Cybersecurity researchers have uncovered a novel malware command-and-control (C2) technique that abuses AI assistants with web browsing and URL fetching capabilities, specifically Microsoft Copilot and xAI Grok, to act as stealthy C2 proxies. This method, termed "AI as a C2 proxy" by Check Point, leverages the AI assistants' ability to access external URLs and summarize content to create a covert communication channel between attacker-controlled infrastructure and malware on compromised hosts. The malware, already resident on a victim machine via other infection vectors, sends specially crafted prompts to the AI assistant, causing it to fetch commands from attacker servers and relay responses back to the malware. This bidirectional channel allows attackers to issue commands, receive data, and dynamically adapt their operations. Notably, this technique does not require API keys or registered accounts, circumventing common mitigation strategies like key revocation or account suspension. The AI's capabilities also enable attackers to generate reconnaissance workflows, script attacker actions, and decide subsequent steps based on system information, effectively automating parts of the intrusion lifecycle. This approach is an extension of living-off-the-land and living-off-trusted-sites tactics, exploiting trusted AI services to blend malicious traffic with legitimate enterprise communications, thereby evading detection by traditional security controls. The research highlights the potential for AI-driven implants and AI Operations (AIOps)-style C2 that automate triage, targeting, and operational decisions in real time. While no known exploits in the wild have been reported yet and the severity is currently assessed as low, the technique represents a significant evolution in attacker capabilities, especially as AI assistants become more integrated into enterprise environments. The disclosure follows related findings where attackers use large language models (LLMs) to dynamically generate malicious code in browsers, further illustrating the expanding attack surface introduced by AI technologies.

For European organizations, the abuse of AI assistants like Microsoft Copilot and xAI Grok as malware C2 proxies poses a stealthy threat that can bypass conventional network security monitoring and detection tools. Since these AI services are trusted and widely used in enterprise environments, malicious traffic routed through them can blend seamlessly with legitimate communications, complicating incident detection and response. The technique enables attackers to maintain persistent, adaptive control over compromised systems without relying on traditional C2 infrastructure, reducing their exposure to takedown efforts. This could lead to prolonged intrusions, data exfiltration, and lateral movement within networks. Additionally, the AI-driven automation of reconnaissance and evasion strategies can accelerate attack progression and reduce the need for human operator intervention, increasing attack efficiency. European organizations heavily invested in AI-assisted development and operational tools may face increased risk, especially if endpoint security solutions do not adequately monitor AI assistant interactions or if AI usage policies are not enforced. The lack of requirement for API keys or accounts means that standard access control measures may be ineffective, necessitating new detection paradigms. While no active exploitation has been reported, the potential for future attacks leveraging this technique could impact critical sectors such as finance, government, technology, and manufacturing across Europe, where AI adoption is significant.

1. Implement strict endpoint monitoring to detect unusual interactions with AI assistants, including anomalous prompt patterns or unexpected web browsing activity initiated by local processes. 2. Enforce application whitelisting and behavioral analytics to identify malware attempting to leverage AI services for C2 communication. 3. Restrict or monitor the use of AI assistants with web browsing or URL fetching capabilities in sensitive environments, especially on endpoints handling critical data. 4. Deploy network traffic analysis tools capable of correlating AI service usage with suspicious command patterns or data exfiltration attempts. 5. Educate security teams about this emerging threat to enhance detection capabilities and incident response readiness. 6. Collaborate with AI service providers to develop and implement usage anomaly detection and abuse prevention mechanisms, such as rate limiting or prompt content filtering. 7. Maintain robust malware prevention controls to reduce initial compromise risk, as this technique requires pre-existing malware on the host. 8. Integrate AI usage logs into security information and event management (SIEM) systems for comprehensive visibility. 9. Consider segmentation and least privilege principles to limit the ability of compromised hosts to communicate freely with AI services. 10. Regularly update and patch AI assistant software and related dependencies to mitigate potential vulnerabilities that could facilitate abuse.