Several comfortable beliefs about AI-written code do not hold up against primary sources. AI output is not automatically free to use, matching checks are not guarantees, a prompt is not a security control, and the submitter, not the tool, remains accountable. Here are five claims checked one by one.
Myth 1: AI-written code is public domain
Fact: the question is narrower and less settled than that. The US Copyright Office says copyright does not extend to purely AI-generated material, but it protects human authorship, including creative selection, arrangement and modification of outputs, and it analyzes sufficiency case by case. That is about copyright in the US. It does not say the code is free of other obligations. See is AI-generated code copyrightable.
Myth 2: Our assistant blocks public code, so there is no license risk
Fact: GitHub documents that its Block setting checks suggestions, with about 150 characters of surrounding code, against public code on GitHub and hides a match or near match. That helps. It does not cover code from other sources or lightly reworded text, and it does not cover the packages the assistant tells you to install. Keep license scanning on dependencies.
Myth 3: Telling the model to be secure makes the output safe
Fact: OWASP's guidance on improper output handling says to treat the model as any other user and validate its responses in code. A prompt is a request. Controls belong where the output lands. See is model output safe to pass to your database.
Myth 4: An agent with admin rights is faster and equally safe
Fact: OWASP names excessive functionality, permissions and autonomy as root causes of damaging actions, and recommends minimal tools, scoped credentials and human approval for high-impact steps. Speed gained from broad rights is borrowed from your incident budget.
Myth 5: If the reviewer approved it, the author is off the hook
Fact: the Linux kernel's published guidance expects submitters to understand and be able to defend everything they submit, and lets maintainers reject work that fails this. Review adds a second set of eyes. It does not transfer accountability.
A short summary table
| Belief | What the source says |
|---|---|
| Public domain | Purely AI output is not covered by copyright in the US, human contributions can be |
| Block mode removes risk | Checks public code on one platform, matches and near matches only |
| Prompt equals control | Validate and encode in code |
| Admin agent is fine | Least privilege and approvals |
| Reviewer owns it | Submitter must be able to defend it |
Where Safeguard fits
Safeguard scans dependencies, produces SBOMs with license data, and applies policy gates to pull requests. Try the free scan.
Try it yourself
Click through it at your own pace, or open it full screen.
Click through the inventory and look at the licenses recorded beside each package.
Quick check
Which myth costs teams the most?
This article does not rank them. Check each against your own process.
Are these claims legal advice?
No. They summarise public documents. Ask counsel about your situation.
Sources
- US Copyright Office, Part 2: Copyrightability
- GitHub Docs: managing Copilot policies
- OWASP LLM05:2025
- OWASP LLM06:2025
- Linux kernel documentation on tool-generated content
Last reviewed: 19 October 2026.
