustment	(Siadati et al., 10 Sep 2025)
Message Database	Logging, analytics, engagement metadata	(Siadati et al., 10 Sep 2025)
Guard Model	Safety filtering, privacy moderation	(Hossain et al., 4 Sep 2025)

These architectural components are orchestrated to enable both robust scam detection and productive information-gathering through prolonged scammer interaction.

2. Detection, Engagement, and Strategy Design

Scam Detection

LLMs are fine-tuned or prompted to evaluate incoming content for scam traits such as implausible offers, urgency, linguistic markers, sender anomalies, and requests for personal or financial information. High-performing configurations utilize standardized prompts and scoring metrics, aggregating multiple criteria (e.g., sender authenticity, urgency, grammar) into a confidence score (Patel et al., 23 Apr 2024).

Engagement Strategies

Key engagement methodologies include:

• Persona Consistency and Roleplay: Generating responses that remain consistent with preselected victim characteristics to maintain scammer interest and extract deeper information (Basta et al., 10 Mar 2025).
• Adaptive Dialogue Management: Using persistent memory and multi-turn dialogue history to tailor each subsequent response based on the scammer’s replies, including delay tactics, counter-questions, and feigned confusion (Badhe, 8 Aug 2025).
• Utility-Driven Response Optimization: Implementing reward-punishment utility functions to balance engagement quality (e.g., measured with DialogRPT scores, lexical diversity) and harm minimization (e.g., penalizing PII disclosure) (Hossain et al., 4 Sep 2025).

A typical engagement proceeds through an initial seeding, iterative message exchange (with LLM/HITL oversight), and an extraction phase, aiming to induce voluntary threat intelligence disclosures (e.g., financial accounts).

3. Evaluation Metrics and Performance

Operational success is quantified via several key metrics:

• Information Disclosure Rate (IDR):

$$\mathrm{IDR} = \frac{\text{Number of Successful Engagements}}{\text{Total Engagements}} \times 100$$

For example, over a five-month deployment, a rate of approximately 32% was achieved (Siadati et al., 10 Sep 2025).

• Information Disclosure Speed (IDS):

Assessed via mean message turns (e.g., 10.3 turns) and elapsed days (e.g., 7.4 days) until disclosure.

• Human Acceptance Rate (HAR):

Percentage of LLM-generated responses sent without modification, with typical rates near 70% (Siadati et al., 10 Sep 2025).

• Engagement and Takeoff Ratio:

The system’s ability to initiate bidirectional engagements—about 48.7% replied to initial outreach (Siadati et al., 10 Sep 2025).

• Conversational Quality:

Perplexity (fluency), engagement ($\approx$0.80), and relevance ($\approx$0.74) (Hossain et al., 4 Sep 2025).

Metric	Description	Observed Value (Ex.)
IDR	Sensitive info extracted per engagement	32%
HAR	LLM reply acceptance (unmodified)	70%
Takeoff Ratio	Outreach→bidirectional dialogue	48.7%
IDS (turns)	Avg. messages to critical disclosure	10.3

These metrics are used as both operational benchmarks and as feedback signals for further tuning of dialogue and response strategies.

4. Adversarial Robustness, Guardrails, and Privacy

LLM-based scambaiting systems must address several adversarial and safety challenges:

• Adversarial Prompting and Evasion: Scammers and advanced agents can employ linguistic obfuscation, noise injection, or prompt decomposition to evade detection (Li et al., 22 Jul 2025, Chang et al., 1 Dec 2024). Even minimal changes in formality or order can significantly reduce LLM detection rates.
• Guard Models: Specialized models (e.g., LlamaGuard, MD-Judge) are deployed to analyze both incoming and outgoing messages for risk, filtering any candidate with predicted harm (e.g., $H(g_i) > \delta$, where $\delta$ is a hard safety threshold) (Hossain et al., 4 Sep 2025).
• Federated Learning and Privacy: To continually adapt to evolving scams without sacrificing user privacy, federated learning approaches (e.g., FedAvg aggregation) are used, ensuring decentralized model updates. Differential privacy is applied to gradient updates, maintaining strong privacy without substantial drops in engagement or detection performance (Hossain et al., 4 Sep 2025).
• Multi-Turn Moderation: Model refusal mechanisms and safety filters originally designed for single-prompt moderation are insufficient. Multi-turn intent tracking is required to recognize distributed harmful objectives—a practical consideration underscored by demonstrations that current safety guardrails are ineffective against agent-based, staged deception (Badhe, 8 Aug 2025).

