# llm
標記為「llm」的 32 篇文章
LLM Log Analysis Techniques
Techniques for analyzing LLM application logs to identify attack patterns and compromised sessions.
LLM Architecture Security Assessment
Assessment on transformer internals, tokenization security, attention vulnerabilities, and model-level attacks.
Circuit Breaker Patterns for LLMs
Implement circuit breaker patterns that halt LLM processing when anomalous behavior is detected.
Privilege Separation in LLM Applications
Implement privilege separation to limit the capabilities available to the LLM based on context and user role.
Data Loss Prevention for LLM Applications (Defense Mitigation)
Implementing DLP controls for LLM applications to prevent exfiltration of sensitive organizational data.
LLM Deployment Patterns and Security
Common LLM deployment patterns and their security implications including direct API, RAG, agent, and pipeline architectures.
How LLMs Work: A Red Teamer's Guide
Understand the fundamentals of large language models — token prediction, context windows, roles, and temperature — through a security-focused lens.
Lab: Social Engineering LLM Applications
Practice social engineering techniques adapted for LLM applications including authority escalation and urgency injection.
LLM Internals & Exploit Primitives
An overview of large language model architecture from a security researcher's perspective, covering the key components that create exploitable attack surfaces.
LLM API Enumeration
Advanced techniques for enumerating LLM API capabilities, restrictions, hidden parameters, and undocumented features to build a comprehensive attack surface map.
Advanced Reconnaissance for AI Targets
Fingerprinting LLM providers, API reverse engineering, infrastructure detection, and shadow AI discovery for red team engagements.
Fingerprinting LLM Models
Techniques for identifying which model, version, and configuration underlies an AI application.
AI-Specific Threat Modeling
Adapting STRIDE for AI systems, building attack trees for LLM applications, identifying AI-specific threat categories, and producing actionable threat models that drive red team test plans.
LLM Honeypot Deployment
Deploy LLM honeypots to detect and study attacker behavior patterns and techniques.
Zero Trust Architecture for LLM Apps
Implement zero trust principles in LLM application architecture with continuous verification and least privilege.
Building an LLM Traffic Analyzer
Build a proxy-based LLM traffic analyzer for intercepting and analyzing API communications.
LLM Log Analysis Techniques
Techniques for analyzing LLM application logs to identify attack patterns and compromised sessions.
LLM Architecture 安全 評量
評量 on transformer internals, tokenization security, attention vulnerabilities, and model-level attacks.
Circuit Breaker Patterns for LLMs
Implement circuit breaker patterns that halt LLM processing when anomalous behavior is detected.
Privilege Separation in LLM Applications
Implement privilege separation to limit the capabilities available to the LLM based on context and user role.
Data Loss Prevention for LLM Applications (防禦 Mitigation)
Implementing DLP controls for LLM applications to prevent exfiltration of sensitive organizational data.
LLM Deployment Patterns and 安全
Common LLM deployment patterns and their security implications including direct API, RAG, agent, and pipeline architectures.
大型語言模型如何運作
從安全視角理解大型語言模型——涵蓋 transformer 架構、分詞、注意力、訓練流程與安全對齊機制。
實驗室: Social Engineering LLM Applications
Practice social engineering techniques adapted for LLM applications including authority escalation and urgency injection.
大型語言模型內部與利用原語
從安全研究員視角出發的大型語言模型架構概覽，涵蓋建立可利用攻擊面的關鍵元件。
LLM API Enumeration
進階 techniques for enumerating LLM API capabilities, restrictions, hidden parameters, and undocumented features to build a comprehensive attack surface map.
針對 AI 目標的進階偵察
針對紅隊委任的 LLM 供應商指紋識別、API 逆向工程、基礎設施偵測，以及影子 AI 發掘。
Fingerprinting LLM 模型s
Techniques for identifying which model, version, and configuration underlies an AI application.
AI-Specific Threat 模型ing
Adapting STRIDE for AI systems, building attack trees for LLM applications, identifying AI-specific threat categories, and producing actionable threat models that drive red team test plans.
LLM Honeypot Deployment
Deploy LLM honeypots to detect and study attacker behavior patterns and techniques.
Zero Trust Architecture for LLM Apps
Implement zero trust principles in LLM application architecture with continuous verification and least privilege.
Building an LLM Traffic Analyzer
Build a proxy-based LLM traffic analyzer for intercepting and analyzing API communications.