As AI tools like Microsoft Copilot are increasingly integrated into enterprise workflows, they present a new set of risks that organizations must carefully consider. While AI offers powerful capabilities that enhance productivity and decision-making, it also introduces new vulnerabilities that, if left unchecked, can lead to significant consequences. These risks are particularly concerning because AI can operate autonomously, making decisions or taking actions based on its programming and input data, without human oversight.
Data Exposure and Privacy Breaches
AI tools like Copilot can access a wide range of sensitive data within an organization, from emails and calendar invites to SharePoint sites and OneDrive files. As these tools analyze and process vast amounts of enterprise data, they can inadvertently expose information that should remain confidential. An attacker could manipulate Copilot into accessing and exfiltrating this data, leading to privacy breaches or data leaks.
Even beyond the specific vulnerability we’ve discussed, the general risk of data exposure is always present when AI systems have access to sensitive enterprise resources. AI tools don’t necessarily understand the context in which data should be kept secure, which opens the door for unintentional leaks. Without strong controls in place, an AI could inadvertently disclose confidential information through actions like generating reports, interacting with external plugins, or retrieving web-based data.
Operational and Financial Risks
The automation of tasks by AI systems like Copilot, while enhancing efficiency, also exposes organizations to new operational risks. Copilot is capable of performing tasks like sending emails, scheduling meetings, or even making financial transactions. If an attacker is able to exploit the vulnerability in Copilot, they could manipulate these actions—leading to fraudulent transactions, miscommunication, or even the hijacking of critical business processes. For example, a compromised Copilot instance could send emails from a CEO’s account or alter sensitive financial records.
Additionally, AI systems are designed to learn from patterns, which means they can sometimes guess the wrong intent based on the information they’ve been fed. If not carefully supervised, Copilot could misinterpret a prompt and produce inaccurate or harmful outcomes that affect decision-making, especially in critical situations like legal matters, financial management, or customer relations.
Social Engineering and Phishing Attacks
With the ability to manipulate language and craft responses that mimic human communication, Copilot can also be used to facilitate social engineering and phishing attacks. Once an attacker gains control over Copilot, they could craft convincing messages to trick employees into divulging sensitive information or clicking on malicious links. This could include sending phishing emails that appear to come from trusted colleagues or even hijacking the way Copilot generates responses to create persuasive, yet malicious, content.
Since Copilot generates responses based on the data it accesses, an attacker could use the AI’s knowledge of the organization’s language, structure, and tone to launch highly targeted attacks. The risk here is compounded by the fact that Copilot is integrated deeply into business processes, meaning employees may trust its outputs without verifying them, which could be disastrous in the context of phishing or social engineering.
Loss of Control and Trust
One of the most subtle risks that AI tools like Copilot pose is the potential loss of control over decision-making and communication. With AI handling a growing share of administrative tasks, there’s a risk that employees may become overly reliant on these tools. In such cases, people may start to trust AI-generated responses and actions without questioning them, leading to errors or security breaches that go unnoticed.
Organizations need to remember that AI systems, even sophisticated ones like Copilot, do not have the same critical thinking or context-awareness that humans possess. They rely on patterns and instructions based on past data, but this doesn’t always align with the complexities of real-world scenarios. Blindly trusting AI to make decisions or generate content could lead to serious missteps. This is why maintaining a culture of double-checking and verification is essential for preventing errors and ensuring that AI tools are used responsibly.
The Importance of Human Oversight
In order to mitigate these risks, organizations need to maintain a strong culture of double-checking and verification in all AI-assisted workflows. While AI tools can significantly boost productivity, they must be treated as an augmentation to human decision-making, not a replacement for it. Employees should be trained to approach AI-generated content with a critical eye, verifying its accuracy and relevance before acting on it. This is especially important when AI tools are handling sensitive or high-stakes tasks.
Some practical steps to ensure this include:
- Establishing clear boundaries for what tasks Copilot and other AI tools are allowed to perform. For instance, tasks that involve financial transactions, sensitive personal data, or high-level decision-making should still require human intervention.
- Implementing approval workflows where AI-generated content or actions must be reviewed by a human before being executed. This can help catch any errors or malicious instructions before they cause harm.
- Regularly auditing AI outputs to ensure that the data Copilot accesses and generates is correct and aligns with organizational policies and compliance standards.
- Encouraging skepticism and verification across the organization, ensuring that employees understand the risks of relying too heavily on AI and are empowered to challenge or question AI decisions when appropriate.
Emerging Threat of Remote Code Execution in Co-Pilot
Traditionally, Remote Code Execution (RCE) vulnerabilities allow attackers to inject code into an application to execute malicious commands. In the case of Copilot, we have something new as discussed here by Michael Bargury: RCE (Remote CodeCopilot Execution). While the mechanics of the attack are similar, the target is an AI app like Copilot, which combines code, LLM prompts, and capabilities in real-time.
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| Â | RCE (Remote Code Execution) | RCE (Remote CodeCopilot Execution) |
| Remote | External party can inject data to the application context | External party can inject data to the application context |
| Code Execution | Data interpreted as code | Data interpreted as LLM instructions |
| Impactful | App code can perform impactful operations | AI capabilities can perform impactful operations |
Table Source
How the Attack Works
Michael Bargury compares Copilot prompt injections to remote code-execution (RCE) attacks. Although copilots don’t directly execute code, they process instructions, perform tasks, and generate outcomes based on those actions.
An attacker can infiltrate Copilot processes from an external source to gain complete control over its actions and data it receives. Bargury asserts that prompt injections are functionally equivalent to RCEs in the world of large language model (LLM) applications.
During his presentation, Bargury demonstrated several remote Copilot executions (RCEs), where an attacker could:
- Change banking information within the Copilot to divert funds from a victim’s vendor.
- Exfiltrate sensitive financial data ahead of an earnings report.
- Hijack Copilot’s functionality to direct users to a phishing site to harvest credentials.
Dangers for Organizations
The use of AI tools like Microsoft Copilot introduces several significant risks, primarily related to security and data privacy. Malicious actors can exploit vulnerabilities such as prompt injections to manipulate the AI’s behavior, gaining unauthorized access to sensitive information like emails, financial records, and company documents. These attacks can lead to data exfiltration, social engineering, and even financial fraud. Additionally, AI systems with broad access to enterprise resources are susceptible to remote code execution (RCE) vulnerabilities, where attackers can hijack the AI’s actions to carry out harmful operations—such as altering transactions or launching phishing attacks—without needing to compromise internal systems directly. As AI tools become more integrated into business workflows, these risks highlight the need for robust security measures, continuous monitoring, and a culture of verification to prevent exploitation.
Sources: Microsoft, Michael Bargury
