The Security Risks and Benefits of AI/LLM in Software Development
Artificial Intelligence
AI refers to the knowledge in a model that you're going to apply to tasks. Under the AI term, there are two types:
- Specific AI - Concentrated on a particular functionality
- Generic AI - AI that has many use cases
Think of AI-powered customer service agents that understand and respond to user queries, as a good example.
Machine Learning
Machine learning is computer software learning to adapt without explicate instructions from humans. This can be broken into two types:
- Unsupervised Learning - Unlabeled data that AI defines and tries to gain insight into relationships within that data
- Supervised Learning - Clean, structured, labeled data from a data prep phase to train the AI
An example of ML is Automated testing, where systems learn from previous test cases to generate new test scenarios and identify potential bugs.
Large Language Models
A specialized type of AI model trained on massive amounts of text data to understand and generate human-like language. LLMs excel at tasks such as language translation, summarization, and question-answering.
An example of an LLM is natural language interfaces that enable users to interact with software using everyday language instead of complex commands.
Benefits of Using AI/LLM in Secure Coding
How AI and Security Work Together
According to Insight, 72% of business leaders believe that implementing AI will enhance their teams' productivity.
Artificial intelligence is swiftly reshaping the software development field. AI-powered tools are becoming essential for developers, aiding in everything from generating code snippets to identifying vulnerabilities. This transformation holds the promise of increasing productivity and enhancing code quality.
AI/ML/LLM Can Be Used To Generate Secure Code
AI/ML/LLMs have emerged as powerful tools for aiding in the generation of secure code, contributing to a more proactive approach to application security. Here are some ways they are used:
- Code Completion and Suggestions - AI-powered code assistants provide real-time secure code suggestions and improvements, leveraging vast codebases to identify vulnerabilities and ensure best security practices.
- Automated Code Generation - AI/LLMs can create secure code from natural language descriptions, saving time and letting developers focus on complex tasks.
- Security-Focused Code Review - AI/ML models can detect security flaws, suggest fixes, and provide insights to improve code security, while LLMs enhance code readability and maintainability with comments and explanations.
- Vulnerability Prediction and Prevention - AI/ML models can predict potential vulnerabilities in new code and suggest secure coding alternatives, allowing developers to proactively address security risks.
- Secure Code Refactoring - AI/ML-powered tools can assist developers in refactoring existing code to enhance security. These tools can suggest safer implementations and automatically perform code transformations while preserving functionality.
Nearly 40% of developers have indicated they are concerned that AI-generated code may introduce security vulnerabilities, according to GitLab.
As developers adopt these advanced AI tools, it's essential to understand that they are not a cure-all for security issues. While AI can assist in identifying and mitigating certain risks, it cannot substitute for human expertise and a robust security mindset.
Read About What Your Devs Are Doing with AI and How it Impacts Your Software Security
To ensure the safe and effective use of AI in development, it's crucial to prioritize secure coding practices and invest in ongoing education and skill enhancement.
Potential For AI/ML/LLM To Introduce New Vulnerabilities In Software Code
AI/LLMs offer tremendous benefits for software development, but they also introduce potential risks and can inadvertently introduce new vulnerabilities into code:
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Inaccurate, Insecure, or Contextually Blind Code Generation - AI models can generate code that is incorrect, contains vulnerabilities, or is inappropriate for the specific context, leading to unexpected behavior and potential security risks.
For instance, an AI model might generate code that is syntactically correct but semantically incorrect, leading to unintended consequences or vulnerabilities. Additionally, AI models might not fully understand the context of the code they are generating, leading to the inclusion of unnecessary or harmful code elements.
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Hidden Dependencies and Vulnerabilities - AI-generated code might introduce security risks by including outdated or vulnerable third-party libraries, as well as transitive dependencies that can bring in additional vulnerabilities. These hidden dependencies can be difficult to identify and mitigate, as they might not be directly visible in the generated code.
For example, an AI model might suggest using a popular library that is known to contain vulnerabilities, without considering the potential risks associated with using it.
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Lack of Domain Expertise - AI models might not have the same level of domain expertise as human developers, leading to the generation of code that is not optimized for performance, security, or maintainability.
For example, an AI model might generate code that is inefficient or difficult to understand, making it more prone to errors and harder to maintain.
Hackers Using AI/LLM For Malicious Purposes
AI/LLMs, while offering significant benefits to software development, can also be misused for malicious purposes, posing serious security risks. Here are some of the concerning ways they can be exploited:
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Malware Creation and Exploit Development - AI/LLMs can be used to generate sophisticated malware, including polymorphic variants that evade traditional security tools. Additionally, given information about vulnerabilities, AI/LLMs can potentially aid in creating exploits, lowering the barrier of entry for attackers who might not have the technical expertise to develop exploits manually.
For instance, AI/LLMs can be used to create new malware families or variants of existing malware, making it more difficult for security teams to detect and respond to threats.
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Evasion and Obfuscation - AI/LLMs can be used to generate code or modify malware in ways that evade detection by traditional security tools and conceal malicious intent, making attacks more stealthy and difficult to detect. Additionally, AI/LLMs can be used to generate polymorphic malware that changes its appearance with each execution, making it more difficult for security tools to detect and block.
For example, AI/LLMs can be used to generate obfuscated code that is difficult for humans to understand and analyze, making it more challenging for security teams to identify malicious activity.
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Supply Chain Attacks - AI/LLMs can be used to identify vulnerabilities in software supply chains and exploit them to compromise downstream applications.
For example, AI/LLMs can be used to identify third-party libraries or components that contain vulnerabilities and then target these components to gain access to the systems that use them.
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