# SpearBot: Leveraging Large Language Models in a Generative-Critique Framework for Spear-Phishing Email Generation

arXiv: https://arxiv.org/abs/2412.11109 · HTML: https://mlsanity.com/paper/2412.11109/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities](https://mlsanity.com/paper/2603.25056/) (2026)
- [PiMRef: Deducing Ever-evolving Spear-phishing Emails with Knowledge Base Invariants](https://mlsanity.com/paper/2507.15393/) (2025)
- [Trustworthiness Calibration Framework for Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2511.04728/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly capable, aiding in tasks such as content generation, yet they also pose risks, particularly in generating harmful spear-phishing emails. These emails, crafted to entice clicks on malicious URLs, threaten personal information security. This paper proposes an adversarial framework, SpearBot, which utilizes LLMs to generate spear-phishing emails with various phishing strategies. Through specifically crafted jailbreak prompts, SpearBot circumvents security policies and introduces other LLM instances as critics. When a phishing email is identified by the critic, SpearBot refines the generated email based on the critique feedback until it can no longer be recognized as phishing, thereby enhancing its deceptive quality. To evaluate the effectiveness of SpearBot, we implement various machine-based defenders and assess how well the phishing emails generated could deceive them. Results show these emails often evade detection to a large extent, underscoring their deceptive quality. Additionally, human evaluations of the emails' readability and deception are conducted through questionnaires, confirming their convincing nature and the significant potential harm of the generated phishing emails.