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This release provides very important insights into mitigating risks and implementing countermeasures for large language models (LLMs) in modern cybersecurity environments.
For over a year, the adoption of generative AI and LLMs has surged, with companies racing to mold their systems to these technologies. However, this rapid implementation has expanded the attack surface, leaving developers and security teams without clear guidance.
Elastic’s Head of Threat and Security Intelligence Jake King emphasises the significance of this issue, stating, “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.”
The LLM Safety Assessment builds on the Open Web Application Security Project (OWASP) research, detailing common LLM attack techniques and offering in-depth explanations of risks, best practices, and countermeasures.
This research is crucial for information security teams aiming to protect their LLM implementations. It covers various areas of enterprise architecture, focusing on in-product controls for developers and security measures for Security Operations Centres (SOCs).
“Publishing open detection engineering content is in Elastic’s DNA. Security knowledge should be for everyone—safety is in numbers. We hope that all organisations, whether Elastic customers or not, can take advantage of these new rules and guidance,” says King.
The guide’s release is part of Elastic Security Labs’ commitment to making security knowledge accessible to all.
Elastic Security Labs has also added a set of detection rules specifically for LLM abuses, complementing the over 1,000 detection rules already available on GitHub.
Cyber Security Lead for Asia Pacific and Japan at Elastic Asjad Athick highlights the importance of these additions: “Standardising data ingestion and analysis enhances industry safety, aligning with our research goals.”
“Our detection rule repository now incorporates detections for LLMs, allowing customers to monitor threats efficiently and stay on top of issues that may affect their environment.”
With this new guide, Elastic aims to empower organisations to adopt LLM technology securely, ensuring that the integration of these advanced AI systems does not compromise cybersecurity.
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