Cerbere-AG 為 AI 智能體提供安全防護、可觀測性與控制層
Cerbere-AG -- The layer that ensures security, observability, and control, which sits between the AI agent and the real world
Christopher dikesa 開發 Cerbere-AG，這套 AI 智能體安全防護以 Regex、ML 和 LLM Judge 三層檢查工具呼叫。系統會允許、阻止或標記呼叫供人即時批准；作者稱，測試中單用 Regex 會讓超過 30% 的攻擊通過，ML 在部分測試的召回率達 100% 但會增加誤報，LLM Judge 則處理模糊個案並參照上下文。
Cerbere-AG is a three-layer security guardrail for AI agents: Regex, ML, and an LLM Judge.
AI agents are unpredictable. They improvise and act with full autonomy. I built Cerbere-AG (named after the three-headed dog, Cerberus) so that autonomy doesn’t come at the cost of control.
Why three layers? Our benchmarks show it:
→ Regex alone lets more than 30% of attacks through.
→ ML pushes recall up to 100% in some of our tests, but raises false positives.
→ The LLM Judge resolves the ambiguous cases ML can’t classify.
It also looks at context. Cerbere-AG follows tool calls, checks whether they’re legitimate, and then allows them, blocks them, or flags them for real-time human approval. Agents keep their autonomy, and humans keep control.
Everything is audited too. The collector gathers spans and traces into an audit trail that is independent from the agent and supports compliance.
And like any tool, it has limits. That’s why observability matters: security isn’t only about blocking attacks, it’s about understanding what happened, what was allowed, what was blocked, and why.
Open source and free for now. Try it and give me your honest feedback 👇
https://github.com/chrismsmr-celcom/cerbere-AG
https://app.cerbereag.site
來源：DEV Community · dev.to