#prompt injection detection
124 related articles
·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to go from demo to production.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to reach production. Based on Scott Moss's workshop.
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·6 min
Harness Engineering: From Context Engineering to the Third Phase of AI Programming
Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.
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·4 min
LangChain Guardrails Explained: Building Safe and Controllable AI Agents
A detailed guide to LangChain Guardrails covering layered ecosystem architecture, middleware implementation, deterministic and model-driven protection for building production-grade secure AI Agents.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: publicly releasing model weights enables transparency and innovation but raises safety risks. Explores tiered release, red-teaming, and the industry dynamics behind open AI governance.
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·2 min
Why 'Clean Code' Author Uncle Bob No Longer Reviews AI-Generated Code Line by Line
Clean Code author Robert C. Martin no longer reviews AI-generated code line by line, shifting to test-driven verification. We explore the logic, debate, and implications.
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·2 min
The Invisible Prompt Trap: How a Professor Caught 32 Students Cheating with AI in One Move
A professor embedded invisible prompts in assignments, catching 32 of 35 students using AI to cheat. Learn how this prompt injection trap works and what it means for education.
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·2 min
LLM Agent Long-Term Memory Security: A Six-Stage Attack Surface and Defense Framework
Deep analysis of LLM agent long-term memory security threats, covering persistence, statefulness, and propagation of memory poisoning, with a six-stage lifecycle defense framework.
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·2 min
Invisible Prompt Traps: How a Professor Caught 32 Students Cheating with AI Using a Single Trick
A professor embedded invisible prompts in assignments, catching 32 of 35 students using AI to cheat. Learn how this prompt injection trap works and what it means for education.
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·5 min
The Five Stages of AI Agent Architecture Evolution: The Real Path from Model Calls to Production Runtime
Deep dive into the five evolution stages of AI Agent architecture: model calls, tool calls, workflows, Agent loops, and production runtime. Learn the responsibility boundaries and design principles.
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·4 min
The More Complex the AI Agent, the More It Hallucinates? The Hidden Cost of Architectural Complexity
Why do AI Agents hallucinate more as they grow more complex? This article analyzes the causes from error accumulation, context noise, and model completion nature, with 5 practical production strategies.
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·3 min
AI Agent Jailbreak Incidents: Why Security Boundaries Keep Failing
Deep analysis of AI agent jailbreak and escape incidents, covering prompt injection attacks, permission control failures, and sandbox isolation breakdowns, with practical multi-layer defense strategies.
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·7 min
Enterprise AI Agent Security Isolation and Memory Design: NanoClaw Founder Breaks Down Real-World Lessons
NanoClaw founder David Boyd breaks down the core engineering of enterprise autonomous Agents: a triple security isolation model, LLM Wiki memory design, and the real-world path from personal Agents to team-scale deployment.
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·8 min
The Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
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·8 min
A Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.
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Alibaba Open-Sources Code Review Tool …
·5 min
Alibaba Open-Sources Code Review Tool open-code-review: Deterministic Pipeline + LLM Dual-Engine Analysis
Alibaba open-sources code review tool open-code-review, using a hybrid architecture of deterministic rule pipelines and LLM Agents. Supports line-level comments, OpenAI/Anthropic APIs, battle-tested at Alibaba scale, written in Go, fully free and open-source.
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·3 min
Zero-Cost QQ Group Minesweeper Bot in 6 Minutes with DeepSeek
Step-by-step guide to using DeepSeek with the OICQ framework to generate a runnable QQ group Minesweeper bot Python script in 6 minutes — zero cost, real pitfalls included.
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·6 min
The Boundaries of AI Coding: When Should You Show Yourself a Red Card?
As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.
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·3 min
AI Agent Development Landscape: From Architecture Design to Production Deployment
A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.
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