AI-SPM vs Mozilla 0DIN AI Scanner

AI-SPM (AI Security Posture Management) is an open-source infrastructure layer for securing LLM pipelines running in production. It targets three attack surfaces that traditional application security doesn't cover: prompt injection (including obfuscated and multi-step variants), tool abuse via unvalidated structured outputs, and data exfiltration through PII leakage in model responses. The architecture layers a gateway intercept layer over incoming prompts, runs context inspection before the LLM sees any input, enforces policies via Open Policy Agent (OPA) for declarative, auditable rules, then pipes all events through Apache Kafka and Apache Flink for real-time streaming analysis. This means security posture can be monitored and enforced at scale without blocking the inference path. The project is genuinely fresh — posted as a Show HN today. Early community feedback pointed to capability-based token models (similar to OS kernel permission rings) as a complementary approach to content-scanning, which the author acknowledged as a meaningful gap. The timing is right: as companies push AI agents from demos to production, the security tooling layer is largely underdeveloped. AI-SPM is one of the first OSS projects to tackle it at the infrastructure layer rather than with prompt-level guardrails alone.

Mozilla's AI security team — 0DIN (Zero Day Investigation Network) — open-sourced their internal LLM vulnerability scanner on April 10, 2026. Unlike synthetic red-teaming tools, this is built on real attack knowledge: 0DIN researchers have spent two years getting paid to break every major frontier model, discovering and reporting thousands of verified vulnerabilities. Those discoveries are now encoded as reproducible probes. Built on NVIDIA's GARAK open-source framework, the 0DIN Scanner adds a graphical interface, automated scan scheduling, cross-model comparative analysis, and enterprise reporting. It ships with 179 community probes covering 35 vulnerability families — prompt injection, jailbreaks, data leakage, harmful content generation, and more — all aligned to the OWASP LLM Top 10. Six specialty probes target advanced threat categories. For any team deploying LLMs in production — RAG systems, agents with tool access, customer-facing chatbots — this is now the baseline for security auditing. The Apache 2.0 license means enterprise deployment without legal headaches. With LLM security audits running $50K-$200K from specialist firms, this democratizes access to professional-grade testing.