• 💬 Let’s Talk About Security Auditing in Web3

    Ophirians Maharlika
    Updated: Jan 10, 2026
    Views: 99

    Security has become one of the most talked-about topics in Web3 — and for good reason.

    Every week, we see new exploits, drained contracts, or projects shutting down because something small was missed during development. What’s interesting is that most of these issues aren’t caused by complex hacks… but by simple oversights that could’ve been caught early.

    That’s why we’ve started using an AI-based security auditing tool as part of our workflow.

    Not as a replacement for human audits — but as a first line of defense.

    The idea is simple:

    • Scan contracts early

    • Catch common issues before deployment

    • Improve code quality during development

    • Reduce risk before manual review

    AI doesn’t get tired.
    It doesn’t skip steps.
    And it can analyze patterns faster than a human reviewer.

    Of course, no tool guarantees zero risk — and it shouldn’t claim to. Security is a process, not a checkbox. But having an AI auditor in the loop helps us move faster and safer.

    It’s like having an extra set of eyes on every commit.

    Curious to hear from others:

    • Are you using AI tools in your security workflow?

    • Do you trust automated audits?

    • Or do you rely purely on manual review?

    Let’s discuss. 👇

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  • Abdil Hamid

    @ForensicBlockSmith1mo

    We’ve tried AI scanners in our workflow as well, mostly early on when contracts were still evolving.

    They helped with some obvious misses, but honestly most of the issues that caused real pain for us weren’t things a scanner could flag. They showed up when assumptions broke across functions or when state changes played out over multiple calls.

    One thing we learned the hard way is that a clean automated scan doesn’t mean much unless someone actually reasons through the system end to end. In fact, the bigger risk for us was teams trusting “no issues found” more than they should.

    These days we treat AI checks strictly as hygiene, not a signal of safety, and try to keep human review depth the same regardless of what the tools say.

  • Damon Whitney

    @CareerSensei3w

    We went through a similar phase last year when everyone started pushing AI checks as part of “secure by default”.

    What stood out for us was that the hardest bugs never came from things a scanner could reason about in isolation. Most of the serious issues showed up only after a few weeks in staging, when edge cases piled up and assumptions started leaking.

    The dangerous part wasn’t that AI missed things — that’s expected. It was that people subconsciously relaxed once automated checks were green. Reviews got faster, questions got softer.

    We eventually treated automated checks like linting: useful, necessary, but never a reason to reduce manual scrutiny or threat modeling.

    Security felt better once we stopped asking “what tools are we using?” and started asking “what assumptions would break if this contract is called in the worst possible order?”

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