Yes, you can scan proprietary code for plagiarism, but the right method depends on what you need to compare and where the code may be sent.
Source-code similarity tools can identify overlapping patterns; their findings need human review and do not, by themselves, prove copying or misconduct.
What does a code plagiarism scan check?
Code similarity tools compare source files or repositories for shared structure, tokens, sequences, or other patterns.
Depending on the tool and its configuration, a scan may compare code against a supplied set of files, a repository, or a larger collection of indexed code.
The scope matters: a result can only speak to the material the tool actually searched.
Similarity is not the same as identical text.
Two programs written in the same language may use familiar syntax, standard library calls, common algorithms, or required interfaces.
Those features can create overlap even when developers worked independently.
Conversely, code can be modified, renamed, or reorganized and still retain meaningful structural similarities.
AI detection and code similarity are different
An AI authorship classifier estimates whether text resembles machine-generated writing.
A source-code similarity checker looks for relationships between code samples.
These are different tasks, and a result from one should not be treated as an answer to the other.
Turnitin has stated that its AI writing model does not reliably detect code.
Its writing report should not be confused with source-code similarity analysis.
If your concern is whether code resembles another submission or codebase, look for a tool designed for source-code comparison rather than relying on a prose-oriented AI report.
Can I upload proprietary code to an online checker?
Technically, some services let users submit code for analysis.
Whether you should upload a particular repository is a separate question.
Proprietary source code may contain trade secrets, customer-specific logic, unreleased features, credentials, personal data, or information covered by a contract or confidentiality obligation.
Before using an external service, review its current privacy and data-handling terms.
Look for clear answers to questions such as:
- Is submitted code stored, and for how long?
- Can the provider use submissions to improve or train its systems?
- Who can access the code, including contractors or subprocessors?
- Can you request deletion, and does deletion cover backups?
- Where is data processed, and what security controls are described?
- Does your organization require approval before code is sent to a third party?
Do not assume that a service is safe for confidential material simply because it offers a scan.
If the terms are unclear, check with your organization’s security, legal, or engineering lead.
For sensitive code, an approved self-hosted or local workflow may be more suitable, provided it meets your team’s security requirements.
How to scan code more safely
Start by defining the comparison you need.
For example, you may be checking whether one submission overlaps with another, whether a new contribution resembles existing repository files, or whether a code sample matches material available to a particular service.
Different goals call for different comparison sets and review steps.
- Confirm authorization. Make sure you are allowed to scan the code and that your chosen service or workflow is approved for its sensitivity level.
- Reduce unnecessary exposure. Exclude secrets, credentials, unrelated files, and data that the scan does not need.
Do not upload an entire repository when a smaller comparison is sufficient. - Check the comparison scope. Record which files, repositories, or reference sources were included.
A report cannot establish similarity to material outside that scope. - Review matches in context. Inspect the overlapping sections, their purpose, the surrounding code, and whether shared elements are conventional or required.
- Keep a record of the method. Note the tool, version or date where available, settings, inputs, and limitations so another reviewer can understand what the report means.
How should I interpret a similarity report?
A similarity score or highlighted match is a lead for investigation, not a verdict.
Check whether the overlap involves substantial, distinctive logic or common constructs such as boilerplate, generated files, public interfaces, standard algorithms, or code copied from an authorized source.
Also consider the project’s history, attribution, licenses, and contribution records.
Reports may miss transformed or obfuscated code, and they may flag unrelated code that shares common patterns.
Their results depend on the tool, its comparison corpus, language support, preprocessing, and settings.
A low score does not prove that code is original, and a high score does not establish intent, ownership, or a policy violation.
If the scan is part of an academic, hiring, compliance, or workplace review, follow the applicable process.
Policies vary by institution and organization.
Give the author an opportunity to explain the result, and have a qualified person assess the evidence rather than relying on an automated score alone.
What about Codespy?
Codespy is a service positioned for source-code detection.
It may be a relevant option to investigate when you need an external code-focused checker, but its mention is not an endorsement or a claim that it has been tested for your repository.
Before submitting proprietary material, review its current terms, security information, supported languages, comparison scope, and data-retention practices.
Confirm that these meet your organization’s requirements.
For any service, verify what its report actually compares and what happens to submitted files.
Product capabilities and terms can change, so consult the provider’s current documentation rather than relying on assumptions or an old description.
When is scanning appropriate?
Scanning can help triage a specific concern, support a code review, or locate possible overlap between authorized comparison samples.
It is less useful when the question is vague, the reference set is unknown, or reviewers intend to treat a score as definitive proof.
Use the result as one piece of evidence alongside source history, documentation, attribution, project context, and a fair review process.
The safest scan is one with a clear purpose, a limited and authorized input, a known comparison scope, and a human reviewer who understands the limits of code similarity analysis.