# rate-limiting
標記為「rate-limiting」的 15 篇文章
MCP 錢包阻斷：防範符元消耗攻擊
聚焦防禦的指南，理解透過 MCP 的錢包阻斷攻擊——惡意伺服器如何製造「過度思考迴圈」導致 142.4 倍的符元放大——並實作預算控制、速率限制與成本監控以保護 LLM 端點。
Rate Limiting、Abuse 評估
評估 of rate limiting bypass技巧、成本型攻擊、billing abuse in AI services。
速率限制與濫用防護
為 LLM API 端點與應用實作速率限制與濫用防護。
速率限制、沙箱化與執行控制
AI API 的速率限制策略、以 E2B 與 Docker 進行程式碼執行沙箱化、工具呼叫核准工作流,以及 AI 代理的最小權限原則。
AI API 濫用偵測
偵測並緩解針對 AI 推論端點的 API 濫用模式，涵蓋提示詞萃取與模型盜竊。
LLM API 安全測試
LLM API 的安全測試方法論，涵蓋認證、速率限制、輸入驗證、輸出過濾與 LLM 特有的 API 漏洞。
LLM API 的速率限制策略
LLM API 的速率限制策略,包含符元預算、濫用防禦與公平性。
Lab: Rate Limit Enumeration and Bypass
Enumerate API rate limits and test common bypass 技術 including header manipulation and request distribution.
Basic Rate Limit Abuse Patterns
測試common rate-limit bypass patterns,包括 header manipulation與endpoint discovery.
AI API Reverse Engineering
Techniques for reverse engineering AI APIs including mapping undocumented endpoints, parameter discovery, rate limit profiling, and extracting implementation details from API behavior.
API 速率限制繞過
繞過 LLM 服務 API 速率限制的技術，包括標頭操控、分散式請求、認證輪換和端點探測。
Rate Limiting and Abuse Prevention for LLM APIs
導覽 for implementing rate limiting and abuse prevention systems for LLM API endpoints, covering token bucket algorithms, per-user quotas, cost-based limiting, anomaly detection, and graduated enforcement.
AI Rate Limiting 導覽
Step-by-step walkthrough for implementing token-aware rate limiting for AI applications: request-level limiting, token budget enforcement, sliding window algorithms, abuse detection, and production deployment.
Adaptive Rate Limiting for LLM APIs
Implement adaptive rate limiting that adjusts thresholds based on detected attack patterns and user behavior.
AI API 紅隊 Engagement
Complete walkthrough for testing AI APIs: endpoint enumeration, authentication bypass, rate limit evasion, input validation testing, output data leakage, and model fingerprinting through API behavior.