In this delightfully “bad” dive into password hygiene, we scrape millions of leaked passwords for the first dictionary word they contain. The top ten words (love, baby, password…) barely scratch 5% of the total, and a whopping 21k words appear only once. We also compare happy vs. angry vocab. Turns out love trumps f**k by a healthy margin. The takeaway? Stick to random passphrases; dictionary words are a playground for attackers and a source of endless amusement for analysts.

In this delightfully “bad” foray into password cracking, we tally two‑ and three‑character combos from millions of leaked passwords and compare them to a subtitle‑derived English word list. Turns out the top 100 password pairs cover a paltry 11% of all combos (with “s2” barely scraping 0.15%), while the same slice of English captures a whopping 60%. Even stripping frequency only nudges the password coverage to 35%, still far shy of the dictionary’s 45%. The takeaway? Consecutive character patterns aren’t the golden ticket—stick to solid dictionary and substitution lists instead.

In this tongue‑in‑cheek post we dive deep—actually deeper than usual—into the world of malware string analysis by counting individual characters. After pulling roughly 500 malicious samples from theZoo and dasMalwerk and comparing them against a hefty collection of benign binaries, we discovered that a handful of seemingly innocuous characters (v, j, ;, , 4, q, 5, /) pop up more often in the bad guys’ code. By looking at raw counts and then normalising those counts by file size, we expose why naïve “character‑frequency” heuristics are both amusing and alarmingly unreliable. The piece is deliberately over‑the‑top, aiming to entertain seasoned security folks while reminding everyone that good malware hunting requires more nuance than a simple character checklist.