What does this research mean for the field?

An automated prompt-mutation framework can successfully expose jailbreak vulnerabilities and generate concrete attack scenarios across various large language models, revealing distinct differences in how these models respond to malicious inputs. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to evaluate the security vulnerabilities of large language models through penetration testing.

May 30, 2026Open Access

Pentesting LLM Models With an Automated Framework

Key Points

This research aims to evaluate the security vulnerabilities of large language models through penetration testing.
Developed an automated framework for prompt mutation to test LLMs for jailbreak vulnerabilities.
Conducted experiments across multiple LLM models including GPT-3.5 turbo, GPT-4.1, and GPT-5.0.
Analyzed different model responses to mutated prompts that simulate attack scenarios.
Identified concrete attack scenarios generated by mutated prompts in various LLM models.
Revealed significant differences in how models responded to malicious inputs, indicating varied security postures.
Demonstrated the utility of the framework in assessing both offensive and defensive capabilities in AI-driven cybersecurity.

Abstract

Artificial intelligence (AI) has become an essential tool in modern cybersecurity, enabling faster and more accurate detection, prevention, and response to threats. Within this landscape, large language models (LLMs) have emerged as versatile systems capable of generating code, providing technical guidance, and automating complex tasks. However, LLMs also introduce new security challenges, as they can be manipulated through prompt engineering and jailbreaking to perform malicious actions, potentially lowering the barrier for cyberattacks. This article investigates the risks and opportunities of LLMs using penetration testing, both as tools for ethical hacking and as potential targets themselves. We present an automatic framework that mutates prompts to test for jailbreak vulnerabilities across multiple LLM models, including GPT‐3.5 turbo, GPT‐4.1, and GPT‐5.0. Our experiments demonstrate how mutated prompts can generate concrete attack scenarios and reveal differences in how various models respond to malicious inputs. By analyzing the effectiveness and limitations of these techniques, this work contributes to a deeper understanding of LLM security, providing insights for both offensive and defensive applications in AI‐driven cybersecurity.

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