5. Limitations, Deployment Challenges, and Mitigation Strategies

Despite strong results, deployments encounter several operational and technical limits:

• Engagement Takeoff and Message Design: Only about half of outreach attempts elicit scammer responses. Message design—specifically concise, contextually believable, and persona-aligned openers—significantly impacts takeoff rates (Siadati et al., 10 Sep 2025).
• Response Latency and Endurance: Scammer latency varies widely. Sustained multi-turn engagement (~12.2 messages, 10.9 days) correlates with successful information extraction but introduces operational uncertainty.
• Balance Between Automation and Human Oversight: While fully autonomous LLM systems perform robustly (HAR $\approx$70%), human-in-the-loop systems facilitate faster disclosures and more natural dialogue, but at the expense of added response latency (Siadati et al., 10 Sep 2025).
• Template Diversity and Adaptivity: Overreliance on templated turns reduces message freshness (as measured by n-gram diversity) and may increase the risk of scammer detection. Approaches using adaptive, context-aware generation partially mitigate this risk (Siadati et al., 10 Sep 2025).
• Safety-Engagement Tradeoff: Stricter moderation (low $\delta$) reduces PII leakage, but can excessively dampen dialogue, while relaxed moderation improves threat intelligence extraction but may pose privacy risks (Hossain et al., 4 Sep 2025).

6. Future Development and Research Directions

Refinements and open research challenges highlighted for LLM-based scambaiting systems include:

• Advanced Dialogue Management: Modeling engagement state transitions to optimize for early information extraction and efficient resource allocation (Siadati et al., 10 Sep 2025).
• Real-Time and Multimodal Interaction: Integration with speech-to-text and text-to-speech pipelines for voice call scambaiting, with real-time adaptive notification systems to maximize user trust and minimize disruption (Shen et al., 6 Feb 2025, Badhe, 8 Aug 2025).
• Ensemble and Multi-Agent Architectures: Building modular systems with specialized agents (for URL, content, semantics, brand detection) coordinated through structured debate (as in PhishDebate), yielding higher interpretability and precision (Li et al., 18 Jun 2025).
• Explainability and Transparency: Generating justification modules to support decision rationale, facilitating user trust and forensic audits (Sehwag et al., 14 Oct 2024).
• Continuous Adversarial Testing and Adaptation: Routine adversarial probing (“red teaming”), benchmark updates, and integrated concept drift detection to ensure system resilience against evolving scam tactics (Chang et al., 1 Dec 2024, Li et al., 22 Jul 2025, Senol et al., 7 May 2025).
• Global/Multilingual Capabilities: Expanding benchmarks and fine-tuning for effective operation in non-English languages and local scam patterns, addressing observed performance gaps (Yang et al., 18 Feb 2025).
• Collaboration with Domain Experts: Sustained cybersecurity collaboration to integrate domain knowledge, verify threat intelligence extraction, and stay ahead of rapidly evolving fraud methodologies (Jiang, 5 Feb 2024).

7. Significance and Impact

LLM-based scambaiting systems represent a paradigm shift in defensive cyber operations, enabling proactive, scalable, and adaptive engagement with scammers. With evidence of operational efficacy—such as extracting actionable bank mule accounts or cryptocurrency wallets—these frameworks not only impede scammer activity in real time but also provide rich intelligence for financial disruption and law enforcement action (Siadati et al., 10 Sep 2025). However, the system’s unique capability to simulate lifelike, adaptive conversation necessitates stringent safeguards, continuous monitoring, and a nuanced balance between user privacy and intelligence gathering. As scammers increasingly exploit generative AI, LLM-based scambaiting systems—when rigorously designed and ethically deployed—constitute a critical component of contemporary cybersecurity defense-in-depth strategies.