Byte Sized Security: Automating Security Testing for your Web3 Product

In the rapidly evolving realm of blockchain technologies, ensuring the robust security of software has evolved into an imperative and business critical task. With cyber threats growing increasingly sophisticated, web3 companies must adopt proactive measures to safeguard their digital assets effectively. One such emerging solution gaining remarkable traction is the integration of automated testing and invariant checks into the development process. In this multi part series article, we will delve deep into the rationale behind the adoption of this approach and explore its multifaceted implementation. In future articles we will give an overview of the tooling available and how you can incorporate it into your web3 product development process to automate vulnerability detection.

The Imperative Need for Advanced Solutions

The escalating complexity of distributed applications and smart contracts correlates directly with the proliferation of potential vulnerabilities within them. The realization that traditional testing methods might overlook critical security issues has driven the adoption of advanced tools and methodologies to empower web3 projects. These tools are adept at uncovering specific vulnerability classes that often elude manual testing efforts. By simulating diverse attack scenarios, these automated solutions effectively identify vulnerabilities pertaining to input validation, memory safety, access control/authorization violations, and more. Employing such tests consistently during development significantly minimizes the risk of exposing security flaws to potential attackers in your web3 product.

Targeting Vulnerability Classes

Leveraging automated security testing tools in your development pipelines can drastically increase your products security however is not a silver bullet. A layered approach is needed to target specific vulnerability classes or system properties that should always be true with automated testing and supplement with dynamic testing. Knowing what types of vulnerabilities are best detected by what type of tool is the first step in automating web3 security product testing. Below are some examples of vulnerability classes that can be identified with automation embedded into your build pipeline and some guiding tenets.

Safeguarding Contracts with State Machine Analysis

A fundamental perspective on contract behavior involves treating them as state machines. This view prompts the necessity to 1) Prevent invalid states from being accessible 2) Ensure that if a state is valid, it's reachable and 3) Verify that no state ensnares the contract. 

Navigating Access Controls with Precision

Smart contracts often entail roles with distinct privileges, necessitating rigorous access control mechanisms. Two primary tenets govern this aspect 1) Each user should exclusively perform authorized actions and 2) No user, irrespective of their role, should hinder actions of more privileged users.

Nailing Arithmetic Operations for Soundness

The integrity of arithmetic operations is paramount in smart contract security. While integrating libraries such as SafeMath mitigates overflows and underflows, it's imperative to address broader arithmetic vulnerabilities like rounding errors and traps in contract logic.

Ensuring Inheritance Harmony

The inheritance structure of Solidity contracts introduces complexities that demand thorough analysis. Oversight in areas like shadowing functions and linearization order can compromise contract integrity.

The Web of External Interactions

Smart contracts interact dynamically with external entities, some of which may not be inherently trustworthy. The onus is on developers to ensure that their contracts remain secure even in the face of potentially compromised external inputs. Automated tooling is great at validating these cases by sending permutations of non standard inputs and evaluating the result.

Harmonizing with Standards

Smart contracts adhering to Ethereum standards like ERC20 benefit from established guidelines. However, historical design flaws in these standards necessitate vigilant adherence.

Automated tooling can aide in identifying deviations from standards and ensures that your contracts align with the expected norm, safeguarding against potential vulnerabilities stemming from non-standard practices.

Unveiling the Potency of Invariants

An invariant is a condition or property that should always hold true for a software system. For example in an ERC20 contract an invariant would be that the sum of all balances should equal the total supply. Invariants, as the bedrock of software integrity, warrant meticulous attention. These are conditions that persist unaltered despite the evolution of the codebase and should be constantly checked (using automation) after changes are made. Integrating invariant checks into the development pipeline empowers teams to identify potential breaches of vital security properties. This practice acquires heightened significance after each code modification, ensuring the consistent preservation of security throughout the software's lifecycle.

Coming up next!

By incorporating automated security tooling into your product’s testing regimen, you can enhance their contract's resilience against several types of vulnerability classes. This helps free up valuable security resources to focus on complex high impact vulnerabilities not suited for automated tooling. In the next blog we’ll take a look at available tooling for EVM-based smart contract products and tactics to implement automate this tooling as part of your deployment pipeline. We can help you implement this type of solution or customize security checks specific to your product. Contact our team now for a free consultation.