AI tool comparison
ElevenAgents Guardrails 2.0 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.
AI Safety & Governance
ElevenAgents Guardrails 2.0
Real-time safety controls for voice agents — stop drift, injection, and off-brand behavior
75%
Panel ship
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Community
Free
Entry
ElevenAgents Guardrails 2.0 is a safety layer built on top of ElevenLabs' voice agent platform, designed for enterprises deploying customer-facing AI voice agents at scale. The core problem it solves: voice agents in production tend to drift, get manipulated through prompt injection, or go off-brand in ways that only surface after something embarrassing happens. Version 2.0 adds three main capabilities: real-time policy enforcement that monitors agent behavior as it happens, prompt injection protection against users trying to manipulate the agent's instructions, and configurable custom rules that enterprises can tailor to their specific compliance or brand requirements. Unlike static guardrails baked into the system prompt, these operate as a live enforcement layer during conversations. The timing matters. As more enterprises put voice agents on their phone lines and websites, the "what could go wrong" list has gotten longer — agents giving wrong pricing, going off-script with sensitive customers, or being jailbroken into saying things they shouldn't. Guardrails 2.0 positions ElevenLabs not just as a voice synthesis platform but as an enterprise-safe agent runtime.
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
“Static system prompt guardrails are a band-aid. Having a live enforcement layer that can catch drift and injection attempts as they happen is the right architecture for anything customer-facing. This is the kind of tooling that makes it reasonable to deploy voice agents in sensitive contexts like healthcare or finance.”
“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.”
“Guardrails as a paid add-on to your voice agent platform is a strange model — safety shouldn't be upsold. Also, ElevenLabs controlling both the voice synthesis and the safety layer means there's no independent verification that the guardrails are actually working. That's a dangerous single point of trust for enterprise compliance purposes.”
“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.”
“Voice agents are the new customer service reps, and companies are learning the hard way that they need guardrails. This is the beginning of a whole category: real-time behavioral safety systems for AI agents. The team that solves this at scale — across providers, not just ElevenLabs — will be enormous.”
“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.”
“Brand safety for voice is genuinely underserved. Written AI outputs can be reviewed and filtered; voice interactions happen in real time with no undo. Knowing your agent won't say something off-brand to a live customer is worth paying for, especially for high-volume contact centers.”
“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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