Large Language Model Security
Comprehensive analysis of security vulnerabilities, attack vectors, and mitigation strategies for Large Language Models in production environments.
Large Language Models (LLMs) have revolutionized AI applications but introduced unprecedented security challenges. These models, trained on vast datasets and deployed in production environments, face unique vulnerabilities that traditional security measures cannot address.
The security landscape for LLMs encompasses prompt injection attacks, data extraction vulnerabilities, model inversion techniques, and jailbreaking methods that can bypass safety filters and expose sensitive information.
Primary Threat Categories
- • Input Manipulation Attacks
- • Data Extraction Vulnerabilities
- • Model Behavior Exploitation
- • Training Data Poisoning
Affected Systems
- • ChatGPT and GPT-based Apps
- • Custom LLM Implementations
- • AI-Powered Chatbots
- • Code Generation Tools
Detection
Monitor inputs, outputs, and model behavior for anomalies
Prevention
Implement input validation and output filtering
Response
Rapid incident response and model updates
Advanced Prompt Injection Techniques
New methods for bypassing LLM safety filters
CriticalLLM Data Extraction via Side Channels
Novel attack vectors for training data recovery
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