Prompt injection, guardrails, sandboxing, code execution, and red-team practice.
8 articles
Systematic comparison of NVIDIA NeMo Guardrails (Colang-based procedural guardrails) and Guardrails AI (Pydantic-based structured output validation) across protection layers, interception timing, integration, and production maintainability. Includes a decision tree by scenario, scale, and compliance requirements.
Systematic guide to rate limiting and cost attack defense for LLM Agents in production: single-user token quotas, multi-tenant sliding windows, prompt injection amplifying cost, DDoS protection, and integration with LangChain/CrewAI/AutoGen frameworks.
Why do LLM agents hallucinate? This article traces root causes and systematically reviews practical mitigation patterns: retrieval augmentation, confidence scoring, multi-agent cross-validation, forced citation backtracking, and observability with UpTrain, Giskard, RagaAI Catalyst, Comet Opik, and NVIDIA Garak.
Based on OWASP LLM Top 10 engineering practice, this article systematically explains the seven layers of defense-in-depth for agent prompt injection: input sanitization, instruction isolation, least-privilege, output auditing, guardrails frameworks, continuous red-teaming, and kill switches -- with actionable code and toolchains.
A side-by-side comparison of five sandbox technologies, weighing latency, security, and ops cost.
Five-layer defense plus red-team loop, built on five open-source projects you can copy.
A systematic walkthrough of three major attack surfaces in AI agents, with practical code examples for prompt injection defense, tool permission scoping, and output filtering.
Comparing container, WebAssembly, and process-level isolation approaches, with practical code for safely executing agent-generated code.