Product controls and SOC countermeasures for secure LLM adoption
Elastic (NYSE: ESTC), a leading Search AI company, has released a new research report titled “LLM Safety Assessment: The Definitive Guide on Avoiding Risk and Abuses,” issued by Elastic Security Labs. This report explores the safety of large language models (LLMs) and provides best practices and countermeasures to mitigate potential abuses.
The widespread adoption of generative AI and LLMs over the past 18 months has increased the attack surface, leaving developers and security teams seeking clear guidance for safe implementation. Jake King, head of threat and security intelligence at Elastic, emphasized the importance of making security knowledge widely available, stating, “Security knowledge should be for everyone—safety is in numbers.”
The LLM Safety Assessment expands on the Open Web Application Security Project (OWASP) research, detailing common LLM attack techniques and offering vital information for protecting LLM implementations. It includes in-depth risk explanations, best practices, and suggested countermeasures for mitigating attacks.
The research covers various areas of enterprise architecture, focusing on in-product controls for developers and information security measures for SOCs to ensure secure LLM usage. Additionally, Elastic Security Labs has added specific detection rules for LLM abuses to its existing repository of over 1,000 detection rules on GitHub.
Asjad Athick, Cyber Security Lead for Asia Pacific and Japan at Elastic, noted the rapid integration of LLM technology into business applications and the resulting vulnerabilities. He highlighted the importance of standardizing data ingestion and analysis to enhance industry safety and keep customers informed about potential threats.
Overall, the guidance aims to help organizations, whether Elastic customers or not, adopt LLMs securely and mitigate associated risks.