Evaluating Adversarial Vulnerabilities in Modern Large Language Models

• URL: http://arxiv.org/abs/2511.17666v1
• Date: Fri, 21 Nov 2025 01:23:56 GMT
• Title: Evaluating Adversarial Vulnerabilities in Modern Large Language Models
• Authors: Tom Perel,
• Abstract summary: This paper presents a comparative analysis of the susceptibility to jailbreak attacks for two leading publicly available Large Language Models (LLMs)<n>The research utilized two main bypass strategies:'self-bypass' and 'cross-bypass'<n>The success of the attack was determined by the generation of disallowed content, with successful jailbreaks assigned a severity score.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent boom and rapid integration of Large Language Models (LLMs) into a wide range of applications warrants a deeper understanding of their security and safety vulnerabilities. This paper presents a comparative analysis of the susceptibility to jailbreak attacks for two leading publicly available LLMs, Google's Gemini 2.5 Flash and OpenAI's GPT-4 (specifically the GPT-4o mini model accessible in the free tier). The research utilized two main bypass strategies: 'self-bypass', where models were prompted to circumvent their own safety protocols, and 'cross-bypass', where one model generated adversarial prompts to exploit vulnerabilities in the other. Four attack methods were employed - direct injection, role-playing, context manipulation, and obfuscation - to generate five distinct categories of unsafe content: hate speech, illegal activities, malicious code, dangerous content, and misinformation. The success of the attack was determined by the generation of disallowed content, with successful jailbreaks assigned a severity score. The findings indicate a disparity in jailbreak susceptibility between 2.5 Flash and GPT-4, suggesting variations in their safety implementations or architectural design. Cross-bypass attacks were particularly effective, indicating that an ample amount of vulnerabilities exist in the underlying transformer architecture. This research contributes a scalable framework for automated AI red-teaming and provides data-driven insights into the current state of LLM safety, underscoring the complex challenge of balancing model capabilities with robust safety mechanisms.

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