A critical code injection vulnerability (CVE-2026-22785) has been discovered in orval's MCP server generation, allowing attackers to execute arbitrary code through malicious OpenAPI summary fields. This vulnerability affects TypeScript client generation from OpenAPI specifications, creating a direct pathway for compromising AI agent deployments that rely on auto-generated API clients. With severity marked as "high" by NIST, this issue demands immediate attention from anyone using orval-generated clients in production AI systems.
How the Attack Works
The vulnerability exploits orval's insufficient validation of OpenAPI summary fields during the client generation process. When orval processes a compromised OpenAPI specification, it fails to sanitize the summary text before embedding it into the generated TypeScript code. Attackers can craft malicious OpenAPI specs containing JavaScript code snippets within summary fields.
The attack vector is particularly insidious because it targets the code generation phase rather than runtime execution. When developers run orval against a compromised specification, the tool directly injects the attacker's code into the supposedly "type-safe" TypeScript client. Since orval is designed to generate production-ready clients, these malicious payloads become part of the trusted codebase.
The generated clients, now containing backdoors, are then integrated into AI agent infrastructure. This gives attackers persistent access to systems that should be protected by TypeScript's type safety guarantees. The vulnerability bypasses traditional security measures because the malicious code appears to come from a legitimate build process.
Real-World Implications for AI Agents
AI agent deployments are particularly vulnerable due to their reliance on multiple API integrations. Most production AI systems use orval or similar tools to generate clients for services like vector databases, LLM providers, and function calling endpoints. A single compromised client can provide attackers access to sensitive data flows, API credentials, and agent decision-making processes.
The trust boundaries in AI agent architectures assume that generated code is safe by design. This vulnerability shatters that assumption, creating a situation where the foundation of your API layer is potentially compromised. Attackers gaining code execution through this vector could manipulate agent behavior, exfiltrate training data, or pivot to other systems through the agent's privileged access.
Consider an AI agent handling customer support queries with access to internal databases. A compromised orval-generated client could silently log all queries, modify responses, or even extract customer data without triggering traditional security alerts. The type-safe nature of the generated code means security teams might never inspect the actual implementation, assuming the compiler and type system provide sufficient protection.
Immediate Defensive Measures
Update to orval v7.18.0 immediately if you're using any version below this threshold. The fix implements proper input validation and sanitization of OpenAPI summary fields before code generation. This should be treated as a critical security patch requiring emergency deployment procedures.
Implement specification validation before feeding OpenAPI documents to orval. Create a preprocessing pipeline that validates summary fields against a strict allowlist of characters and patterns. Here's a defensive validation example:
import { OpenAPIObject } from 'openapi3-ts';
function validateOpenAPISpec(spec: OpenAPIObject): boolean {
const dangerousPatterns = [
/<script\b[^<]*(?:(?!<\/script>)<[^<]*)*<\/script>/gi,
/javascript:/gi,
/eval\s*\(/gi,
/Function\s*\(/gi,
/\\u0000.*$/gi // Null byte injection
];
function checkSummary(obj: any): boolean {
if (typeof obj === 'object' && obj !== null) {
if (obj.summary && typeof obj.summary === 'string') {
for (const pattern of dangerousPatterns) {
if (pattern.test(obj.summary)) {
throw new Error(`Dangerous pattern detected in summary: ${obj.summary}`);
}
}
}
return Object.values(obj).every(checkSummary);
}
return true;
}
return checkSummary(spec);
}
Audit existing generated clients by reviewing the actual TypeScript code in your current deployments. Look for unusual patterns, embedded strings, or function definitions that don't align with expected API operations. Pay special attention to summary comments and documentation blocks where malicious payloads might hide.
Long-Term Security Practices
Establish a secure code generation pipeline that treats OpenAPI specifications as untrusted input until proven otherwise. Implement mandatory security scanning of all generated code before it enters your main codebase. This includes static analysis tools specifically configured to detect code injection patterns in TypeScript files.
Consider adopting a "generated code quarantine" approach where new client versions are deployed to staging environments with enhanced monitoring before production rollout. Monitor for unusual network activity, unexpected file system access, or abnormal memory usage that might indicate malicious code execution.
Implement supply chain verification for your OpenAPI sources. If you're consuming third-party API specifications, establish cryptographic verification of specification integrity. Use content-addressable storage for specifications and validate checksums before code generation. This prevents attackers from substituting malicious specifications during the build process.
The vulnerability in orval (CVE-2026-22785) serves as a critical reminder that the security of AI agent infrastructure depends on every component in the toolchain. Code generation tools, while powerful, must be treated as potential attack vectors requiring the same security scrutiny as any other dependency. Update immediately, implement validation pipelines, and establish monitoring that can detect compromise of generated code. Your AI agents are only as secure as their most vulnerable generated client.
Source: NVD CVE-2026-22785