One of the biggest challenges of generative AI is the need for more transparency in how generative AI models work. These models are said to be complex and their decision-making process opaque. This makes it difficult to understand how these AI models generate code and identify potential security weaknesses in the output.
For example, a programmer used a GenAI tool to create a login function. The generated code might work as intended on the surface. However, a developer will never know if the GenAI model has introduced any hidden vulnerabilities due to its internal working.
Malicious actors could potentially exploit vulnerabilities within the generative models themselves or trick them into generating code with hidden security flaws. This could involve manipulating the training data or crafting specific prompts to influence the AI output.
For example, an attacker finds out how to overload a GenAI model with a specific prompt, causing it to generate code with a backdoor vulnerability that allows unauthorized access.
We know that GenAI models can learn from existing code but may not always understand the minute differences between coding and secured coding practices. The generated code may lack proper security measures, making it an entry for attackers.
For example, a GenAI model might generate code that doesn't properly sanitize user input, leaving the application vulnerable to SQL injection attacks.
You should know that AI-written codes can be more complex when compared with human-written. This can make it challenging for human programmers to review and audit the code for security vulnerabilities, potentially delaying the identification and mitigation of risks.
As we said earlier, Generative AI models can be trained based on datasets. Similarly, we can train our AI models on vast datasets on security vulnerabilities and attack patterns. This allows them for potential threats in application designs during the early planning stages. By simulating attacks and analyzing weaknesses, AI models can guide developers toward more secure architectural choices.
Read more about Threat Modeling here.
Maybe not the entire code, but AI models can assist in generating code snippets with built-in security best practices. For instance, it could suggest secure coding patterns or identify common pitfalls to avoid during development. This can improve the overall security posture of the codebase.
Generative AI can be used to create a wider variety of automated security tests. GenAI can help you automatically generate test cases that target different attack vectors and scenarios, GenAI can also help uncover vulnerabilities that traditional static analysis tools might miss.
Even if you have configured your system for security, GenAI can help you optimize that and suggest improvements. Identifying weaknesses or redundant settings can help optimize security controls and ensure they are aligned with best practices for the specific application architecture.
AI models can be leveraged to assist penetration testing teams by creating customized test scripts or simulating specific attacker behaviors. This can streamline the testing process and uncover hidden vulnerabilities that might be difficult for manual penetration testing alone.
AI can now be integrated into security monitoring systems to analyze network traffic and application logs for suspicious activity. By continuously learning and adapting, GenAI models can potentially detect novel attacks or zero-day vulnerabilities that traditional signature-based detection might miss.