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Thor - Vechain Ecosystem

Prepared by:

Halborn Logo

HALBORN

Last Updated 11/18/2025

Date of Engagement: September 5th, 2025 - October 30th, 2025

Summary

100% of all REPORTED Findings have been addressed

All findings

4

Critical

0

High

1

Medium

0

Low

0

Informational

3

Table of Contents

1. Introduction

VeChain Foundation engaged Halborn to conduct a security assessment of the VeChainThor Go codebase beginning on September 5, 2025 and ending on October 31, 2025. The security assessment was scoped to consensus, staking, networking, API, and runtime components in the GitHub repository provided to the Halborn team.

2. Assessment summary

A senior Halborn security engineer performed a full manual review of the Go codebase with targeted automation. The objectives were to:

    • Ensure protocol components operate as intended under adversarial conditions.

    • Identify logic, state-transition, and concurrency risks.

    • Recommend robust, low-risk remediations aligned with production constraints.


Key risk themes included:

    • Unsigned arithmetic underflows in staking and epoch housekeeping could block expected activations or corrupt state.

    • Scheduler policy allows offline proposers to be included and auto-reactivated, weakening uptime enforcement.

    • Panic usage in consensus flow reduces fault tolerance and can crash nodes under rare invariants.

    • Input validation gaps (e.g., zero weights) reduce robustness and future maintainability.

3. Key recommendations

    • Harden arithmetic in staking/housekeeping: Guard decrements and bounds checks to prevent unsigned underflow when the leader set is empty and an exit is scheduled; validate subtraction does not underflow queued stake.

    • Enforce proposer liveness policy: Exclude offline validators from scheduling and remove auto-activation side-effects; ensure updates cannot clear OfflineBlock implicitly.

    • Replace panics in consensus-critical paths: Return typed errors instead of panics in scheduling/consensus to prevent node crashes and enable graceful rejection/recovery.

    • Validate scheduler inputs: Require weight > 0 before computing scores to avoid undefined behavior and simplify invariants.

    • Strengthen state-transition prechecks: Centralize and reuse validation guards for staking transitions to ensure consistent behavior across services and edge epochs.

4. Test approach and methodology

    • Architecture and threat modeling: Reviewed consensus, staking, scheduling, activation/exit flows, and state persistence boundaries.

    • Manual code review: Deep review of consensus, scheduler, staking services, epoch housekeeping, and API boundaries for logic and state-transition flaws.

    • Static analysis and linters: Ran Go-focused checks (e.g., go vet, staticcheck, golangci-lint) to flag common correctness and robustness issues.

    • Targeted dynamic validation: Reproduced edge scenarios described in findings; leveraged existing fuzz and property tests around blocks and state transitions where present; recommended additional unit/property tests for activation/exit edge cases.

    • Defense-in-depth recommendations: Proposed invariant checks, error handling upgrades, and precondition guards to improve resilience and maintainability.


5. 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.

5.1 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:
EXPLOITABILITY METRIC (mem_e)METRIC VALUENUMERICAL 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 EE is calculated using the following formula:

E=∏meE = \prod m_e

5.2 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 (mIm_I)METRIC VALUENUMERICAL VALUE
Confidentiality (C)None (C:N)
Low (C:L)
Medium (C:M)
High (C:H)
Critical (C: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 II is calculated using the following formula:

I=max(mI)+∑mI−max(mI)4I = max(m_I) + \frac{\sum{m_I} - max(m_I)}{4}

5.3 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 (CC)COEFFICIENT VALUENUMERICAL VALUE
Reversibility (rr)None (R:N)
Partial (R:P)
Full (R:F)		1
0.5
0.25
Scope (ss)Changed (S:C)
Unchanged (S:U)		1.25
1
Severity Coefficient CC is obtained by the following product:

C=rsC = rs

The Vulnerability Severity Score SS is obtained by:

S=min(10,EIC∗10)S = min(10, EIC * 10)

The score is rounded up to 1 decimal places.
SeverityScore Value Range
Critical9 - 10
High7 - 8.9
Medium4.5 - 6.9
Low2 - 4.4
Informational0 - 1.9

6. SCOPE

REPOSITORY
(a) Repository: thor
(b) Assessed Commit ID: f5efbfa
(c) Items in scope:
  • abi/
  • api/ (exclude api/doc/**)
  • bft/
↓ Expand ↓
Out-of-Scope: docs/**, api/doc/**, builtin/gen/** (Solidity, ABIs, bin-runtime), test/**, **/*_test.go, **/*.md, **/*.png, **/*.css, tracers/*.js, tracers/*.json, api/doc/*.js, api/doc/*.css, genesis/example.json, vrf/*.json
Remediation Commit ID:
Out-of-Scope: New features/implementations after the remediation commit IDs.

7. Assessment Summary & Findings Overview

Critical

0

High

1

Medium

0

Low

0

Informational

3

Security analysisRisk levelRemediation Date
Queued underflow enables contract drainHighSolved - 09/17/2025
Epoch Housekeeping UnderflowInformationalAcknowledged
Uncaught panic in Schedule leads to hard process crashInformationalAcknowledged
Zero-weight proposer accepted in scheduler score computationInformationalAcknowledged

8. Findings & Tech Details

8.1 Queued underflow enables contract drain

//

High

Description
BVSS
Recommendation
Remediation Comment
Remediation Hash
https://github.com/vechain/thor/pull/1348

8.2 Epoch Housekeeping Underflow

//

Informational

Description
BVSS
Recommendation
Remediation Comment

8.3 Uncaught panic in Schedule leads to hard process crash

//

Informational

Description
BVSS
Recommendation
Remediation Comment

8.4 Zero-weight proposer accepted in scheduler score computation

//

Informational

Description
BVSS
Recommendation
Remediation Comment

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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