Meta Unveils 'Purple Llama': A Comprehensive AI Security Toolkit for Developers
Summary:
Meta unveils "Purple Llama," a toolkit with safety measures for generative AI models. This suite of tools is dedicated to assisting developers to construct secure and safe applications using generative AI models. The term "Purple Llama" is derived from the combined strategies of "red teaming" (proactive attacks to identify faults) and "blue teaming" (reactive strategies to mitigate threats). The toolkit includes metrics for threat quantification, frequency assessment of insecure code suggestions, and continuous evaluations to combat cyberattacks. The primary intention is to reduce insecure codes and undesired outputs in the model pipeline, mitigating the exploitation opportunities for cybercriminals.
December 7 saw the reveal of a set of tools designed for the security and benchmarking of generative artificial intelligence models, courtesy of Meta. The toolkit, referred to as “Purple Llama”, is set up to assist developers in securely and safely constructing with generative AI tools, including Llama-2, an open-source model from Meta. The goal of Purple Llama is to level the playing field for the construction of safe and responsible generative AI experiences. It includes licensable tools, evaluations, and models for commercial use and research.
The term “Purple” in “Purple Llama” ties to a combination of “blue teaming” and “red teaming”, as per a reverse from Meta. Red teaming adopts a strategy where developers or internal testers launch attacks against an AI model purposefully to identify potential errors, faults, or undesired outputs and interactions. This practice enables developers to strategize against potential harmful attacks and defend against security or safety faults. Blue teaming serves as the comparative opposite, wherein developers or testers counteract red teaming attacks to identify necessary strategies for mitigating real-world threats against client or consumer models in production.
Meta indicates that fusing both defensive (blue team) and attacking (red team) positions is key to truly tackle challenges that generative AI introduces. Purple-teaming, therefore, combines the responsibilities of both teams and presents a combined effort to evaluate and minimize potential risks.
Meta claims that the first suite of cyber security safety evaluations for Large Language Models (LLMs) has been unveiled through this release. It includes metrics to quantify LLM cyber security risk, tools to assess the frequency of insecure code suggestions, and tools to evaluate LLMs to increase the difficulty in generating harmful code or assisting in cyber attacks. The main objective is to incorporate the system into model pipelines to decrease insecure codes and undesired outputs while concurrently limiting model exploitations' benefits for cyber criminals. The initial release, as the Meta AI team states, aims to provide tools to tackle risks highlighted in the White House commitments.
Published At
12/7/2023 9:30:00 PM
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