Elastic Security Labs Releases Guidance to Avoid LLM Risks and Abuses
Product controls and SOC countermeasures to securely adopt LLMs
Generative AI and LLM implementations have become widely adopted over the past 18 months, with some companies pushing to implement them as quickly as possible. This has expanded the attack surface and left developers and security teams without clear guidance on how to adopt emerging LLM technology safely.
“For all their potential, broad LLM adoption has been met with unease by enterprise leaders, seen as yet another doorway for malicious actors to gain access to private information or a foothold in their IT ecosystems,” said
The LLM Safety Assessment builds and expands on the
In addition to 1000+ detection rules already published and maintained on
“Normalizing and standardizing how data is ingested and analyzed makes the industry safer for everyone — which is exactly what this research intends to do,” said King. “Our detection rule repository helps customers monitor threats with confidence, as quickly as possible, and now includes LLM implementations. The rules are built and maintained publicly in alignment with Elastic’s dedication to transparency.”
Additional Resources
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Blog: Elastic Advances LLM Security with Standardized Fields and Integrations
- Explores the creation of integration workflows to reduce friction when assessing LLM security and details a new integration with AWS Bedrock
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Blog: Embedding Security in LLM Workflows: Elastic's Proactive Approach
- Highlights suggestions and examples of how to detect malicious LLM activities with ES|QL, and proposes a proxy based telemetry solution.
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Blog: Accelerating Elastic detection tradecraft with LLMs
- Focuses on Elastic Security Labs’ dedication to LLM research in the context of streamlining detection workflows with generative AI
About Elastic
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