The disclosed vulnerabilities pertain to Anthropic's Model Context Protocol (MCP), a framework designed to manage and structure interactions with large language models (LLMs). The research reveals two critical attack vectors: Full-Schema Poisoning and Advanced Tool Poisoning. Full-Schema Poisoning involves injecting malicious logic into any schema field within the MCP, effectively altering the behavior of the LLM in a way that can be stealthily activated only in production environments. This manipulation can cause the model to execute unintended commands or leak sensitive information. Advanced Tool Poisoning further exploits the LLM by tricking it into revealing secrets such as SSH keys, which are critical credentials for secure system access. These attacks are particularly insidious because they do not manifest during testing or development phases, making detection challenging until deployment. The proof-of-concept (PoC) demonstrates how attackers can embed malicious payloads within the schema definitions that the LLM processes, leading to unauthorized data disclosure and potential compromise of systems relying on the MCP for secure model interactions. The lack of patches or mitigations at the time of disclosure, combined with the critical severity rating, underscores the urgency for organizations using Anthropic's MCP to assess their exposure and implement protective measures.

For European organizations, the impact of these vulnerabilities could be severe, especially for entities leveraging Anthropic's MCP in production environments for sensitive applications such as automated customer support, internal knowledge bases, or security tooling. The ability to poison schemas and extract secrets like SSH keys threatens confidentiality by exposing sensitive credentials and internal data. Integrity is compromised as attackers can manipulate the model's behavior, potentially causing erroneous outputs or unauthorized actions. Availability risks arise if attackers leverage these vulnerabilities to disrupt services or gain persistent access. Given the stealthy nature of these attacks, detection and response may be delayed, increasing the window for exploitation. Organizations in sectors with strict data protection regulations, such as finance, healthcare, and government, face heightened risks of regulatory penalties and reputational damage. Furthermore, the reliance on LLMs and AI-driven tools is growing in Europe, amplifying the potential attack surface and impact.

European organizations should immediately conduct a thorough audit of their use of Anthropic's MCP, focusing on schema definitions and production deployment configurations. Implement strict input validation and schema integrity checks to detect unauthorized modifications. Employ runtime monitoring to identify anomalous LLM behavior indicative of poisoning attacks. Segregate environments to ensure that production secrets like SSH keys are not accessible or embedded within model contexts. Use hardware security modules (HSMs) or secure vaults to manage sensitive credentials separately from the LLM environment. Engage with Anthropic for updates or patches addressing these vulnerabilities and apply them promptly once available. Additionally, incorporate anomaly detection systems that monitor for unusual data exfiltration patterns or unexpected LLM outputs. Train security teams on the specifics of LLM-related threats to enhance incident response capabilities. Finally, consider limiting the scope of schema fields that can influence model behavior to reduce the attack surface.