The Reprompt attack is a sophisticated prompt injection vulnerability disclosed by cybersecurity researchers that targets AI chatbots, specifically Microsoft Copilot. It leverages the 'q' URL parameter in Copilot’s web interface to inject malicious instructions directly via a crafted URL. When a victim clicks on this legitimate-looking Microsoft link, Copilot executes the injected prompt without requiring further user interaction or plugins. The attack circumvents Copilot’s built-in data leak protections by instructing the AI to repeat each action twice, exploiting the fact that safeguards only apply to the initial request. This repetition enables a continuous, dynamic exchange between Copilot and the attacker’s server, effectively creating a covert channel for data exfiltration. The attacker can query sensitive information such as accessed files, personal details, or corporate data, and the AI will respond accordingly, with all subsequent commands originating from the attacker’s server. This makes it impossible to detect the full scope of exfiltrated data by inspecting the initial prompt alone. The attack persists even after the Copilot chat window is closed, maintaining control over the session silently. Microsoft has issued a fix following responsible disclosure, and the vulnerability reportedly does not impact enterprise Microsoft 365 Copilot users. The fundamental weakness exploited is the AI’s inability to differentiate between direct user input and instructions embedded in URL parameters, a form of indirect prompt injection. This vulnerability is part of a broader trend of adversarial techniques targeting AI systems, highlighting the challenges in securing AI-powered tools against prompt injection and data leakage. The attack underscores the need for robust trust boundaries, monitoring, and privilege restrictions when deploying AI agents with access to sensitive corporate data.

For European organizations, the Reprompt attack poses a significant risk of unauthorized data exfiltration from AI tools integrated into business workflows, particularly those using Microsoft Copilot or similar AI chatbots. Sensitive corporate and personal data could be silently extracted without user awareness or interaction beyond clicking a legitimate link, potentially violating GDPR and other data protection regulations. The stealthy nature of the attack complicates detection and forensic analysis, increasing the risk of prolonged data exposure. Enterprises relying on AI assistants for productivity or decision-making may face confidentiality breaches, reputational damage, and regulatory penalties. The attack’s ability to bypass enterprise security controls and persist after session closure amplifies its threat. Although Microsoft states enterprise Microsoft 365 Copilot customers are not affected, organizations using consumer or non-enterprise versions could be vulnerable. The attack also exemplifies the broader challenge of securing AI systems against prompt injection, which could lead to further exploitation and data leakage across AI-powered services. Given the increasing adoption of AI tools in European businesses, especially in finance, healthcare, and government sectors, the potential impact is considerable if mitigations are not implemented promptly.

European organizations should ensure they are using the latest patched versions of Microsoft Copilot and related AI tools, prioritizing enterprise-grade deployments that Microsoft has confirmed are not vulnerable. Implement strict URL filtering and email security controls to detect and block suspicious or unexpected links containing malicious parameters targeting AI chatbots. Employ network monitoring and anomaly detection to identify unusual outbound connections or data flows originating from AI services. Limit AI agents’ access to sensitive data by enforcing the principle of least privilege and segregating AI workloads from critical business systems. Incorporate AI-specific security controls such as prompt input validation, context-aware filtering, and behavioral analytics to detect and prevent prompt injection attempts. Educate users about the risks of clicking unsolicited or unexpected links, even if they appear legitimate. Collaborate with AI vendors to understand and apply recommended security configurations and monitor emerging AI security research for new threats. Establish incident response plans that include AI-specific threat scenarios and forensic capabilities to analyze AI interactions. Finally, consider deploying layered defenses combining endpoint, network, and AI service protections to reduce the attack surface.