Understanding LLMs Artificial intelligence (AI) has reached new heights with the emergence of Large Language Models ( ). These models, trained on extensive internet data, possess the ability to generate remarkably human-like responses to user prompts. In this two-part series, we delve into two distinct applications of LLMs: the security posture of open-source LLM projects and the US military's trials of classified LLMs. To comprehend the significance of these developments, it is vital to grasp the fundamental concepts of AI, LLMs, and open source. LLMs Reshaping Digital Content Creation Open Source LLM projects have revolutionized the digital content landscape, pushing the boundaries of what machines can create. These projects have garnered immense popularity, evidenced by the number of GitHub stars they accumulate. However, the rapid growth of LLMs brings forth critical concerns regarding their security risks and vulnerabilities. Open-source LLM projects often exhibit an immature security posture, which necessitates the adoption of enhanced security standards and practices, as shown by the recent , demonstrating: Rezilion Study on the top 50 Open Source LLM Project Security Risks with an average of Open Source LLM Projects are , extremely popular 15,909 stars with an average age of This is a major spike in open source interest, because these projects are still , extremely immature 3.77months The unique combination of early-stage project development and massive community interest in this emerging technology has resulted in a with an average score of very poor security posture 4.60 out of 10 The projects with the most stars have the worst security scores on average, reporting that “the most popular GPT-based project on GitHub, Auto-GPT, has over 138,000 stars, is less than three months old, and has a Scorecard score of 3.7. Github Stars are Stupid, As Usual: The Urgency for Improved Security Measures: The popularity of LLMs in open-source projects makes them prime targets for attackers. As these powerful models gain traction, it becomes increasingly crucial to address the security challenges they present. Organizations must prioritize security considerations when selecting software solutions involving LLMs. By doing so, they can mitigate risks such as bypassing access controls, unauthorized resource access, system vulnerabilities, and potential compromise of sensitive information or intellectual property. The Role of OpenSSF Scorecard in Enhancing Security Standards To address the security challenges associated with open-source LLM projects, a robust framework known as the can prove invaluable. Developed by the Open Source Security Foundation (OSSF), the Scorecard is an automated SAST (Static Application Security Testing) tool that evaluates project security by assigning scores to various security heuristics or checks. By utilizing the Scorecard, developers can assess the risks associated with project dependencies, make informed decisions, and collaborate with maintainers to address security gaps. This framework also facilitates the comparison of similar projects, enabling organizations to prioritize security considerations and select the most secure options. OpenSSF Scorecard The Security State of Open Source LLM Projects: An in-depth analysis of the security posture of open-source LLM projects sheds light on the pressing issues that need to be addressed. By examining statistics such as the number of GitHub stars and the level of security awareness within these projects, it becomes apparent that many of them are in their nascent stages of development. This immaturity leaves them vulnerable to potential security breaches. Despite their popularity, open-source LLM projects often lack the necessary security measures, making them susceptible to exploitation by malicious actors. Open-source LLM projects have ushered in a new era of digital content creation. However, the rapid growth of these projects has given rise to security challenges that must be urgently addressed. By implementing enhanced security standards and practices, organizations can safeguard against bypassing access controls, unauthorized resource access, and potential compromises of sensitive information. The OpenSSF Scorecard framework serves as a valuable tool for evaluating and improving the security posture of open-source LLM projects. Now for Some Cool Stuff: Now you’ve got a good grounding in the unique cybersecurity of open-source large language models. In the next part of this series, we will explore the trials of classified LLMs by the US military, highlighting their potential impact on security considerations in A Tale of Two LLMs: Open Source vs the US Military's LLM Trials .

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Securing AI: Concerns & Immune Systems for Emerging Technologies

A Tale of Two LLMs: Open Source vs the US Military's LLM Trials

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Security Threats to High Impact Open Source Large Language Models

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