March 30, 2026
Bugs, bots, and backlash
Vulnerability Research Is Cooked
Bots will find the bugs — the internet’s split between doomsday and dumb hype
TLDR: A veteran predicts AI agents will soon spot serious software flaws on command, reshaping online security. Commenters split between hype fatigue, fears of a flood of junk bug reports swamping open‑source, and optimists saying teams can use the same bots to patch fast — a looming race that could affect everyone’s safety online.
A security veteran says AI code agents are about to turn hacking upside down: point a bot at your code and tell it “find me zero-days” (unknown security holes), and it will. Cue chaos in the comments. Skeptics like nitros point to curl getting flooded by AI‑generated junk reports and say, “we’ve seen this movie.” tomjakubowski takes it darker: even if the new bots find real flaws, the slop won’t stop — open‑source maintainers will drown in noise while trying to fix the real stuff.
On the other side, stavros asks the obvious: if bots can find holes, why not run them ourselves and patch everything? “Wouldn’t that make software, like, boringly safe?” Meanwhile, spr‑alex drops a reality check: the old fantasy that tools catch “nearly all bugs” never matched the messy truth — hinting that agents might widen the gap between well‑resourced teams and everyone else. And then there’s badgersnake, who delivers the meme of the thread: “Another boring AI hype article.”
Amid throwbacks to the ’90s and jokes about the mysterious “font gland,” the community riffs on the author’s catchphrase — “find me zero days” — like it’s the T‑shirt of the year. The stakes? If the author’s right, we’re in for AI interns that never sleep and an internet patch race; if he’s wrong, it’s just more robo‑spam and maintainer burnout.
Key Points
- •The article argues that AI coding agents will soon automate a large portion of high-impact vulnerability research, enabling rapid zero-day discovery across codebases.
- •It claims LLMs already encode extensive knowledge of software relationships and known bug classes, aiding pattern-matching and exploitability analysis.
- •Historical examples highlight that vulnerabilities often reside in complex, input-heavy components (e.g., font rendering, Unicode shaping), not just obvious security modules.
- •The piece suggests many systems have been protected by limited expert attention as much as by mitigations, leaving underanalyzed targets vulnerable.
- •Exploit outcomes are testable, and agents can iterate indefinitely, making exploitation research an ideal task for LLM-driven agents.