/mar 4, 2024

The Risks of Automated Code Generation and the Necessity of AI-Powered Remediation

By Natalie Tischler

Modern software development techniques are creating flaws faster than they can be fixed. While using third-party libraries, microservices, code generators, large language models (LLMs), etc., has remarkably increased productivity and flexibility in development, it has also increased the rate of generating insecure code. An automated and intelligent solution is needed to bridge the widening gap between the introduction and remediation of flaws.  

Let’s explore the potential dangers of modern methods of automated code generation and the need for a secure and automated mode of flaw remediation.  

Automated Methods That Produce Insecure Code  

Code Generators  

These tools can generate code based on specific inputs or templates that developers provide, such as feature specifications, design patterns, or other parameters. This accelerates development cycles, reduces errors, and maintains consistency across an application. Examples include Swagger Codegen, Yeoman, and SQLalchemy's Alembic

However, without rigorous validation, the generated code could present several security risks. These include: 

  • Static code patterns happen when code generators produce predictable, static code patterns that attackers can quickly identify and exploit.  

  • Code generators might also include outdated libraries or frameworks that contain unpatched vulnerabilities, which can be introduced into software.  

  • Insufficient input validation within the generated code might not properly validate user input, opening the potential for injection attacks such as SQL injection, Cross-Site Scripting (XSS), or Command Injection

Library and Framework Usage  

Libraries and frameworks containing pre-written code for everyday tasks are widely used among developers. Examples are React.js for UI, TensorFlow for machine learning, and Bootstrap for front-end development. They significantly speed up the development process and expose your code to potential security threats if incorrectly used.  

Significant risks associated with this method chiefly revolve around dependency vulnerabilities. Libraries and frameworks often rely on numerous dependencies, which could have hidden flaws or security bugs adopted into your application. Dependencies that are not regularly updated or audited become easy targets for attackers. Furthermore, if developers are unaware of the internal workings of these tools, it can lead to improper implementation that opens security gaps. 

Large Language Models 

Probably the most hyped method is the use of LLMs. This utilizes an AI language model trained on extensive web content to assist developers in generating code; this practice entails potential security and legal risks. One example is intellectual property infringement, as LLMs can generate code that mirrors or is similarly structured to proprietary code from public repositories. This could result in complex legal challenges concerning code ownership and copyright. 

Other issues include the generation of insecure code. LLMs might generate code that lacks necessary security measures, thus introducing vulnerabilities into your software. Code could also suffer from over-reliance on common patterns, which, while practical, might not align with best security practices or the needs of a given project. 

Software Composition  

Software Composition refers to developers building applications by assembling pre-existing components. Python packages, npm packages in Node.js, and Maven dependencies in Java are a few examples. Security concerns arise when components are not regularly updated or flawed components are used. 

There's also the risk of complex dependency trees, where an application uses a library which relies in turn on a on other dependencies. A single vulnerability within this chain can affect the entire application. As such, identifying the source can be challenging, making mitigation or remediation efforts difficult.  

Automated Refactoring Tools  

These tools automatically refactor existing code to improve its structure without changing its behavior. Tools such as ReSharper for .NET and JRefactory for Java allow developers to streamline their codebase, enforce best practices and naming conventions, and remove redundant code.  

However, these tools may introduce subtle bugs due to the complexity of code patterns and intricacies that automated tools cannot comprehend. Furthermore, automated refactoring tools are not fully aligned with the unique contextual needs of a project, which can result in inappropriate refactorings that negatively impact the performance or maintenance of the software.    

AI-Powered Flaw Remediation with Veracode Fix 

With the overwhelming number of flaws introduced through the many methods that produce insecure code, an automated process is necessary to remediate flaws and vulnerabilities quickly and securely.  

Veracode Fix is an AI-powered tool that allows developers to remediate flaws without manually writing a single line of code. Flaws that take months can be resolved within minutes, drastically saving labor costs and time to ensure speedy delivery. 

To give an idea of its efficiency, in controlled tests, Veracode Fix reduced the remediation time for a CWE-117 vulnerability in a Java application from 35 minutes to just 3 minutes. 

Responsible by design, Veracode Fix is not trained on open-source code, code in the wild, or customer data, but it delivers 17 years of security best practices from Veracode's proprietary dataset to your fingertips. It is a proprietary closed-loop AI system trained on Veracode's knowledge base. Hence, remediations cannot generate compromised or sensitive information. 

The Efficacy of Veracode Fix 

Eight months since its launch, Veracode Fix has shown efficacy in resolving 80% of vulnerabilities in Java and 75% in C#. It is also effective in fixing 65% of issues in Python, 60% in JavaScript, and 55% in PHP. Over time, Veracode Fix will continue to increase its capability to address flaws and vulnerabilities. 

As a specialized AI remediation tool, Veracode Fix generates fixes almost instantly, accelerating remediation times and supercharging your SDLC. To learn more about Veracode Fix and its transformative capabilities for your organization's CI/CD pipelines, click here

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By Natalie Tischler

Natalie Tischler believes in a world where software is built secure from the start. She writes content for Veracode that focuses on empowering harmony between Security and Development teams.