GuardPhish: Securing Open-Source LLMs from Phishing Abuse

• URL: http://arxiv.org/abs/2604.17313v1
• Date: Sun, 19 Apr 2026 08:02:57 GMT
• Title: GuardPhish: Securing Open-Source LLMs from Phishing Abuse
• Abstract summary: Open-source Large Language Models (LLMs) in offline and enterprise environments have introduced a largely unexamined security risk.<n>We investigate this vulnerability through GuardPhish, a large scale multi-vector phishing prompt dataset.<n>Using a deterministic five model ensemble for labeling, we achieve near perfect inter model agreement.
• Score: 0.7009487789080343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid adoption of open-source Large Language Models (LLMs) in offline and enterprise environments has introduced a largely unexamined security risk like susceptibility to adversarial phishing prompts under static safety configurations. In this work, we systematically investigate this vulnerability through GuardPhish, a large scale multi-vector phishing prompt dataset comprising 70,015 samples spanning web, email, SMS, and voice attack scenarios derived from real world campaigns. Using a deterministic five model ensemble for labeling, we achieve near perfect inter model agreement (Fleiss kappa = 0.9141), with residual disagreements resolved through expert adjudication. By evaluating eight open-source LLMs under fully offline inference conditions, we uncover a substantial enforcement gap like models that correctly identify phishing intent with detection rates up to 96% nevertheless generate actionable phishing content from identical prompts, with attack success rates reaching 98.5% in voice-based scenarios. These findings demonstrate that intent classification alone does not guarantee generative refusal in the absence of dynamic guardrails. To mitigate this risk, we train transformer based classifiers on GuardPhish, achieving up to 98.27% accuracy as modular pre-generation filters deployable without modifying the underlying generative model. Our results highlight a critical weakness in current open-source LLM deployments and provide a reproducible foundation for strengthening defenses against phishing and social engineering attacks.

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