Discover Elastic’s latest advancements in LLM security, focusing on standardized field integrations and enhanced detection capabilities. Learn how adopting these standards can safeguard your systems.

Introduction

Integrating LLM logs into Elastic

Vendor selection and API capabilities

Elastic AWS Bedrock model integration

Overview

Configuring integration data collection method

Collecting logs from S3 buckets

Collecting logs from CloudWatch

Integration installation

Configuring Bedrock Guardrails

The importance of ECS mappings

Broader Implications and Benefits of Standardization

Sample LLM-specific fields: AWS Bedrock use case

LLM detection engineering examples

Basic detection of sensitive content refusal

Potential denial of service or resource exhaustion attacks

Monitoring for latency anomalies

Advanced LLM detection engineering use cases

Malicious model uploads and cross-tenant escalation

Unauthorized URLs and external communication

Hierarchical instruction prioritization

Extended detections featuring additional Elastic rule types

Summary

Explore How Elastic's New LLM Security Strategies Enhance Detection, Standardization, and Protection Across the LLM Ecosystem

Elastic Advances LLM Security with Standardized Fields and Integrations

In our previous article, we explored using the GPT-4 Large Language Model (LLM) to condense Linux user sessions. In the context of the same experiment, we dedicated some time to examine sessions that shared similarities. These similar sessions can subsequently aid the analysts in identifying related suspicious activities.

Using LLMs and ESRE to find similar user sessions

Leveraging GPT for Session Categorization

Lessons learned

Prompt engineering

Few-shot tuning

Evaluating GPT Categories

Semantic search with ELSER

What's next

In this publication, we will talk about lessons learned and key takeaways from our experiments using GPT-4 to summarize user sessions.

作者

Susan Chang

文章

Elastic 通过标准化字段和集成推进 LLM 安全性

使用 LLM 和 ESRE 查找相似的用户会话

使用 LLM 总结用户会话