In a dynamic cybersecurity landscape where threats are constantly evolving, staying ahead of potential code vulnerabilities is vital. One promising way is to integrate AI and Large Language Models (LLMs). The use of these technologies can help to early detect and mitigate vulnerabilities in libraries that have not been identified before, enhancing the overall security of software applications. Or, as we like to say, “finding unknown unknowns”.
For developers, implementing artificial intelligence to identify and fix software vulnerabilities has the potential to increase productivity by reducing the time spent finding and fixing coding errors, helping them achieve the desired “state of flow”. However, there are some things to consider before an organization adds LLM to its processes.
Unlocking the flow
One of the benefits of adding LLM is scalability. Artificial intelligence can automatically generate patches for multiple vulnerabilities, reducing the number of vulnerabilities and providing a more optimized and accelerated process. This is especially useful for organizations that are struggling with multiple security issues. The volume of vulnerabilities can overwhelm traditional scanning methods, leading to delays in addressing critical issues. LLMs allow organizations to comprehensively address vulnerabilities without being held back by resource constraints. LLMs can provide a more systematic and automated way to reduce flaws and strengthen software security.
This leads to the second benefit of AI: efficiency. Time is of the essence when it comes to finding and fixing vulnerabilities. Automating the process of patching software vulnerabilities helps minimize the window of vulnerability for those hoping to exploit them. This efficiency also contributes to significant time and resource savings. This is especially important for organizations with a large code base, allowing them to optimize their resources and allocate efforts more strategically.
The ability of LLMs to learn from a huge dataset of secure code creates a third benefit: the accuracy of these generated patches. The right model builds on its knowledge to provide solutions that meet established security standards, increasing the overall resilience of the software. This minimizes the risk of new vulnerabilities during the patching process. BUT these data sets can also create risks.
Navigating trust and issues
One of the biggest drawbacks of using AI to fix software vulnerabilities is reliability. Models can be trained with malicious code and learn patterns and behaviors associated with security threats. When a model is used to generate patches, it can draw on its acquired experience, inadvertently suggesting solutions that may create security vulnerabilities rather than fix them. This means that the quality of the training data must be appropriate for the code to be patched AND free of malicious code.
The ability of LLMs to learn from a huge dataset of secure code creates a third benefit: the accuracy of these generated patches. The right model builds on its knowledge to provide solutions that meet established security standards, increasing the overall resilience of the software. This minimizes the risk of new vulnerabilities during the patching process. BUT these data sets can also create risks.
Navigating trust and issues
One of the biggest drawbacks of using AI to fix software vulnerabilities is reliability. Models can be trained with malicious code and learn patterns and behaviors associated with security threats. When a model is used to generate patches, it can draw on its acquired experience, inadvertently suggesting solutions that may create security vulnerabilities rather than fix them. This means that the quality of the training data must be appropriate for the code to be patched AND free of malicious code.
LLMs can also have the potential to introduce bias in the fixes they generate, leading to solutions that may not cover the full range of possibilities. If the dataset used for training is not diverse, the model can develop narrow perspectives and preferences. When tasked with generating software vulnerability patches, it may favor certain solutions over others based on patterns established during training. This bias can lead to a patch-centric approach that potentially ignores non-traditional but effective ways to address software vulnerability issues.
Although ML models do a great job of recognizing patterns and creating solutions based on learned patterns, they can fail when faced with unique or novel problems that differ significantly from the training data. Sometimes these models can even “hallucinate” generating false information or incorrect code. Generative AI and LLMs can also be fussy when it comes to hints, meaning that a small change in what you type can lead to significantly different code results. Attackers can also take advantage of these patterns by using quick injections or data poisoning training to create additional vulnerabilities or gain access to sensitive information. These challenges often require a deep understanding of the context, sophisticated critical thinking skills, and an awareness of the broader system architecture. This underscores the importance of human expertise in managing and verifying results and why organizations should consider LLM as a tool to augment human capabilities rather than replace them entirely.
The human element remains important
Human oversight is crucial throughout the software development lifecycle, especially when using advanced AI models. While Generative AI and LLM can perform tedious tasks, developers must maintain a clear understanding of their end goals. Developers need to be able to analyze the intricacies of a complex vulnerability, consider broader systemic implications, and apply subject matter expertise to develop effective and tailored solutions. This specialized expertise allows developers to tailor solutions that meet industry standards, compliance requirements, and specific user needs, factors that cannot be fully captured by AI models alone. Developers also need to conduct thorough validation and verification of AI-generated code to ensure that the generated code meets the highest standards of security and reliability.
Combining LLM technology with security testing is a promising way to improve code security. However, a balanced and cautious approach that recognizes both the potential benefits and risks is important. By combining the strengths of this technology with human expertise, developers can proactively identify and mitigate vulnerabilities, improving software security and maximizing the productivity of engineering teams by allowing them to better determine the state of the flow.