DLN Solana Contracts - deBridge

Prepared by:

HALBORN

Last Updated 11/14/2024

Date of Engagement by: September 16th, 2024 - October 7th, 2024

Summary

100% of all REPORTED Findings have been addressed

All findings

Critical

High

Medium

Low

Informational

1. Introduction
2. Assessment summary
3. Test approach and methodology
4. Risk methodology
5. Scope
6. Assessment summary & findings overview
7. Findings & Tech Details

7.1 Logging can be improved in key processes

8. Automated Testing

1. Introduction

deBridge team engaged Halborn to conduct a security assessment on their dln-dst and dln-src programs beginning on September 16th, 2024 and ending on October 7th, 2024. The security assessment was scoped to the smart contracts provided in the GitHub repositories dln-solana. Commit hashes, and further details, can be found in the Scope section of this report.

deBridge is releasing a new version of dln-dst and dln-src, that includes new implementations for the different instructions necessary to coordinate token orders across different blockchains.

2. Assessment Summary

Halborn was provided 1,5 weeks for the engagement and assigned one full-time security engineer to review the security of the Solana Program in scope. The engineer is a blockchain and smart contract security expert with advanced smart contract hacking skills, and deep knowledge of multiple blockchain protocols.

The purpose of the assessment is to:

Identify potential security issues within the codebase.
Check that the codebase does not have any known vulnerability that might affect the participants' funds
Validate that the implemented changes do not affect the asynchronous nature and cross chain liquidity capabilities of the DLN network.

In summary, Halborn identified one improvement to reduce the likelihood and impact of multiple risks, which has been acknowledged by the deBridge team. It was the following:

Logging can be improved in key processes

3. Test Approach and Methodology

Halborn performed a combination of a manual review of the source code and automated security testing to balance efficiency, timeliness, practicality, and accuracy in regard to the scope of the program assessment. While manual testing is recommended to uncover flaws in business logic, processes, and implementation; automated testing techniques help enhance coverage of programs 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 the architecture, purpose, and use of the token airdrop program.
Manual program source code review to identify business logic issues.
Mapping out possible attack vectors
Thorough assessment of safety and usage of critical Rust variables and functions in scope that could lead to arithmetic vulnerabilities.
Scanning dependencies for known vulnerabilities (cargo audit).

4. RISK METHODOLOGY

Every vulnerability and issue observed by Halborn is ranked based on two sets of Metrics and a Severity Coefficient. This system is inspired by the industry standard Common Vulnerability Scoring System.

The two Metric sets are: Exploitability and Impact. Exploitability captures the ease and technical means by which vulnerabilities can be exploited and Impact describes the consequences of a successful exploit.

The Severity Coefficients is designed to further refine the accuracy of the ranking with two factors: Reversibility and Scope. These capture the impact of the vulnerability on the environment as well as the number of users and smart contracts affected.

The final score is a value between 0-10 rounded up to 1 decimal place and 10 corresponding to the highest security risk. This provides an objective and accurate rating of the severity of security vulnerabilities in smart contracts.

The system is designed to assist in identifying and prioritizing vulnerabilities based on their level of risk to address the most critical issues in a timely manner.

4.1 EXPLOITABILITY

Attack Origin (AO):

Captures whether the attack requires compromising a specific account.

Attack Cost (AC):

Captures the cost of exploiting the vulnerability incurred by the attacker relative to sending a single transaction on the relevant blockchain. Includes but is not limited to financial and computational cost.

Attack Complexity (AX):

Describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability. Includes but is not limited to macro situation, available third-party liquidity and regulatory challenges.

Metrics:

EXPLOITABILIY METRIC ( $m_e$ )	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

Exploitability

E

is calculated using the following formula:

$E = \prod m_e$

4.2 IMPACT

Confidentiality (C):

Measures the impact to the confidentiality of the information resources managed by the contract due to a successfully exploited vulnerability. Confidentiality refers to limiting access to authorized users only.

Integrity (I):

Measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of data stored and/or processed on-chain. Integrity impact directly affecting Deposit or Yield records is excluded.

Availability (A):

Measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability. This metric refers to smart contract features and functionality, not state. Availability impact directly affecting Deposit or Yield is excluded.

Deposit (D):

Measures the impact to the deposits made to the contract by either users or owners.

Yield (Y):

Measures the impact to the yield generated by the contract for either users or owners.

Metrics:

IMPACT METRIC ( $m_I$ )	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

Impact

I

is calculated using the following formula:

$I = max(m_I) + \frac{\sum{m_I} - max(m_I)}{4}$

4.3 SEVERITY COEFFICIENT

Reversibility (R):

Describes the share of the exploited vulnerability effects that can be reversed. For upgradeable contracts, assume the contract private key is available.

Scope (S):

Captures whether a vulnerability in one vulnerable contract impacts resources in other contracts.

