Systematic Evaluation of Adversarial Jailbreaking Attacks and Defenses Across Seven Large Language Models
A systematic evaluation across seven LLMs and five jailbreak attack families quantifies which defences hold — providing a practical benchmark for enterprises deploying models in safety-critical contexts.
Summary written by editorial AI · Source link below
arXiv:2610.39509v3 Announce Type: replace Abstract: Large language models (LLMs) deployed in safety-critical settings must resist adversarial inputs designed to elicit harmful outputs. We present a systematic empirical evaluation of five adversarial jailbreaking attack families-direct requests, roleplay-based jailbreaking, hypothetical framing, instruction override, and obfuscation-across seven open-weight LLMs served locally: Llama-3-8B, Llama-3.1-8B, Llama-3.2-3B, Mistral-7B, Qwen2.5-7B, Gemm
Editorial Analysis
Enterprises deploying LLMs in regulated or safety-critical environments need evidence-based defence selection; this benchmark provides cross-model comparative data.
Map the evaluated attack families to your LLM deployment scenarios and verify that your chosen defences align with the most effective strategies identified.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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