AI tool comparison
Mozilla 0DIN AI Scanner vs qsag-core
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.
Security
qsag-core
Open-source security scanner for AI agents — catches MCP poisoning and prompt injection
50%
Panel ship
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Community
Free
Entry
qsag-core is a fresh open-source Python toolkit from Neoxyber that addresses the OWASP Top 10 for Agentic Applications 2026 — specifically the two fastest-growing attack vectors: MCP tool poisoning and prompt injection in AI agents. The library uses pattern-based detection (not ML-based, to minimize false positives) to scan 26 MCP tool poisoning patterns across 7 categories and detect 28+ prompt injection patterns across 9 threat categories. It also catches ghost agent attempts, credential harvesting, and memory poisoning in real time. The toolkit is available on PyPI, ships with cryptographic attestations, and is licensed under Apache 2.0. It was created in early April 2026, making it genuinely new-to-the-scene. The timing is significant: a recent Dark Reading poll found 48% of cybersecurity professionals now identify agentic AI as the #1 attack vector, up from a niche concern in 2025. Microsoft released a similar (but much larger-scope) Agent Governance Toolkit in early April, which validates the problem space but leaves room for nimble open-source tooling. qsag-core is early-stage — zero stars on GitHub as of today, minimal community traction, and no documented production deployments. But it addresses a problem that's going to become critical as MCP adoption accelerates. First-mover advantage in a niche that's about to explode.
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.”
“I've been looking for exactly this since MCP started proliferating. Pattern-based detection over ML is the right call for security tooling — I can audit what it's flagging and why. Dropping this into my agent pipeline CI was a 30-minute job. The MCP tool poisoning scanner alone is worth it.”
“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.”
“Zero stars, no known production deployments, no security audit of the security tool itself — that's an uncomfortable situation. Pattern-based detection will generate false positives as MCP tool definitions grow more complex, and attackers who know about this scanner can trivially evade it. Treat as research, not production security.”
“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.”
“MCP security is going to matter enormously as AI agents gain real-world tool access. The OWASP Top 10 for Agentic Applications is brand new and most teams haven't even read it. Getting familiar with these attack patterns now, before an incident forces the conversation, is table-stakes security hygiene.”
“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.”
“Unless you're running AI agents in production that use MCP tools, this is highly specialized developer/security tooling. Relevant context for understanding AI agent risks, but not something most creatives will interact with directly.”
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