It does not seem you feel the question is answered so I will try to elaborate over what I think seems to bother you.
Let $S_t = e^{(\mu -\sigma^2/2) t + \sigma W_t}$ be the stock price process and $B_t=e^{rt}$ be the risk free. The arbitrage you describe is then choosing a nice $\varepsilon >0$ and setting $\tilde{T}=\inf \{t>0 : (\mu -r -\sigma^2/2)t +\sigma W_t> \varepsilon\}$. Then one would have an "arbitrage" at $\tilde{T}$, as you say this will eventually happen which is true. In fact one even know the distribution of when your "arbitrage" will occur see
http://en.wikipedia.org/wiki/Inverse_Gaussian_distribution
which is unfortunately also the problem. Since the inverse Gaussian distribution has mass over entire $\mathbb{R}^+$ you will not be able to choose $T\in \mathbb{R}$ such that $P(T \geq \tilde{T})=1$, ergo you can not in this way find an arbitrage.
It is in fact very easy to find an equivalent martingale measure in this model formally implying that the model is arbitrage free.
It is a different issue whether your "mini arbitrage strategy" is a attractive feature of a model. It is as you say simply a consequence of having a model where volatility accumulates without bound together with no possibility of default.