TestMachine

Azimuth was built to explore smart contract state space and find real exploits. Watch how it goes from random probing to converging on a working attack path. The output is a proof-of-concept exploit, not just a risk score. This is what happens when you apply reinforcement learning to security research. Learn more: testmachine.ai/products/azimu…

@asen_sec Cooking🧑‍🍳

While everyone races to brand themselves AI security experts, the people who'll actually own this category are heads-down, building. The category gets decided before most realize it started.

Users lose trust when they get rugged. They lose trust when honeypots drain their wallets. They lose trust when "verified" tokens turn out to be scams. TestMachine’s Token Custody tool rebuilds that trust: 11M+ tokens scored before users touch them. Keep your users safe. Keep their trust. testmachine.ai/products/token…

@immunefi Whitehat wins!

We've just crossed $135,000,000 in payouts to security researchers on Immunefi. Huge shoutout to every single SR who's gotten a payment. Collectively, you've saved countless billions from being hacked. Imagine what crypto would've looked like without those contributions over the last 5.5 years.

@pashov We've used it.

like this post if you've ever used pashov/skills

The point: When Azimuth catches 70% of the bugs that actually drained $1B+ in production, it's not optional. AI doesn't replace auditors. But when it catches Euler's $197M bug? You can't ignore it. Try a 14-day free trial of Azimuth: app.testmachine.ai

What Azimuth excels at: ✓ Oracle manipulation (TWAP, spot price) ✓ Reentrancy (ERC777, callback exploits) ✓ Donation/inflation attacks ✓ Access control bypasses ✓ Exchange rate manipulation What it misses: ✗ Unverified proxy implementations ✗ Transient storage bugs ✗ Post-incident bricked contracts

We ran Azimuth against 54 real DeFi exploits. $1.12B in actual losses. Not theoretical bugs. Not CTFs. Production hacks. Here's what it found 👇

@RoundtableSpace The best AI-powered security tool in the business.

WHAT ARE YOU BUILDING TODAY?

@solana big week lets go

BIG week

@ForgeAudit Exactly. An audit is just a point-in-time snapshot. Risk evolves with every update!! We broke this down here 👇 testmachine.ai/blog/smart-con…

One audit doesn't mean your protocol is safe forever. It means every vulnerability we could find at that point in time has been addressed. But protocols evolve. New features get added. Integrations expand. Every change shifts the attack surface. The teams that stay secure aren't the ones who audited once. They're the ones who treat security as an ongoing process. Every new feature deserves a fresh set of eyes. Every upgrade is a new risk surface that didn't exist before. And even after your code is final, bug bounties exist for a reason. A hundred security researchers looking at your protocol will always catch things a single audit couldn't. Different perspectives, different assumptions, different findings. One audit is the foundation. What you build on top of it determines how long it holds.

@EliBenSasson Security - we are working hard on it🫡

Complete the sentence: The real thing standing between crypto and mass adoption is...

@pashov We ran Azimuth against LLMs on the same EVMBench security repo (some using Pashov audit skill). Results speak for themselves.

🚨Ethereum Developers: you can now install your first AI Auditor in 1 minute - fully autonomous, available 24/7, with multiple sub-agent helpers. Open Source. FREE to use (with your AI model) and already finding vulnerabilities in smart contracts. Link below🫡

Cooking 🧑‍🍳

Early signal: tooling matters as much as the model If you're building agents, don't just throw a bigger LLM at the problem. Build better scaffolding Full results coming soon! 📊

What's the difference? The model is the same. But our harness: → Knows what to look for (security-specific patterns) → Knows how to look (structured audit workflows) → Knows where to look (tooling that guides analysis) The LLM is the engine. The harness is the driver

We ran a small test in our labs today Vanilla Codex (GPT 5.5) vs Azimuth on the same security audit task Vanilla: 2/7 vulnerabilities found Our harness: 7/7 vulnerabilities found The model is the same. The difference is the tooling. Here's why that matters 🧵