Metrics:

SEVERITY COEFFICIENT ( $C$ )	COEFFICIENT VALUE	NUMERICAL VALUE
Reversibility ( $r$ )	None (R:N) Partial (R:P) Full (R:F)	1 0.5 0.25
Scope ( $s$ )	Changed (S:C) Unchanged (S:U)	1.25 1

Severity Coefficient

C

is obtained by the following product:

$C = rs$

The Vulnerability Severity Score

S

is obtained by:

$S = min(10, EIC * 10)$

The score is rounded up to 1 decimal places.

Severity	Score Value Range
Critical	9 - 10
High	7 - 8.9
Medium	4.5 - 6.9
Low	2 - 4.4
Informational	0 - 1.9

5. SCOPE

Files and Repository

(a) Repository: dln-solana

(b) Assessed Commit ID: 6e3a755

crates/client/examples/create_order.rs
crates/client/examples/devnet.rs
crates/client/examples/external_ix.rs

↓ Expand ↓

Out-of-Scope:

Out-of-Scope: New features/implementations after the remediation commit IDs.

6. Assessment Summary & Findings Overview

Critical

High

Medium

Low

Informational

Security analysis	Risk level	Remediation Date
Logging can be improved in key processes	Informational	Acknowledged - 11/14/2024

7. Findings & Tech Details

7.1 Logging can be improved in key processes

// Informational

Description

The current logging mechanism for some functions lacks sufficient detail, which limits the ability to troubleshoot and analyze system failures effectively.

While logs capture when a process fails, they do not include critical information such as the parameters or inputs that triggered the error.

As a result, the deBridge team might face increased difficulty in identifying the root cause of failures, leading to longer recovery times and higher costs in resolving issues. In some cases, this lack of detailed logging may result in recurring failures going undetected or being improperly addressed. Furthermore, the incomplete logs hinder post-incident reviews, reducing the ability to implement long-term fixes and improve system reliability.

The identified log messages that lack information are mentioned below:

crates/client/src/dln_dst_instructions/builder.rs:
- the build_fulfill_instruction function currently logs an error in case a token address of a take order cannot be transformed into a Public Key (PubKey). The function is not logging the token address that generated the error.
- the build_fulfill_instruction function currently logs an error in case the receiver destination token accounts of an order can't be transformed into a Public Key (PubKey). The function is not logging the destination token address that generated the error.
programs/dln_dst/src/lib.rs:
- the close_external_call_storage function currently logs an error in case that order_id != external_call_order_id. The function is not logging the order_id that generated the error.

Score

AO:S/AC:L/AX:L/C:N/I:N/A:N/D:N/Y:N/R:N/S:U (0.0)

Recommendation

Consider adding extra information to the mentioned functions that are not implementing complete logging:

for crates/client/src/dln_dst_instructions_builder.rs:
- in line 193, add the token address that generated the error
- in line 199, add the destination token address that generated the error
for programs/dln_dst/src/lib.rs:
- in lines 635, 653 and 671 add the order_id that generated the error

Remediation

ACKNOWLEDGED: The deBridge team acknowledged this finding.

8. Automated Testing

Static Analysis Report

Description

Halborn used automated security scanners to assist with detection of well-known security issues and vulnerabilities. Among the tools used was cargo audit, a security scanner for vulnerabilities reported to the RustSec Advisory Database. All vulnerabilities published in https://crates.io are stored in a repository named The RustSec Advisory Database. cargo audit is a human-readable version of the advisory database which performs a scanning on Cargo.lock. Security Detections are only in scope. All vulnerabilities shown here were already disclosed in the above report. However, to better assist the developers maintaining this code, the auditors are including the output with the dependencies tree, and this is included in the cargo audit output to better know the dependencies affected by unmaintained and vulnerable crates.

Cargo Audit Results

ID	package	Short Description
RUSTSEC-2024-0344	curve25519-dalek	Timing variability in `curve25519-dalek`'s `Scalar29::sub`/`Scalar52::sub`
RUSTSEC-2022-0093	ed25519-dalek	Double Public Key Signing Function Oracle Attack on `ed25519-dalek`
RUSTSEC-2024-0332	h2	Degradation of service in h2 servers with CONTINUATION Flood
RUSTSEC-2024-0003	h2	Resource exhaustion vulnerability in h2 may lead to Denial of Service (DoS)
RUSTSEC-2024-0019	mio	Tokens for named pipes may be delivered after deregistration
RUSTSEC-2024-0357	openssl	`MemBio::get_buf` has undefined behavior with empty buffers
RUSTSEC-2023-0063	quinn-proto	Denial of service in Quinn servers
RUSTSEC-2024-0336	rustls	`rustls::ConnectionCommon::complete_io` could fall into an infinite loop based on network input
RUSTSEC-2024-0006	shlex	Multiple issues involving quote API
RUSTSEC-2020-0071	time	Potential segfault in the time crate
RUSTSEC-2023-0001	tokio	reject_remote_clients Configuration corruption
RUSTSEC-2023-0065	tungstenite	Tungstenite allows remote attackers to cause a denial of service
RUSTSEC-2023-0052	webpki	webpki: CPU denial of service in certificate path building

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.

1. Introduction
2. Assessment summary
3. Test approach and methodology
4. Risk methodology
5. Scope
6. Assessment summary & findings overview
7. Findings & Tech Details