A newly disclosed critical vulnerability (CVE-2025-69256) has sent shockwaves through the AI development community, revealing a dangerous command injection flaw in the Serverless Framework's experimental MCP server package. This vulnerability allows remote code execution through unsanitized child_process.exec calls, potentially compromising entire serverless AI agent deployments. For teams running AI workloads on AWS Lambda with the @serverless/mcp package, this represents an immediate and severe security risk that demands urgent attention.
How the Attack Works
The vulnerability stems from insufficient input sanitization within the MCP (Model Context Protocol) server implementation. When processing user inputs through the experimental MCP feature, the framework fails to properly escape shell metacharacters before passing them to child_process.exec calls. This allows attackers to inject arbitrary commands that execute with the same privileges as the serverless function.
The attack vector is particularly concerning because it exploits the trust relationship between AI agents and their underlying infrastructure. Malicious inputs can be crafted to appear as legitimate requests to the AI agent, which then unknowingly passes the payload to the vulnerable MCP server component. Once executed, these commands can access AWS credentials, exfiltrate data, or pivot to other systems within the cloud environment.
What's especially troubling is that this vulnerability affects the experimental MCP feature specifically designed to enhance AI agent capabilities. Organizations adopting cutting-edge AI tooling are unknowingly exposing themselves to RCE risks. The serverless nature of the deployment means traditional security monitoring tools may miss these attacks, as the malicious activity exists only for the duration of the function execution.
Real-World Implications for AI Deployments
For production AI agent deployments, CVE-2025-69256 represents a perfect storm of accessibility and impact. Serverless functions often run with IAM roles granting access to databases, APIs, and other critical resources. A successful exploit doesn't just compromise the individual function—it potentially exposes the entire AWS account and connected services.
Consider a customer service AI agent deployed via Serverless Framework with MCP capabilities. An attacker could inject commands through what appears to be a routine customer inquiry. The injected payload might exfiltrate customer databases, access payment processing systems, or establish persistent backdoors in the infrastructure. Since serverless functions scale automatically, such an attack could execute across hundreds of concurrent instances before detection.
The experimental nature of the MCP feature means many organizations may not even realize they're vulnerable. Development teams often enable experimental features for testing and forget to disable them before production deployment. This "shadow infrastructure" creates blind spots in security postures, especially when security teams aren't aware of the MCP server's presence in their stack.
Immediate Defensive Measures
Organizations must act immediately to assess and mitigate this vulnerability. First, audit all serverless deployments to identify any using the @serverless/mcp package. The following command can help identify vulnerable projects:
# Check for vulnerable MCP package in your projects
grep -r "@serverless/mcp" package.json */package.json 2>/dev/null
If the MCP package is present and the experimental feature is enabled, disable it immediately by removing or commenting out the MCP configuration in your serverless.yml:
# Remove or comment out MCP configuration
# experimental:
# mcp:
# enabled: true
For teams requiring MCP functionality, implement strict input validation before any data reaches the MCP server. Create a sanitization layer that strips shell metacharacters and validates input against expected patterns. Additionally, apply the principle of least privilege to your Lambda functions—ensure they only have access to resources absolutely necessary for their specific function.
Long-Term Security Strategies
Beyond immediate mitigation, organizations should implement comprehensive security practices for AI agent deployments. Establish a security review process for all experimental features before production use. This includes threat modeling specific to AI agent attack vectors and regular security assessments of serverless configurations.
Implement runtime protection through AWS Lambda's built-in security features. Enable AWS CloudTrail logging for all Lambda invocations and set up CloudWatch alarms for suspicious execution patterns. Consider using AWS WAF in front of API Gateway endpoints to filter malicious requests before they reach your functions.
For development teams, establish secure coding standards specifically for AI agent implementations. This includes mandatory input sanitization, output encoding, and regular dependency scanning. Tools like Snyk or OWASP Dependency Check should be integrated into CI/CD pipelines to catch vulnerabilities like CVE-2025-69256 before deployment.
The CVE-2025-69256 vulnerability serves as a critical reminder that experimental AI features can introduce significant security risks. Organizations must balance innovation with security, implementing proper safeguards before deploying AI agents to production. Regular security audits, input validation, and the principle of least privilege remain fundamental to protecting serverless AI deployments. For full technical details, refer to the original advisory at https://nvd.nist.gov/vuln/detail/CVE-2025-69256 and prioritize patching or disabling affected systems immediately.