The package still comes with a vignette describing both DieHarder and the RDieHarder package. And because pictures speak louder than a thousand (blogged) words, here is the first chart from the vignette:
ran0function. The histogram illustrating the distribution of test scores is somewhat uneven. An ideal (and asymptotic) outcode is a uniform distribution of p-values from the test. The empirical cumulative distribution function (ECDF) below indicates a somewhat pronounced departure from the diagonal. Informally speaking, this is what the (Kuiper-)Kolmogorov-Smirnov test quantifies, and we see (in the text in the chart) that the null of can be rejected an conventional levels. Based on this example (which had a short run-time with few samples) we would indeed mistrust this (known bad) RNG.
On the right, we have a more recent and trusted RNG, the well-known Mersenne Twister. The ten histogram buckets are all closer to the expected value of one-tenth, the estimated density is closer to flat, the ECDF is closer to the diagonal and the tests don't reject---so no reason to mistrust this RNG based on this test alone.