# llm
16 articlestagged with “llm”
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.