AI tools are increasingly used to generate code, but traditional security scanners often miss specific vulnerabilities unique to AI-generated code. These vulnerabilities include hallucinated packages, prompt injection surfaces, insecure LLM output handling, and overly permissive agent configurations. Developers using AI tools may unknowingly introduce these vulnerabilities into their projects, leading to potential security risks.
Pain Points
- Traditional security scanners do not detect AI-specific vulnerabilities.
- Developers may not be aware of the unique security risks posed by AI-generated code.
- Lack of tools specifically designed to scan and secure AI-generated code.
- Potential security risks due to hallucinated packages and prompt injection surfaces.
Alright let me have it. I've been working on [Oculum](https://oculum.dev) which is basically a security scanner specifically for code generated by AI tools (Cursor, Bolt, Lovable, Copilot etc). It checks for stuff traditional scanners miss: hallucinated packages, prompt injection surfaces, insecure LLM output handling, overly permissive agent configs, that kind of thing. CLI + GitHub Action, 40+ detection categories, has a free tier, The pitch is basically: Snyk and SonarQube catch classic vulns but don't know what a system prompt is. AI tools ship the same insecure patterns over and over. Oculum catches the gap. Where I think I'm vulnerable (pun intended): * still in beta so detection coverage has blind spots for sure * landing page could probably use work, or just Web pages overall, have not been focusing on those much * no autonomous fix suggestions yet, just detection * competing in a space where Snyk has like a billion dollars Roast the product, the site, the positioning, whatever. Genuinely want the honest feedback, I'd rather hear it here than figure it out the hard way.
A security scanner specifically for code generated by AI tools. It checks for vulnerabilities that traditional scanners miss, such as hallucinated packages, prompt injection surfaces, insecure LLM output handling, and overly permissive agent configurations.