最新博客文章
5 / 200AI 开发实战教程，从零到一构建你的 AI 应用。
All200
Beginner32
Intermediate92
Advanced76
All
Prompt Engineering12
RAG Retrieval Augmented17
Agent Development34
Model Fine-tuning21
Deployment and Operations25
AI Application Development40
DeepSeek API16
Multimodal Development6
AI Safety6
Model Inference3
Retrieval Systems1
RAG Retrieval1
Model Architecture3
API Development1
Agent Development10
Model Deployment1
Multimodal1
Model Evaluation2
Tutorial
Date
DeepSeek Harness Sandbox & Approval: Containing Risky Operations
AI Safety
Advanced
How dsh decides if an operation can run: two-stage sandbox decisions and approval flow, read-only/workspace-write/full-access permissions, approval context and timeouts, plus security rules for Plugin developers.
AI Data Compliance and Privacy Computing: An Engineering Guide
AI Safety
Advanced
Data classification and minimization become gates; masking, pseudonymization, and anonymization diverge legally; k-anonymity and t-closeness buy and cost; TEE, homomorphic encryption, federational learning, and secret sharing differ in trust; RAG vector stores and retrieval logs leak; deletion rights, cross-border transfer, audit logs; memorization risk in fine-tuning.
AI Agent Red Teaming and Security Assessment Framework
AI Safety
Advanced
Agents that can call tools are at risk of being exploited. This article systematically explains Agent red team testing methodologies, covering attack surfaces such as prompt injection, unauthorized tool calls, supply chain pollution, as well as supporting automated evaluation and defense systems.
Prompt Injection: Indirect Attacks, Tool Poisoning, and Data Exfiltration
AI Safety
Advanced
Once agents read web pages and documents, prompt injection becomes an architecture problem, not a model problem. This article separates direct from indirect injection at the trust boundary, then walks four channels: injection in retrieved content, tool poisoning in MCP descriptions, multimodal injection in images, and exfiltration paths. It ends with a layered defense model.
Automated AI Red Teaming: From Manual Adversarial Work to Continuous Evaluation
AI Safety
Advanced
Automated red teaming yields an attack surface map and a regression suite, not one exploit. It maps injection, tool poisoning, multimodal payloads, and privilege abuse; drives mutation and evolutionary search from few seeds to variants under convergence control; automates scoring in three layers: rules, model judges, human review; freezes attacks as CI regression tests.
