How should Stellar DevKit implement static security analysis for Soroban smart contracts?

How should Stellar DevKit implement static security analysis for Soroban smart contracts?

Category: Q&A

Question:

What would be a reliable architecture for implementing a static security-analysis engine in Stellar DevKit that examines Soroban smart-contract WASM files and identifies potential security vulnerabilities before deployment?

In particular:

1. Which WASM-level characteristics should the analyzer inspect?
2. How should it identify potentially unsafe contract patterns and suspicious host-function usage?
3. How should findings be classified by severity and confidence?
4. Should the engine support SARIF output for integration with GitHub Code Scanning?
5. How can we test the analyzer against known vulnerable and safe contracts?

What architecture would make the analyzer useful to developers without producing excessive false positives?

[Ayinkx]

I would recommend implementing the security analyzer as a modular static-analysis pipeline rather than building one large detection function.

1. WASM inspection

Start by validating the input WASM module and inspecting its structure, imports, exports, memory declarations, function signatures and relevant instructions.

The analyzer should also account for Soroban-specific contract metadata and environment compatibility.

2. Rule-based security analysis

Create independent analysis rules for suspicious patterns, including:

• Unsafe assumptions about authorization.
• Missing or inadequate authorization checks.
• Risky external contract interactions.
• Potentially unsafe input handling.
• Resource-exhaustion risks.
• Incorrect handling of storage and contract state.

Rules should identify patterns requiring investigation rather than automatically declaring every suspicious pattern exploitable.

3. Severity and confidence

Each finding should include:

• A unique rule identifier.
• Severity: critical, high, medium or low.
• Confidence level.
• Affected function or WASM location where available.
• Explanation of the potential risk.
• Recommended remediation.

4. SARIF integration

Support SARIF 2.1.0 output so results can be consumed by compatible GitHub security tooling.

Each finding should map to a stable rule ID, severity, message and source location where available.

5. Testing and validation

Build a test corpus containing:

• Known vulnerable contracts.
• Secure contracts.
• Edge cases.
• Invalid WASM modules.
• False-positive scenarios.

Run regression tests whenever a new detection rule is introduced.

6. Architecture

I would separate the implementation into five components:

• WASM parser and validator.
• Soroban metadata and environment inspector.
• Security rule engine.
• Finding aggregator and severity classifier.
• SARIF and human-readable report generators.

This architecture would make the security engine extensible, testable and easier to maintain.

My recommendation: Begin with a small set of well-tested security rules, measure false positives, and expand the rule library incrementally. Static analysis should complement code review and dynamic testing, not claim to guarantee that a contract is secure.