AI Attacks & Threats: What are they and how do they work?

Artificial intelligence (AI) is undoubtedly changing the world as we know it. But while this emerging technology has many benefits, it also brings the possibility of AI cyber-attacks. Bad actors can use AI to increase the effectiveness of existing threats and create new attack vectors. To stay ahead of the curve, businesses must consider how to protect their systems against various AI attacks.

Check out our guide to the OWASP LLM Top 10 for a deep dive into AI risks.

What is an AI attack?

Bad actors can manipulate an AI system deployed by the organization to serve a malicious purpose. This attack occurs when a bad actor finds limitations in a machine learning (ML) model and exploits them.

Threat actors can also use AI to drive their offensive attack technology. They use AI to automatically generate a higher volume of attacks or generate exploits themselves, making it more difficult for organizations to protect themselves.

Why CISOS need to be aware of AI attacks

CISOs must consider both threats — the greater volume of offensive attacks driven by AI technology and the possibility of their own systems getting used against them. As businesses leverage AI for more critical infrastructure, it becomes increasingly important to defend against AI attacks.

How do AI attacks work

Bad actors can leverage AI systems on their end for an offensive attack, enabling them to exploit organizations on a much larger scale. AI empowers attackers to create more convincing phishing and social engineering schemes, making staff members more likely to fall for these ploys. 

Attackers use AI to enhance the following attack methods:

• LLMJacking: the unauthorized hijacking or manipulation of a large language model (LLM) to gain control, extract sensitive data, or alter its behavior.

• Phishing: Attackers can leverage Generative AI to create targeted phishing emails, making these emails more believable to victims.

• Malware & vulnerability discovery: Malicious actors can also use AI to find system exploits and create malware. For instance, recent research proved that ChatGPT can generate dynamic, mutating versions of malicious code. This new technology lowers the bar for cyber attackers by enabling novices to carry out sophisticated exploits. 

• Social engineering: Attackers can use deep fake technology to generate audio and video of a familiar, trustworthy person, convincing targets that they are this person.

What are the most common AI attacks?

Along with enhancing existing attack vectors, AI allows bad actors to create new methods that pose unprecedented risk to today’s organizations. 

7 Strategies to prevent AI attacks

While the world of AI might seem intimidating, there are seven practical tactics that businesses can follow to start defending themselves against AI cyberattacks: 

• Leverage AI for defense. Businesses can use AI tools for security best practices such as code analysis, knowledge base maintenance, and security posture modeling.

• Use suitable AI models for your use case. If your AI models are precise and tailored to their intended purposes, it is less likely that these models will be exploitable. 

• Implement employee training on cyber risk awareness. Because AI techniques such as deepfake technology can make social engineering much more believable, employees should receive in-depth training on identifying and avoiding cyber risk.

• Build out in-depth defense measures. To respond to emerging sophisticated threats—especially AI attacks—businesses must rely on more than one layer of security for their projects and applications.

• Leverage “temperature flags.” These indicators define the level of creativity that a model output provides. Higher temperature makes it harder to get repeat outputs from AI models. Businesses should use temperature flags to walk the line between too little creativity and too much freedom and risk.

• Secure your AI-based applications. While application security should permeate every area of app development, it’s crucial to focus on securing AI-based apps.

• Follow AI AppSec best practices.

How Snyk can help prevent AI attacks

As AI becomes a core part of application development, securing the entire AI lifecycle is critical. Snyk's developer-first security platform provides comprehensive visibility and control to protect your AI-based applications from the ground up.

Securing the AI-Generated Code

A common misconception is that code written by an AI assistant is inherently more secure. This just isn't true. AI models are trained on vast datasets, and if that data includes vulnerable code, the model may inadvertently introduce the same security flaws. Snyk Code is built to address this.

Utilizing multiple fine-tuned AI models and security-specific data curated by top security specialists, DeepCode AI is specifically designed to understand and analyze code, whether it's written by a human or an AI. It scans your code in real-time within your IDE, flagging vulnerabilities as they appear. 

Additionally, Snyk Agent Fix can autonomously generate and apply a secure fix with a single click, allowing developers to maintain their speed without sacrificing security. This ensures you're not unknowingly introducing new vulnerabilities when you use AI to boost productivity.

Protecting Your AI Applications

Beyond securing the code itself, Snyk also helps protect the AI applications you're building. AI systems have unique attack vectors, such as prompt injection or model theft. Attackers can manipulate a model's input to force it to behave in unintended ways or steal the intellectual property of a trained model.

Snyk's platform provides end-to-end visibility and governance across your AI-native applications. Snyk can:

• Provide an AI-BOM (AI-Bill of Materials): This gives you a complete inventory of the components, data sources, and models that make up your AI application, so you can track and manage risks.

• Identify vulnerabilities in dependencies: AI-based applications rely on a complex web of open-source libraries. Snyk Open Source scans these dependencies, ensuring you're not inheriting vulnerabilities from third-party packages.

• Enforce security policies: With Snyk, you can create and enforce security policies to ensure that your AI development workflows comply with organizational and industry standards, giving you peace of mind that your applications are built on a foundation of trust.

Ready to accelerate security in the age of AI?

The threats are evolving, and your defenses must too. Learn how to adopt a proactive security strategy that leverages AI for defense and helps you build more resilient applications. 

Download our cheat sheet, Modern DevSecOps: 6 Best Practices for AI-Accelerated Security, to get started.