It looks like this week’s theme is complexity...

This is not directly security-related, but I think there is a strong security lesson here. One of the problems with AI-generated code is not just that it may introduce bugs. It also tends to introduce surface area: extra dependencies, extra abstractions, extra wrappers, and extra “future-proofing” for a future that will probably never happen. Ponytail is interesting because it attacks this problem from the opposite direction. It is a small Rust tool focused on reducing code bloat. I think this is worth stealing for security code review workflows. Before asking an agent to find vulnerabilities, run something like ponytail first and reduce the amount of code that needs to be trusted. Less code is not automatically secure code, but unnecessary code is almost always unnecessary attack surface. DietrichGebert / ponytail.

curl is doing something I suspect more critical open-source projects will have to think about: for July 2026, they will not accept or process vulnerability reports. It is easy to react with “but attackers will not take a holiday.” True. But maintainers are not machines either. The uncomfortable part is that AI makes it cheaper to produce vulnerability reports, but it does not make it equally cheap for a maintainer to verify them, understand the impact, write the fix, write the advisory, coordinate the release, and carry the responsibility if they get it wrong. This feels like another version of XKCD 2347: the internet depends on small groups of people doing high-pressure work for everyone else. AI may increase the pressure before it reduces it. curl summer of bliss.

This is the kind of research I really like because the interesting part is not one magical bug. It is the chain. The exploit path combines several things that, in isolation, may look manageable: key reuse across contexts, a database backup/import feature, a race condition, CAPTCHA automation, and finally an admin-only LFI that becomes RCE. The important lesson is that the application did not fail in one place. It failed in the assumptions between places. This is also why good code review is hard to automate with a checklist. The dangerous question is not only “is this function vulnerable?” It is “where else is this token accepted?”, “what happens while this table is being recreated?”, “can a value that was meant to be a username later become part of a session structure?”, and “which admin-only bug stops being admin-only once another part of the system breaks?” That is where a lot of high-impact security research lives: between components. Chaining Security Bugs in Discuz! X5.0: from Race Condition to Pre-Auth RCE.

This blog post is a great starting point for anyone wondering how to start building a hackbot for bug bounty or general AppSec testing. The useful part is that it does not start with a vague “use AI to hack things” idea. It walks through a concrete setup: giving the agent browser access, creating a dedicated auth testing sub-agent, and scoping that sub-agent around a specific class of testing. That is probably the right way to begin. Not one giant agent that tries to test everything, but smaller agents with narrower responsibilities. Where it gets interesting is what happens after this first version works. Once you have an agent that can interact with an application, you quickly run into a lot of real engineering problems: making sure it only tests the right target, preventing it from following links to another domain, handling shared state between accounts, avoiding rate limits, dealing with 2FA, test inboxes, password reset flows, account lockouts, and making sure two agents do not break each other’s sessions. That is why I think this is a good starting point rather than a complete solution. The first step is getting an agent to do useful work. The next step is building all the boring but critical infrastructure around it: scope enforcement, account isolation, logging, reproducibility, and safety checks. This is where AppSec automation is going to get interesting. The model is only one part of the system. The real work is in the harness around it. Building a Hackbot for Bug Bounties — Auth Testing Subagent Setup.