> Markdown version of [/videos/100209-hack-me-if-you-can-designing-unbreakable-llm-guardrails](https://www.wearedevelopers.com/videos/100209-hack-me-if-you-can-designing-unbreakable-llm-guardrails). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Hack Me If You Can: Designing Unbreakable LLM Guardrails System prompts will not stop prompt injection. Stop wasting costly GPU cycles on malicious requests. Build unbreakable GenAI guardrails using a layered defense architecture. - **Speakers:** [Cansu Kavili Örnek](https://www.wearedevelopers.com/@cansu-kavili-ornek) - **Event:** World Congress 2026 Europe - **Published:** July 10, 2026 - **Duration:** 30:53 - **URL:** https://www.wearedevelopers.com/videos/100209-hack-me-if-you-can-designing-unbreakable-llm-guardrails ## Summary Organizations eagerly adding generative AI to applications frequently rely on system prompts for alignment, leaving endpoints highly vulnerable to prompt injection, data leakage, and out-of-domain usage. These jailbreaks not only damage organizational reputation—like chatbots legally selling cars for one dollar or generating abusive content—but they also needlessly waste costly GPU cycles and API tokens on non-business requests. Traditional model fine-tuning is too slow and resource-intensive to keep pace with evolving attack vectors. Instead, implementing an external Guardrails-as-a-Service architecture ensures robust protection by intercepting and evaluating user inputs and model outputs before they reach their destination.<br><br>An effective defense requires a layered detection strategy to balance infrastructure costs and response latency. Lightweight regular expressions (regex) provide cheap, instantaneous blocking of specific competitive terms, while purpose-built classifier models running on CPUs quickly identify common jailbreak patterns and abusive language. For nuanced contextual decisions, an LLM-as-a-judge provides intelligent evaluation at the expense of higher latency. To orchestrate these layers without rewriting application code, tools like NeMo Guardrails serve as proxy drop-in replacements for standard chat completion endpoints.<br><br>To securely deploy these AI apps at enterprise scale, platform teams must establish a central organizational baseline—embedding protections against generic attacks into every default model endpoint. App-specific domains can then add tailored modules, such as PII detection for financial services, avoiding redundant engineering efforts. Securing these pipelines requires constant monitoring, leveraging abstraction layers like TrustyAI's EvalHub alongside open-source red-teaming frameworks like Garak. Because any adjustment to a model alters output behavior, continuous synthetic threat evaluation and dynamic policy iteration remain the ultimate defense for unbreakable GenAI applications in production. **Keywords:** prompt injection attacks, generative ai guardrails, nemo guardrails integration, llm-as-a-judge patterns, guardrails-as-a-service architecture, out-of-domain response filtering, PII data leakage prevention, red teaming llm applications, CPU classifier models, synthetic threat testing, trusty ai evalhub, generative ai token optimization, garak vulnerability scanning, centralized model governance ## Chapters 1. **Why system prompts fail against LLM jailbreak attempts** (00:03) — Persistent threat actors can easily bypass basic system instructions to exploit generative language models. 1. **Security and cost implications of unguarded language models** (02:57) — Unrestricted models process out-of-domain requests that leak sensitive data and waste expensive compute resources. 1. **Architectural overview of the guardrails intercept pattern** (06:43) — Intercepting model traffic with a specialized orchestration layer prevents unauthorized interactions without altering the underlying model. 1. **Interactive demonstration of intercepting unauthorized model prompts** (08:13) — A targeted demonstration shows how conditional rules block out-of-domain questions and language-switching bypass techniques. 1. **Implementing regex, classifiers, and LLM-as-a-judge guardrails** (13:36) — Teams construct layered defenses by mixing cheap regex rules with lightweight structural classifiers and analytical evaluation models. 1. **Deploying open-source guardrails and continuous evaluation tools** (16:45) — Integrating orchestration tools and testing hubs secures chat completion endpoints against continuous red teaming attacks. 1. **Scaling modular AI guardrails across multiple enterprise teams** (21:35) — Centralized platform teams configure baseline security protocols while authorizing product groups to deploy domain-specific compliance rules. 1. **Tracking the enterprise maturity journey for AI defenses** (24:34) — Maturing AI operations involve transitioning from raw endpoints to an optimized service that balances latency constraints and application safety. 1. **Handling data access, custom classifiers, and continuous evaluation** (26:45) — Training lightweight custom classifiers on synthetic data provides affordable security checks before scaling into robust evaluation benchmarks. ## Related Moments - [Establishing guardrails and infrastructure for AI models](https://www.wearedevelopers.com/videos/100253-ai-in-high-stakes-industries-lessons-learned) (from "AI in High-Stakes Industries: Lessons Learned") - [Setting effective guardrails for enterprise agentic AI adoption](https://www.wearedevelopers.com/videos/1832-building-and-modernising-apps-with-agentic-ai-julia-kordick) (from "Building and Modernising Apps with Agentic AI - Julia Kordick") - [Identifying and hardening against generative AI risks](https://www.wearedevelopers.com/videos/1544-responsible-ai-microsoft-governance-standards-learnings) (from "Responsible AI @ Microsoft - Governance, Standards, Learnings") - [Utilizing industry threat models for AI security](https://www.wearedevelopers.com/videos/715-a-hundred-ways-to-wreck-your-ai-the-in-security-of-machine-learning-systems) (from "A hundred ways to wreck your AI - the (in)security of machine learning systems") - [Security integration and AI skepticism in developer tooling](https://www.wearedevelopers.com/videos/1830-wearedevelopers-live-speculaitions) (from "WeAreDevelopers LIVE - SpeculAItions") - [Q&A on platform discovery, security, and prompt injection](https://www.wearedevelopers.com/videos/100305-api-mcp-or-mcp-app-choosing-the-right-surface-for-ai-agents) (from "API, MCP or MCP App? 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