AI tool comparison
Mozilla 0DIN AI Scanner vs OpenAI Privacy Filter
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Security
Mozilla 0DIN AI Scanner
Battle-tested LLM security scanner from the team that broke every frontier model
75%
Panel ship
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Community
Free
Entry
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.
Privacy & Security
OpenAI Privacy Filter
Open-weight 1.5B model that detects and redacts PII with 96%+ accuracy
75%
Panel ship
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Community
Paid
Entry
OpenAI's Privacy Filter is a 1.5-billion-parameter open-weight model trained specifically for detecting and redacting personally identifiable information (PII) from text. Released today under the Apache 2.0 license, it achieves over 96% F1 score on standard PII detection benchmarks and is compact enough to run locally on consumer hardware — no API required. The model handles standard PII categories (names, emails, phone numbers, SSNs, addresses) plus context-dependent identifiers like account numbers, medical record IDs, and quasi-identifiers that become sensitive in combination. It's designed to run as a pre-processing filter before text hits larger models, letting teams handle sensitive data without sending it to the cloud. Releasing this under Apache 2.0 is a meaningful move. Most enterprise PII tools are expensive, closed, and API-gated. A small, accurate, locally-deployable open-weight model changes the economics for startups, researchers, and developers building with sensitive data. It slots cleanly into data pipelines, agent pre-processors, and document handling workflows.
Reviewer scorecard
“Every team shipping LLM features in production should be running this in CI. The OWASP LLM Top 10 alignment means it maps directly to compliance frameworks. The fact that it's built from actual vulnerabilities found in frontier models — not synthetic prompts — gives it way more credibility than competitors.”
“A 96%+ F1 PII model at 1.5B parameters that runs locally and ships under Apache 2.0 is immediately useful. Drop it at the front of any data pipeline that handles user-generated content, medical records, or financial data. The size means you can run it on CPU if needed. This is the kind of open-source release that actually changes what's practical to build.”
“GARAK-based scanners catch known vulnerability patterns, but novel attacks will always slip through static probe libraries. The graphical interface is serviceable but not polished enough for non-technical security teams. And 179 probes sounds like a lot until you realize a dedicated red teamer generates thousands of custom vectors in a day.”
“96% F1 sounds great until you're in healthcare or finance where the 4% miss rate is a compliance catastrophe. PII detection at production scale requires near-perfect recall, not just high F1. And 'context-dependent quasi-identifiers' are notoriously hard — I'd want to see the breakdown by PII type, not just the aggregate score, before trusting this in a regulated environment.”
“As LLM agents gain tool access and real-world power, security becomes existential not optional. Mozilla's decision to open-source two years of hard-won attack knowledge is a rare act of public benefit in a space dominated by consulting firms charging enterprise rates. This becomes the industry standard within 12 months.”
“The open-source PII filtering layer is missing infrastructure in the AI stack. As agents process more sensitive documents, the ability to strip PII before data hits any external model becomes critical. This is the kind of foundational tooling that enables an entire category of privacy-preserving AI applications — especially in healthcare, legal, and finance.”
“Even content teams using AI for copywriting or customer service need to know their models won't be jailbroken into producing harmful outputs. This gives non-technical managers a report they can actually present to legal. That's underrated value.”
“For anyone building tools that handle user-submitted content, this is a gift. Running PII redaction locally before storing or analyzing content is good practice that was previously too expensive to implement at scale. Apache 2.0 means no legal friction for commercial use.”
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