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

Dive into Elastic's exploration of embedding security directly within Large Language Models (LLMs). Discover our strategies for detecting and mitigating several of the top OWASP vulnerabilities in LLM applications, ensuring safer and more secure AI-driven applications.

Proxying LLM Requests and Setup

Quick Start Option: Proxy with Flask

Langsmith Proxy

LLM Potential Attacks and Detection Rule Opportunities

LLM01 - prompt injection

LLM02 - insecure output handling

LLM04 - model DoS

LLM06 - sensitive information disclosure

Enriching Detection Rules with Security Insights

Alternative Options for LLM Application Auditing

Leveraging Elastic APM for In-Depth Application Insights

Utilizing OpenTelemetry for Enhanced Observability

LLM Auditing with Kibana

Closing Thoughts

Exploring Elastic's innovative approach for integrating security into the lifecycle of LLMs to safeguard against vulnerabilities featuring Elastic’s AI Assistant.

Embedding Security in LLM Workflows: Elastic's Proactive Approach

Learn more about how Elastic Security Labs has been focused on accelerating our detection engineering workflows by tapping into more generative AI capabilities.

The ONWeek Exploration Odyssey

A New Horizon: Generating Investigation Guides

Charting the Future: Next Steps 

Accelerating Elastic detection tradecraft with LLMs

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.

Using LLMs to summarize user sessions

What is a session?

Data processing

Hallucinations

Parameter tuning

Evaluating GPT Summaries

Recently, OpenAI announced APIs for engineers to integrate ChatGPT and Whisper models into their apps and products. For some time, engineers could use the REST API calls for older models and otherwise use the ChatGPT interface through their website.

Preamble

Simple use case: ELI5

Malicious Behavior Prevention alert

Alert prioritization

Sample alerts

Generic response

Generic response with field details

Prioritizing recommendation

Reduced sample alerts by rule name

Prioritized alert

Sample alerts with varying severity & risk score

Prioritized alert with details

Small discussion on LLMs for undesirable outcomes

Try it yourself!

Ambitious applications of ChatGPT to improve detection, response, and understanding

Exploring the Future of Security with ChatGPT

Generative AI

주제

생성형 AI

2024년 5월 6일엔

Elastic, 표준화된 필드와 통합으로 LLM 보안 강화

Elastic의 선제적 접근: LLM 워크플로우에 보안을 내재화

LLM으로 Elastic 탐지 기술 가속화

LLM과 ESRE로 유사 사용자 세션 탐색

LLM을 사용하여 사용자 세션 요약하기

ChatGPT를 통한 보안의 미래 모색