How about US Census Bureau as bruteforce dictionary ?
I bet you never knew that one reliable resource of dictionary based password attacks is the US Census bureau.
http://www.census.gov/genealogy/names/names_files.html
For example, below are 3 lists of the most common names registered in US :
| First ten last names entries | |||
| name | freq | cum.freq | rank |
| SMITH | 1.006 | 1.006 | 1 |
| JOHNSON | 0.810 | 1.816 | 2 |
| WILLIAMS | 0.699 | 2.515 | 3 |
| JONES | 0.621 | 3.136 | 4 |
| BROWN | 0.621 | 3.757 | 5 |
| DAVIS | 0.480 | 4.237 | 6 |
| MILLER | 0.424 | 4.660 | 7 |
| WILSON | 0.339 | 5.000 | 8 |
| MOORE | 0.312 | 5.312 | 9 |
| TAYLOR | 0.311 | 5.623 | 10 |
| First ten female entries | |||
| name | freq | cum.freq | rank |
| MARY | 2.629 | 2.629 | 1 |
| PATRICIA | 1.073 | 3.702 | 2 |
| LINDA | 1.035 | 4.736 | 3 |
| BARBARA | 0.980 | 5.716 | 4 |
| ELIZABETH | 0.937 | 6.653 | 5 |
| JENNIFER | 0.932 | 7.586 | 6 |
| MARIA | 0.828 | 8.414 | 7 |
| SUSAN | 0.794 | 9.209 | 8 |
| MARGARET | 0.768 | 9.976 | 9 |
| DOROTHY | 0.727 | 10.703 | 10 |
| First ten Male entries | |||
| name | freq | cum.freq | rank |
| JAMES | 3.318 | 3.318 | 1 |
| JOHN | 3.271 | 6.589 | 2 |
| ROBERT | 3.143 | 9.732 | 3 |
| MICHAEL | 2.629 | 12.361 | 4 |
| WILLIAM | 2.451 | 14.812 | 5 |
| DAVID | 2.363 | 17.176 | 6 |
| RICHARD | 1.703 | 18.878 | 7 |
| CHARLES | 1.523 | 20.401 | 8 |
| JOSEPH | 1.404 | 21.805 | 9 |
| THOMAS | 1.380 | 23.185 | 10 |
so, how high is your name on the list ?
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