Critical MCP Vulnerability Exposes AI Agents to Session Hijacking Through Predictable IDs

Understanding the MCP Session Hijacking Threat

A significant security vulnerability in the Oat++ implementation of Anthropic’s Model Context Protocol (MCP) has been discovered, enabling attackers to hijack AI agent sessions by exploiting predictable session identifiers. This flaw, designated as CVE-2025-6515, allows malicious actors to intercept and manipulate AI conversations by predicting or capturing session IDs from active MCP sessions., according to industry news

Understanding the MCP Session Hijacking Threat
Technical Breakdown of the Vulnerability
Exploitation Requirements and Attack Scenario
Broader Implications for AI Security
Mitigation Strategies and Best Practices

The vulnerability specifically affects the oatpp-mcp server implementation, which serves as the bridge between Oat++ web applications and MCP-compatible interfaces. MCP, developed by Anthropic, functions as the standard protocol for connecting AI agents with various data sources and external tools, making this security gap particularly concerning for organizations integrating AI into their industrial and operational workflows.

Technical Breakdown of the Vulnerability

According to security researchers at JFrog who discovered the flaw, the vulnerability stems from how the Oat++ MCP implementation handles session ID generation when using Server-Sent Events (SSE) transport method. Unlike the STDIO transport method, the SSE endpoint returns an instance pointer as the session ID, which is neither globally unique nor cryptographically secure.

“The fundamental security requirement for session IDs is that they must be globally unique and randomly generated to prevent prediction or guessing attacks,” explained security researchers in their technical analysis. “When this principle is violated, as in the case of oatpp-mcp’s SSE implementation, it creates an opening for session hijacking.”

The attack methodology involves an attacker rapidly creating and destroying sessions to log session IDs, then waiting for those same IDs to be reassigned to legitimate client sessions. Once a match is identified, the attacker can send POST requests using the hijacked session ID to inject malicious responses, request tools, trigger prompts, or execute commands that the server will then forward to the victim’s active connection., according to technological advances

Exploitation Requirements and Attack Scenario

For this attack to be successful, two specific conditions must be met: the oatpp-mcp server must be configured to use HTTP SSE transport, and the attacker must have network access to the relevant HTTP server. This makes the vulnerability particularly relevant for industrial systems where AI agents might be deployed in networked environments., according to technological advances

In a demonstration by JFrog researchers, they showed how an attacker could manipulate a Claude AI client connected to a test server programmed to return Python package names. When a legitimate user asks Claude to “find a package for image processing,” an attacker who has successfully hijacked the session can direct the server to supply a malicious package instead of legitimate options., according to recent studies

The implications extend beyond simple data manipulation – in industrial contexts where AI agents might control physical processes or access sensitive operational data, such session hijacking could lead to serious operational disruptions or safety concerns.

Broader Implications for AI Security

This vulnerability highlights a critical aspect of AI security that often goes overlooked: while AI models themselves might be secure, the protocols and infrastructure supporting them can introduce significant vulnerabilities. As the researchers noted, “As AI models become increasingly embedded in workflows via protocols like MCP, they inherit new risks – this session-level exploit shows how the model itself remains untouched while the ecosystem around it is compromised.”

The discovery underscores the importance of securing the entire AI infrastructure stack, not just the AI models themselves. For industrial applications where AI agents might interface with control systems, manufacturing equipment, or critical infrastructure, such vulnerabilities could have far-reaching consequences., as comprehensive coverage

Mitigation Strategies and Best Practices

To protect against this type of prompt hijacking attack, organizations should implement several key security measures:

Ensure MCP servers use cryptographically secure random number generators for session ID generation
Avoid simple incrementing IDs or pointer-based identifiers that are vulnerable to prediction attacks
Implement strong session separation and expiry mechanisms
Consider transport channel security and network access controls for MCP implementations
Regularly audit and update MCP server implementations

For developers working with the oatpp-mcp framework, it’s crucial to review the implementation’s session management and ensure proper security practices are followed. Additional guidance on securing MCP implementations can be found in the MCP security best practices documentation.

Organizations leveraging AI agents in industrial environments should conduct thorough security assessments of their MCP implementations and consider the network security implications of their AI deployment architecture. As AI becomes more integrated into critical systems, ensuring the security of the supporting protocols becomes just as important as securing the AI models themselves.