Technical Content
First Line of Defense for cq
cq helps coding agents share resolution paths and learn from past failures. We partnered with Lauren Mushro to bring VIBE✓ into cq and help review knowledge units before they enter shared memory.
Daniel Nissani
AI Safety Engineer at Mozilla.ai who explores automated LLM evaluations and AI safety.
Technical Content
Integrating Alinia into any-guardrail for Multilingual AI Security
The newest integration with any-guardrail: Alinia AI, whose security models are specifically built to detect threats like prompt injection, data exfiltration, and policy violations by understanding the cultural and linguistic nuances of multilingual AI interactions.
Technical Content
Evaluating Multilingual, Context-Aware Guardrails: Evidence from a Humanitarian LLM Use Case
A technical evaluation of multilingual, context-aware AI guardrails, analyzing how English and Farsi responses are scored under identical policies. The findings surface scoring gaps, reasoning issues, and consistency challenges in humanitarian deployments.
Technical Content
Can Open-Source Guardrails Really Protect AI Agents?
AI Agents extend large language models beyond text generation. They can call functions, access internal and external resources, perform deterministic operations, and even communicate with other agents. Yet, most existing guardrails weren’t built to protect these operations.
Product Release
Introducing any-guardrail: A common interface to test AI safety models
Building AI agents is hard, not just due to LLMs, but also because of tool selection, orchestration frameworks, evaluation, safety, etc. At Mozilla.ai, we’re building tools to facilitate agent development, and we noticed that guardrails for filtering unsafe outputs also need a unified interface.