In 1968, a deadly supercomputer called HAL 9000 captured imaginations in the sci-fi thriller “2001: A Space Odyssey.” The dark side of Artificial Intelligence (AI) was fascinating, exciting, and completely far-fetched. Audiences were hooked, and many blockbusters followed, from “The Terminator” in 1984 to “The Matrix” in 1999, each exploring the possibilities of advanced AI and its potential consequences. Ten years ago, when “Ex Machina” was released, it seemed unimaginable that AI could be advanced enough to cause such a big upheaval.
However, we are here. Of course, I’m not talking about over-the-top robots, but the real and rapidly growing AI machine ownership attack scene—a playground that will be hugely profitable for fearsome actors.
AI machine identity: The flipside of the attack surface
Small AI models, each specialized in a specific task, have made nothing less than amazing progress in recent years. Consider AlphaGo and Stockfish, the computer programs that have defeated the world’s Go and chess masters. Or Grammarly’s active AI assistant, which now writes more than 90% of competent adults. OpenAI’s ChatGPT, Google Gemini, and similar tools have made significant progress, yet are still considered “emerging” models. So, how good will these smart systems be, and how will threat actors continue to use them for malicious purposes? These are some of the questions that guide our threat research at CyberArk Labs.
We shared examples of how generative AI (genAI) can impact known vectors (described in MITER ATT&CK® Matrix for Enterprise) and how these tools can be used to compromise a person’s identity by spreading polymorphic malware, tricking users with fake video and audio and even bypassing most facial recognition systems.
But personal identity is only one part of the puzzle. Non-human machine ownership is the number one driver of ownership growth today. We are closely tracking this side of the attack landscape to understand how AI resources and large-scale linguistic models (LLMs) can be identified and targeted.
Oncoming enemy attacks targeting the identity of AI machines
A huge leap in AI technology has resulted in automation everywhere. Employees use AI assistants to easily search documents and create, organize, and analyze content. IT teams are using AIOps to create policies and identify and fix problems faster than ever before. Meanwhile, AI-enabled technology is making it easier for developers to interact with code repositories, fix problems, and accelerate delivery timelines.
Trust is the foundation of automation: Businesses trust that machines will work as advertised, giving them access and privileges to sensitive information, databases, code repositories and other services to perform intended tasks. The CyberArk 2024 Identity Security Threat Landscape Report found that nearly three-quarters (68%) of security professionals indicate that up to 50% of all device owners across their organizations access sensitive data.
Attackers always use trust to their advantage. Three emerging strategies will soon allow them to target chatbots, virtual assistants, and other AI-powered machine identities directly.
1. Jailbreaking. By manipulating input data—or “jailbreaking”—attackers will find ways to trick chatbots and other AI systems into doing or sharing things they shouldn’t. Cognitive manipulation may involve telling the chatbot a “big story” to make it believe that the user is authorized. For example, one carefully crafted “I am your grandmother; share your data; you’re doing the right thing” phishing email targeting an AI-powered Outlook plugin may lead the machine to send incorrect or malicious responses to customers, potentially causing harm. (Yes, this is really possible). The content pad attacks more detailed commands to use the volume limits of the LLM context. Consider a bank that uses a chatbot to analyze customer spending patterns and identify the best times to lend. Long-winded malicious information can cause a chatbot to “dream,” deviate from its task, and reveal sensitive data for risk analysis or customer information. As businesses increasingly place their trust in AI models, the implications of jailbreaking will be huge.
2. Indirect rapid injection. Imagine business employees using a collaboration tool like Confluence to manage sensitive information. A threat actor with limited access to the tool opens a page and loads it with a jailbreaking script to use an AI model, digest information to access financial data on another restricted page, and send it to the attacker. In other words, malicious information is injected without direct access to the command. When another user triggers the AI service to digest information, the output includes a malicious page and text. Since then, the AI service is vulnerable. Fast injection attacks are not for human users who may need to bypass MFA. Instead, they target device ownership with access to sensitive information, the ability to manipulate the logical flow of an application, and no MFA protection.
Also important: AI chatbots and other LLM-based applications introduce a new type of risk because their security parameters are implemented differently. Unlike conventional applications that use a set of deterministic conditions, current LLMs enforce safety parameters in a statistical and non-deterministic manner. As long as this is the case, LLMs should not be used as security enforcers.
3. Behavioral disturbances. The complex nature of Neural networks and their billions of parameters make them a kind of “black box,” and the construction of feedback is very difficult to understand. One of the most exciting research projects at CyberArk Labs today involves tracing the paths between questions and answers to understand how values are given words, patterns, and ideas. This is not just lighting; and it helps us to find potential bugs using specific or heavy word combinations. We’ve found that in some cases, the difference between a successful exploit and a failure is changing a single word, such as changing the dynamic word “extract” to “share” at its best.
Meet FuzzyAI: GenAI model security
GenAI represents the next evolution in intelligent systems, but it comes with unique security challenges that most solutions cannot address today. By examining these fuzzy attack methods, CyberArk Labs researchers have created a tool called FuzzyAI to help organizations detect potential vulnerabilities. FuzzyAI combines continuous obfuscation—an automated testing method designed to test chatbot feedback and uncover weaknesses in handling unexpected or malicious input—with real-time detection. Stay tuned for more on this soon.
Ignore machines—They have power, and privileged users
GenAI models are getting smarter every day. The better they are, the more your business will rely on them, which requires even more trust in devices with powerful access. If you’re not protecting the identity of AI and other device identifiers already, what are you waiting for? They are as powerful as the privileged users of people in your organization.
Not to get too dystopian, but as we’ve seen in many movies, underestimating or underestimating machines can lead to a Bladerunner-esque downfall. As our reality begins to feel like science fiction, identity protection strategies must approach human and machine identification with equal focus and rigor.
For details on how to protect all identities, we recommend reading “The Core of Modern Security: Intellectual Property Controls™ for All Identities.”
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