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, Gemma2-9B, and Phi-4-14B. We further evaluate four defense mechanisms-keyword blacklisting, self-reminder prompting, paraphrase-based sanitization, and perplexity-based filtering against two high-ASR models (Mistral-7B and Llama-3.1-8B) on a 20-prompt subset. Across 250 prompts per model, obfuscation attacks had the highest mean attack success rate (ASR) of any family (61.4%, under a rule-based judge with known defects that has not been validated against human labels), were the highest or tied-highest family for every model and achieved nonzero success on all seven. We find that model size does not significantly predict adversarial robustness (Spearman \r{ho} = -0.63, p = 0.13, n = 7), and that the two Llama-3 generations differ markedly (16.8% and 40.8% ASR). Self-reminder prompting gave the largest reduction, cutting ASR from 50% to 10% on Mistral-7B and from 45% to 5% on Llama-3.1 8B (40 percentage points on each model, against matched no-defense runs). Our code and datasets are publicly available at https://github.com/Mdniloykhan/jailbreak-eval-7ll.