Secure AI-generated code before it ships
Scan AI-authored code for vulnerable patterns, non-existent packages, and supply chain red flags that general code scanners were not designed to catch.
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Catch AI-specific security patterns
Flag insecure code shapes that repeatedly appear in AI-generated code, including weak auth, unsafe deserialization, and broken secret handling.
Detect hallucinated packages early
Stop slopsquatting and broken dependency chains by verifying that every generated package reference is real, trusted, and expected.
Fit into existing CI workflows
Layer AI-code checks into pull requests and pipelines so teams can keep moving without adding another manual review queue.
Comparison
| Workflow need | Generic scanning | AI-code audit layer |
|---|---|---|
| Hallucinated dependencies | Traditional scanners assume the package exists and focus on known CVEs. | Verify generated dependencies exist, are maintained, and match an approved registry path before install. |
| AI-shaped insecure code | Catch some issues after code is merged or deployed. | Flag recurring AI-generated weakness patterns at review time so teams fix them before merge. |
| Developer adoption | Security review feels separate from the coding workflow. | Run checks inside pull requests and CI where vibe-coding teams already work. |
Frequently Asked Questions
Why do teams need AI-specific code scanning if they already use SAST?
General scanners are valuable, but they were not built for hallucinated packages, repeated AI-generated anti-patterns, or the review speed of AI-assisted coding workflows.
What is slopsquatting?
It is the supply chain risk created when AI tools suggest packages that do not exist and attackers later publish malicious versions under those names.
Where should this run in the workflow?
The best place is early: in pull requests, dependency verification, and CI. That keeps insecure generated code from becoming the new default baseline.
Add an AI-code security layer
Review AI-generated code with checks designed for vibe-coding risk, not just traditional hand-written codebases.
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