I’ll be honest here. I don’t feel the same excitement about iOS exploitation anymore like it was during the Lookout days in 2016 with the NSO exploit chains. Today you can run commodity AI like DeepSeek, you run an AI swarm into a problem, it might cost you $3000 via API or nothing if you own some DGX, and you will find vulns. You will find the exact same vulns with $30,000 spent on openAI. I think the only people left finding something are the ones building special fuzzing harnesses steered by an LLM to explore paths that weren’t explored before (there are already open source projects for this). And of course the upstream bugs that Apple keep introducing after each release. Knowing that with an LLM you can reverse engineer anything end to end, plus you have access to better harness now and you can 1-shot your own: Vulnerability research only constraints looks to be money (granted you have the skills already) If you are rich you will find vulns. If you are poor it means you missed all the previous cycles to make money on VR and you have a skill problem, or maybe you just started. If you just started it will be exponentially hard to find anything useful going forward the bar is extremely high and most importantly exponentially expensive. Bad news for threat actors in cybercrime. That leaves us with nation states or well funded firms / having resources to find new bugs. If you are independent VR guy I wish you good luck.

I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem. As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)! I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work. It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results? 88ms => 1.5ms 150K allocs => ~500 allocs Incredible right? Nope. My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path. This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput. The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity. Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.

Tomorrow, I will drop Chrome exploit code showing how an attacker can execute arbitrary Javascript within the context of a domain they control.

Fork your dependencies, trim them to only your use case, never update unless it breaks for your users. I’ve been vocal about this for 10+ years. I’ve always said that updating is way riskier than latent bugs (which can be tracked and CVEs monitored). If you are updating a dependency, it’s on you to analyze every single commit in the full transitive set of dependencies. If you dont see anything compelling, dont update! I remember at HashiCorp once in awhile an engineer would try to update a dep or replace a DIY lib with an external one and id always ask “show me the commit we need.” Dont update for the sake of it. Feeling pretty swell about this mentality with all the supply chain attacks happening.

Cloudflare's security team spent the last few weeks testing Anthropic's Mythos against fifty of our own repositories. What we learned about offensive AI, why faster patching is the wrong reaction, and what the architecture around vulnerabilities has to look like next. cfl.re/49BRUqW

You have no experience. You’ve never started a company. You’ve never had a full time job. Nike is going to kill you. You’re a kid. You don’t have technical skills. You shouldn’t build hardware. Apple is going to kill you. You can’t build hardware. You can’t measure heart rate non-invasively. Athletes don’t care about recovery. Under Armour is going to kill you. It won’t be accurate. You don’t listen. You’re an ineffective leader. You can’t recruit great talent. You’re going to have to pay every athlete. You can’t measure sleep non-invasively. It’s too expensive to research. Athletes are a small market. The product costs too much to make. The product costs too much to sell. Your valuation is too high. Consumers aren’t going to want it. Hardware is too hard. You should measure steps. Fitbit is going to kill you. You can’t build a marketing engine. You can’t raise enough money. You need a real CEO. Google is going to kill you. You can’t be a subscription. You can’t build a brand. You can’t do consumer in Boston. Your valuation is too high. You shouldn’t make accessories. You shouldn’t make apparel. Lululemon is going to kill you. You can’t predict Covid. Stay in your niche. You are going to run out of money. You can’t build a health platform. Amazon is going to kill you. You can’t measure blood pressure. You can’t get medical approvals. The market is too small. You don’t understand AI. The market is too competitive. It won’t work internationally. The supply chain is too complicated. You can’t build an AI. You can’t raise enough money. It’s too competitive. Healthcare isn’t going to want it. … Just keep going ✌️

We evaluated @Tenzai_Labs AI hacker across six major CTF competitions designed for humans. Result: Top 1% performance, outperforming 125,000+ human hackers across different domains - web hacking, ai hacking, low level system hacking. We wanted to see what @Tenzai_Labs's hacking agent is really capable of in the most complicated and competitive environments, where to excel, one needs to solve increasingly difficult challenges. The results we achieved surprised even me. This is incredible evidence of what AI agents with the right harness can do and I expect it to only get better from now. blog.tenzai.com/tenzais-ai-h…