Elastic Security Labs releases guidance to avoid LLM risks and abuses
Elastic Security Labs releases the definitive guide for securing LLM adoption with mitigation recommendations and InfoSec countermeasures
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Elastic Security Labs aims to democratize knowledge and support you in the fight against threat actors. Best known for our library of over 100 in-depth articles and our annual Global Threat Report, we’re elated to bring you our newest publication: the LLM Safety Assessment!
Exploring the best mitigations
Generative AI and large language model (LLM) implementations have become widely adopted in the last year and a half, 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 safely adopt this emerging technology. That’s why the team at Elastic Security Labs has pulled together a new research publication for you and your organization.
This publication explores generative AI and LLM safety, providing security teams with crucial information to protect their LLM implementations, including in-depth explanations of risks and the suggested best practices to mitigate attacks. From insights for those new to LLMs to in-depth explanations on the OWASP Top Ten, this publication explores and recommends countermeasures for common abuses.
When asked about the report, Jake King — head of threat and security intelligence at Elastic Security — said:
For all their potential, broad LLM adoption has been met with uneasiness by enterprise leaders, seen as yet another doorway for malicious actors to gain access to private information or a foothold in their IT ecosystems. Publishing openly available security research is in our DNA at Elastic Security — we firmly believe security knowledge is for everyone and safety is in numbers, and our hope is that all organizations, whether they are Elastic customers or not, can take advantage of our rules, guidance, and the high quality research from our team.
The countermeasures explored in this publication cover different areas of the enterprise architecture — primarily in-product controls that developers must adopt while building LLM-enabled applications and information security measures that the SOC must add to verify and validate secure usage. These are suggestions that we’ve implemented into the Elastic AI Assistant and other AI-driven workflows in Elastic’s product suite.
For those looking to expand their understanding of risk mitigation while leveraging Elastic Security, we have also introduced several detection rules to improve your current LLM security posture, specifically targeting mitigating risks around prompt and response actions. We share these in an open and transparent manner with the hope that these practices demonstrate how Elastic customers can take advantage of our knowledge base as well as our out-of-box detection rules.
Building upon a foundation of threat research
With recent advancements to our detection rules and brand new, formal research, it is important to consider that advancements in our understanding of risks are derived from expertise across months of research in this emerging field. This has been coupled with our deep understanding of the security risks impacting many environments today and published not only in our newest report but also across our body of work.
Our perspective on high level risks is paired with insights as to how Elastic is positioning our focus around traditional detection engineering concepts. These ideas and others are present in many of our Elastic Security Labs publications, including:
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
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
Accelerating Elastic detection tradecraft with LLMs focuses on our dedication to LLM research in the context of streamlining detection workflows with generative AI
These publications and others across our unique blog space aim to inform, inspire, and prepare our audience for what’s to come.
Protect your organization
Elastic Security Labs is dedicated to providing crucial, timely security research to the intelligence community — regardless of if you’re an Elastic customer or not. When we normalize and standardize, we make the industry safer for everyone — which is exactly what we intend this research to do.
For our customers, our detection rule repository exists to help you monitor confidently as quickly as possible — and now includes your LLM implementations. These rules are built and maintained publicly in alignment with Elastic’s dedication to transparency.
It’s a good day to frustrate a threat actor. Check out the free report.
The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.
In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use.
Elastic, elasticsearch, ESRE, elasticsearch Relevance Engine and associated marks are trademarks, logos or registered trademarks of elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.
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