Elastic (NYSE: ESTC), a leader in Search AI, has released “LLM Safety Assessment: The Definitive Guide on Avoiding Risk and Abuses,” a comprehensive report by Elastic Security Labs. This guide addresses safety concerns related to large language models (LLMs) and offers best practices and countermeasures to mitigate potential abuses.
As generative AI and LLMs have surged in adoption over the past 18 months, developers and security teams face increased risks and lack clear guidelines for safe implementation. Jake King, head of threat and security intelligence at Elastic, emphasized the importance of accessible security knowledge to protect against threats posed by LLMs.
The LLM Safety Assessment expands on the Open Web Application Security Project (OWASP) research, detailing common LLM attack techniques, associated risks, and mitigation strategies. The guide includes product controls for developers and security measures for Security Operations Centers (SOCs) to ensure secure LLM usage.
Elastic Security Labs has also introduced new detection rules specifically for LLM abuses, adding to the over 1000 detection rules already available on GitHub. These new rules help organizations monitor and address LLM-related threats effectively.
Asjad Athick, Cyber Security Lead for Asia Pacific and Japan at Elastic, highlighted the importance of standardized data ingestion and analysis to enhance industry safety. The new detection rules for LLMs allow customers to efficiently monitor threats and maintain secure environments amidst the rapid integration of LLM technology into business applications.