• Can Smart Contracts Be Audited? What Are the Common Tools for Auditing?

    Rashad Collins

    Rashad Collins

    @tuuFSbF
    Updated: Jul 14, 2025
    Views: 979

    Can smart contracts really be audited, and what does that process look like in practice? I’m getting into smart contract development and keep hearing that auditing is essential for security, but I’m still not clear on how audits actually work.

    Do auditors manually check every line of code, or do they rely on automated tools to catch issues? Also, what are the most common tools and methods developers use when auditing smart contracts?

    I’d love to know what parts of the code deserve the most attention and any common mistakes beginners should avoid during an audit.

    If you have experience with smart contract security or auditing, please share how you approach it and what tips you have for someone just starting out. Thanks in advance!


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  • AlexDeveloper

    @Alexdeveloper8mos

    Yeah, you're spot on—smart contracts definitely need auditing, and I've learned this the hard way after dealing with multiple DeFi protocols over the past couple of years.

    I remember when I first started working with smart contracts, I thought automated tools would catch everything.

    Boy, was I wrong. Had a project where Slither and MythX both gave clean reports, but we still had a logic flaw that could've been exploited for flash loan attacks1. That's when I realized you can't just rely on automation.


    The tooling landscape has evolved massively in 2024. MythX has actually been discontinued—they've transitioned to Diligence Fuzzing, which is more powerful for finding edge cases. Slither's still my go-to for quick static analysis, but the new fuzzing tools like Echidna and Medusa are game-changers for catching complex vulnerabilities.

    What I've noticed working with recent audits is that access control vulnerabilities are now the biggest threat. According to the latest security reports, they accounted for 75% of all crypto hacks in 2024, with over $1.7 billion stolen4. That's why manual review has become even more critical—automated tools often miss these business logic flaws.

    CertiK and Quantstamp are still the gold standard for comprehensive audits, but I've been impressed with some newer players like Cyfrin and Hacken. They're bringing fresh perspectives and often catch things the established firms miss. The competition has really driven up quality across the board.


    Bug bounty programs have also become huge. Platforms like Immunefi and HackenProof are now offering up to $1M+ rewards for critical findings. I've seen projects combine formal audits with ongoing bounties, which creates this continuous security model that's way more effective than one-time audits.

    The cost of not auditing is brutal. We saw $2.9 billion lost across Web3 in 2024, but here's the kicker—90% of hacked projects never had any audit at all. Even basic automated scanning would've prevented most of these losses.


    Have you noticed any specific vulnerabilities becoming more common in your smart contract reviews? And what's your experience been with balancing audit costs against security needs for newer protocols?


  • Naina Grehwal

    @aIenFpZ8mos

    Following the discussion, I'll break down the practical differences between MythX, Slither, and Securify since I've been knee-deep in these tools for the past few years working on DeFi audits.

    Let me start with where each one really shines:

    Slither is honestly my go-to for daily development. It's incredibly fast—we're talking under a second for most contracts. The false positive rate sits around 4.33% which is solid, and it catches 92 different vulnerability types. The CI/CD integration is seamless; I've got it running in GitHub Actions and it rarely breaks the pipeline.


    MythX brings the heavy artillery with its combo of static analysis, symbolic execution, and fuzzing. Takes longer to run but finds deeper issues that Slither might miss. The commercial version offers better prioritization of findings, though the free tier can sometimes feel limited. Securify has gotten better since v2.0, but I've noticed it struggles with newer Solidity versions and has a higher timeout rate—around 10% failed analysis compared to Slither's 0.1%.


    For reentrancy detection, here's what I've seen in practice: Slither catches the obvious patterns quickly, while MythX excels at complex cross-contract scenarios. A recent study showed that existing tools miss around 50% of reentrancy vulnerabilities in real contracts, so I usually run multiple tools.

    False positives are the real pain point. Most tools flag between 18-25% false positives, which means you're spending hours manually verifying findings. Slither tends to be more conservative, while Securify can be overly aggressive.


    CI/CD integration is where Slither really wins. The slither-action on GitHub makes it trivial to automate. For handling false positives in pipelines, I maintain custom filter configs and run tools in "warning" mode rather than failing builds.


    Recent developments that caught my attention: LLM-based tools are showing promise with 90%+ F1 scores, and there's growing focus on DeFi-specific vulnerabilities like price manipulation that traditional tools miss.

    What's your experience been with gas optimization findings? I've found most tools flag micro-optimizations that aren't worth the review time, but occasionally catch real issues that save significant costs in production.

    Have you tried any of the newer fuzzing tools like Echidna or Medusa alongside these static analyzers? I'm curious about your workflow for combining different analysis approaches.


  • Abdil Hamid

    @ForensicBlockSmith8mos

    When comparing MythX, Slither, and Securify, it's essential to recognize their unique strengths and methodologies in smart contract analysis. Here's a breakdown:

    When comparing MythX, Slither, and Securify, it's essential to recognize their unique strengths and methodologies in smart contract analysis. Here's a breakdown:

    When comparing MythX, Slither, and Securify, it's essential to recognize their unique strengths and methodologies in smart contract analysis. Here's a breakdown:

    (1) MythX MythX is a cloud-based tool that combines symbolic execution, static analysis, and fuzzing. It performs well at identifying vulnerabilities such as reentrancy attacks and integer overflows. It integrates smoothly with developer tools like Truffle and Hardhat, making it a strong choice for automated testing in CI/CD pipelines. However, it may sometimes report false positives, especially with dynamic contract behavior. Developers often need to review flagged issues to ensure accuracy.

    (2) Slither Slither offers quick, static analysis and is great for detecting logic errors, unused variables, and inheritance problems. It runs faster than most tools and provides real-time feedback during development. Its main limitation is the inability to analyze vulnerabilities tied to dynamic contract states, such as deeply nested reentrancy attacks. Slither’s custom detectors allow developers to expand its capabilities as needed.

    (3) Securify Securify uses formal verification and data flow analysis to uncover vulnerabilities, particularly access control flaws and compliance violations. It generates precise, detailed reports but may take longer to analyze complex contracts. While its focus on accuracy reduces false positives, it might overlook certain edge cases in intricate contract setups.

    Handling False Positives

    =>MythX prioritizes thoroughness, which can lead to over-reporting. =>Slither minimizes false positives but has limited coverage for dynamic vulnerabilities. =>Securify aims for precision but may under-report due to its strict criteria.

    Best Practices For comprehensive audits, combine these tools:

    Use Slither for fast checks during development. Apply MythX for deeper, detailed analysis. Leverage Securify for final compliance and access control reviews.

    To improve results, fine-tune each tool’s configuration and manually verify critical findings. This combination covers most vulnerabilities while maintaining efficiency.

  • Shubhada Pande

    @ShubhadaJP3d

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