We’ve just launched a service built on top of alias1, an open-source cybersecurity LLM focused on static analysis, misconfiguration detection and security reasoning.
The goal isn’t to replace pentesters but to make high-throughput security analysis more accessible to individual practitioners, not only companies. The service runs with:
- 10M tokens/month
- 20 sustained RPM
- 50k TPM burst
- access to the alias1 model
- Discord community support
This release also comes after the same underlying agent (CAI) ranked first in the NeuroGrid CTF organized by Hack The Box, outperforming several autonomous agents based on general-purpose LLMs.
I’m sharing it here because I’d be interested in feedback from HN’s security and engineering community regarding:
- practical use cases for LLMs in security workflows
- where specialized models outperform general LLMs
- where blind spots still remain
Not trying to sell anything — genuinely interested in technical feedback on specialized vs. general-purpose LLMs for security tasks.
The goal isn’t to replace pentesters but to make high-throughput security analysis more accessible to individual practitioners, not only companies. The service runs with:
- 10M tokens/month - 20 sustained RPM - 50k TPM burst - access to the alias1 model - Discord community support
This release also comes after the same underlying agent (CAI) ranked first in the NeuroGrid CTF organized by Hack The Box, outperforming several autonomous agents based on general-purpose LLMs.
I’m sharing it here because I’d be interested in feedback from HN’s security and engineering community regarding: - practical use cases for LLMs in security workflows - where specialized models outperform general LLMs - where blind spots still remain
Not trying to sell anything — genuinely interested in technical feedback on specialized vs. general-purpose LLMs for security tasks.