AI: The New Attack Surface – The New Cybersecurity Risks Presented by AI Adoption

As well as the new cybersecurity capabilities that AI makes possible, there is also the dimension that AI itself creates new security risks.

AI has revolutionized industries, offering unprecedented capabilities and efficiencies, however the adoption of AI also introduces new cybersecurity risks that organizations must address to safeguard their data and systems.

As IBM describes AI is the New Attack Surface, and security expert Jeff Crume explains what kinds of attacks you can expect to see, how you can prevent or deal with them, and three resources for understanding the problem better and building defenses.

AI Security Risks

AI systems rely on vast amounts of data to learn and make decisions. This dependence on data raises concerns about data privacy and security. Organizations must ensure that sensitive information is protected throughout the AI lifecycle, from data collection to model deployment.

Adversarial attacks target AI systems by manipulating input data to deceive the algorithms. These attacks can lead to misclassification, compromising the integrity and reliability of AI-powered applications. Defending against adversarial attacks requires robust security measures and continuous monitoring.

As organizations embrace AI technologies, it is essential to recognize and mitigate the cybersecurity risks associated with their adoption. By implementing robust security practices, staying informed about emerging threats, and fostering a culture of cybersecurity awareness, businesses can harness the benefits of AI while safeguarding their digital assets.

LLM Threats

To zoom in on specific areas of AI risks we can consider LLMs. Large Language Models (LLMs) have revolutionized natural language processing, but they also pose significant cybersecurity risks. Understanding these risks is crucial for organizations leveraging LLM technology.

In their Youtube video All About AI identifies the top five LLM risks, and Martin Holste, CTO Cloud at Trellix, builds on this to define and discuss the OWASP Top 10 vulnerabilities for LLM apps and what they mean to organizations, whether they are currently using any AI or not.

Data privacy is a major concern when using LLMs due to the vast amounts of data they process. Organizations must ensure that sensitive information is not exposed or misused during training or inference.

• Adversarial Attacks: LLMs are susceptible to adversarial attacks, where malicious actors manipulate input data to deceive the model. This can lead to incorrect outputs or compromised security.
• Bias and Fairness Issues: LLMs may exhibit biases based on the training data they receive, leading to unfair or discriminatory outcomes. Addressing bias and ensuring fairness is essential for ethical AI deployment.
• Model Poisoning: Model poisoning involves injecting malicious data into the training process to manipulate the LLM’s behavior. This can result in compromised performance and security vulnerabilities.
• Security Vulnerabilities: LLMs can introduce security vulnerabilities if not properly secured. Weaknesses in the model architecture or implementation can be exploited by attackers to gain unauthorized access or manipulate outputs.

Prompt Injection Attacks

To focus another degree further we can review ‘Prompt Injection Attacks’.

A Prompt Injection Attack is a type of security vulnerability that occurs when an attacker injects malicious code into a prompt dialog box on a website. This can lead to various consequences, such as stealing sensitive information, executing unauthorized actions, or compromising the integrity of the website.

In a Prompt Injection Attack, the attacker manipulates the prompt dialog box that appears on a website to execute malicious code. This can be achieved through various means, such as exploiting input fields, forms, or other interactive elements that trigger a prompt dialog.

As Nvidia writes this is a new attack technique specific to large language models (LLMs) that enables attackers to manipulate the output of the LLM. This attack is made more dangerous by the way that LLMs are increasingly being equipped with plug-ins for better responding to user requests by accessing up-to-date information, performing complex calculations, and calling on external services through the APIs they provide.

Cobus Grayling shares insights via this Youtube video and this Medium article, where he explains using the ChainForge IDE to batch test and measure prompt injection detection.

Protecting your website against Prompt Injection Attacks is crucial to maintain its security and integrity. Here are some steps you can take to safeguard your website:

• Implement Input Validation: Validate all user inputs to ensure they are safe and do not contain any malicious code.
• Sanitize User Inputs: Filter and sanitize user inputs to remove any potentially harmful content before processing them.
• Use Content Security Policy (CSP): Implement a CSP to control which resources can be loaded on your website and mitigate the risk of injection attacks.
• Escape User Inputs: Escape special characters in user inputs to prevent them from being interpreted as code.
• Regular Security Audits: Conduct regular security audits to identify and address any vulnerabilities in your website’s code.

By following these best practices and staying vigilant against potential threats, you can significantly reduce the risk of falling victim to Prompt Injection Attacks and protect your website and users from harm.