Prepared by:
HALBORN
Last Updated 03/28/2025
Date of Engagement: March 21st, 2025 - March 25th, 2025
No Reported Findings to Address
All findings
0
Critical
0
High
0
Medium
0
Low
0
Informational
0
Laminar
engaged Halborn to conduct a security assessment on smart contracts beginning on March 21st, 2025 and ending on March 25th, 2025. The security assessment was scoped to the smart contracts provided to the Halborn team.
Commit hashes and further details can be found in the Scope section of this report.
The team at Halborn dedicated 3 days for the engagement and assigned one full-time security engineers to evaluate the security of the smart contract.
The security engineer is a blockchain and smart-contract security expert with advanced penetration testing, smart-contract hacking, and deep knowledge of multiple blockchain protocols.
The purpose of this assessment is to:
Ensure that Smart Contract functions operate as intended.
Identify potential security issues with the Smart Contracts.
In summary, no security vulnerabilities were identified for the contracts in-scope.
Halborn performed a combination of manual, semi-automated and automated security testing to balance efficiency, timeliness, practicality, and accuracy regarding the scope of this assessment. While manual testing is recommended to uncover flaws in logic, process, and implementation; automated testing techniques help enhance coverage of the code and can quickly identify items that do not follow security best practices. The following phases and associated tools were used throughout the term of the assessment:
Research into architecture and purpose.
Smart contract manual code review and walk-through.
Manual assessment of use and safety for the critical Solidity variables and functions in scope to identify any vulnerability classes
Manual testing by custom scripts.
Static Analysis of security for scoped contract, and imported functions. (Slither
)
Local deployment and testing (Foundry
)
EXPLOITABILITY METRIC () | METRIC VALUE | NUMERICAL VALUE |
---|---|---|
Attack Origin (AO) | Arbitrary (AO:A) Specific (AO:S) | 1 0.2 |
Attack Cost (AC) | Low (AC:L) Medium (AC:M) High (AC:H) | 1 0.67 0.33 |
Attack Complexity (AX) | Low (AX:L) Medium (AX:M) High (AX:H) | 1 0.67 0.33 |
IMPACT METRIC () | METRIC VALUE | NUMERICAL VALUE |
---|---|---|
Confidentiality (C) | None (I:N) Low (I:L) Medium (I:M) High (I:H) Critical (I:C) | 0 0.25 0.5 0.75 1 |
Integrity (I) | None (I:N) Low (I:L) Medium (I:M) High (I:H) Critical (I:C) | 0 0.25 0.5 0.75 1 |
Availability (A) | None (A:N) Low (A:L) Medium (A:M) High (A:H) Critical (A:C) | 0 0.25 0.5 0.75 1 |
Deposit (D) | None (D:N) Low (D:L) Medium (D:M) High (D:H) Critical (D:C) | 0 0.25 0.5 0.75 1 |
Yield (Y) | None (Y:N) Low (Y:L) Medium (Y:M) High (Y:H) Critical (Y:C) | 0 0.25 0.5 0.75 1 |
SEVERITY COEFFICIENT () | COEFFICIENT VALUE | NUMERICAL VALUE |
---|---|---|
Reversibility () | None (R:N) Partial (R:P) Full (R:F) | 1 0.5 0.25 |
Scope () | Changed (S:C) Unchanged (S:U) | 1.25 1 |
Severity | Score Value Range |
---|---|
Critical | 9 - 10 |
High | 7 - 8.9 |
Medium | 4.5 - 6.9 |
Low | 2 - 4.4 |
Informational | 0 - 1.9 |
Critical
0
High
0
Medium
0
Low
0
Informational
0
Security analysis | Risk level | Remediation Date |
---|
Halborn used automated testing techniques to enhance the coverage of certain areas of the smart contracts in scope. Among the tools used was Slither, a Solidity static analysis framework. After Halborn verified the smart contracts in the repository and was able to compile them correctly into their ABIs and binary format, Slither was run against the contracts. This tool can statically verify mathematical relationships between Solidity variables to detect invalid or inconsistent usage of the contracts' APIs across the entire code-base.
The security team conducted a comprehensive review of findings generated by the Slither static analysis tool. All the issues identified by the Slither tool were false positives.
Halborn strongly recommends conducting a follow-up assessment of the project either within six months or immediately following any material changes to the codebase, whichever comes first. This approach is crucial for maintaining the project’s integrity and addressing potential vulnerabilities introduced by code modifications.
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