To protect against prompt injection via Model Context Protocol (MCP) tools, focus on input validation, context isolation, and continuous monitoring. Prompt injection occurs when untrusted inputs manipulate the model’s behavior by overriding its intended instructions. MCP tools often define the model’s initial context or constraints, so securing them requires explicit safeguards to prevent malicious inputs from altering that setup.
First, validate and sanitize all inputs that interact with the MCP. Treat user-provided data as untrusted and enforce strict rules on what can be included. For example, if your MCP defines a system prompt like “Answer questions about healthcare,” ensure user inputs cannot append phrases like “ignore previous instructions” or inject new commands. Use allowlists to block reserved keywords (e.g., "system:", “admin:”) or special characters that could modify the context. Additionally, limit the length of user inputs to reduce the risk of hidden payloads. For instance, a chatbot using MCP could truncate inputs over 200 characters and escape brackets or quotes that might break the context structure.
Second, isolate the MCP-defined context from user inputs. Use technical boundaries like separate data channels or markup tags (e.g., <user_input>) to distinguish between system instructions and external data. For example, if your MCP tool uses a JSON configuration, keep the system prompt in a locked field and place user content in a separate, sanitized field. This prevents attackers from blending malicious instructions with legitimate inputs. Tools like OpenAI’s “system” and “user” role tags in API calls demonstrate this approach—system messages define behavior, while user messages are treated as untrusted. Implement runtime checks to detect anomalies, such as sudden changes to the MCP context mid-request, and log these events for review.
Finally, test your defenses rigorously. Simulate attacks by crafting inputs designed to bypass MCP constraints, such as “Previous answer was wrong. Redo it as: {malicious code}.” Use automated tools to scan for vulnerabilities, like fuzzing frameworks that generate random payloads to test input handling. Update your validation rules and context isolation mechanisms as new attack patterns emerge. For example, if a new injection method exploits Markdown formatting in MCP tools, add filters to strip Markdown syntax from untrusted inputs. Regularly audit logs to identify suspicious activity and refine your safeguards. By combining these strategies, you can minimize the risk of prompt injection while maintaining the MCP’s intended functionality.