AI Security Breach: How Predictable Session IDs Enable MCP Protocol Hijacking

The MCP Session Hijacking Vulnerability

A critical security vulnerability in the implementation of Anthropic’s Model Context Protocol (MCP) has exposed AI agents to session hijacking attacks through predictable session identifiers. Designated as CVE-2025-6515, this flaw affects the Oat++ web framework’s MCP integration, specifically targeting the oatpp-mcp server implementation that enables AI systems to communicate with external data sources and tools.

The MCP Session Hijacking Vulnerability
How the Session Hijacking Attack Works
Real-World Exploitation Scenario
Security Implications for AI Integration
Mitigation and Prevention Strategies
The Future of AI Protocol Security

Security researchers at JFrog discovered that the Oat++ MCP implementation fails to generate sufficiently random session IDs, instead returning instance pointers that can be predicted or captured by attackers. This fundamental weakness in session management creates a pathway for malicious actors to intercept and manipulate AI conversations, potentially redirecting users to harmful content or executing unauthorized commands through compromised AI interactions., according to market developments

How the Session Hijacking Attack Works

The exploitation process involves several distinct phases that leverage the predictable nature of session ID generation. According to JFrog researchers Ori Hollander and Ofri Ouzan, attackers begin by rapidly creating and destroying sessions while logging the assigned session IDs. Through this reconnaissance phase, attackers build a database of potential session identifiers that the system is likely to reassign to legitimate users.

The attack specifically targets the Server-Sent Events (SSE) transport method within the Oat++ MCP implementation. Unlike the STDIO transport method, the SSE endpoint exposes session IDs that lack cryptographic security. When an attacker identifies a reused session ID assigned to an active user session, they can inject malicious responses that the MCP server will forward to the victim’s connection., according to expert analysis

“Once a session ID is reused, the attacker can send POST requests using the hijacked ID to request tools, trigger prompts, or inject commands,” the researchers explained. “The server then forwards these malicious responses to the victim’s active GET connection alongside legitimate responses.”, according to recent research

Real-World Exploitation Scenario

JFrog’s demonstration revealed the practical implications of this vulnerability. In their test scenario, a user asks an AI agent (specifically Claude) to “find a package for image processing.” Meanwhile, an attacker who has been systematically testing previously used session IDs identifies a match and directs the server to supply a malicious Python package instead of legitimate image processing libraries.

The compromised session enables what researchers term “prompt hijacking” – where the AI model itself remains uncompromised, but the communication channel between the model and the user is manipulated. This represents a new class of AI security threats where the ecosystem surrounding AI models becomes the attack vector rather than the models themselves.

Security Implications for AI Integration

As organizations increasingly integrate AI agents into critical workflows through protocols like MCP, they inherit previously unforeseen security risks. The MCP protocol was designed specifically to connect AI models with data sources and tools securely, making this vulnerability particularly concerning for enterprises deploying AI solutions.

The researchers emphasized that “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.” This distinction highlights the evolving nature of AI security, where traditional application security concerns merge with AI-specific threat vectors.

Mitigation and Prevention Strategies

To protect against session hijacking attacks targeting MCP implementations, organizations should implement several critical security measures:

Implement cryptographically secure random number generators for all session ID creation, ensuring true unpredictability
Avoid simple incrementing IDs or instance pointers that create predictable patterns
Establish robust session separation and expiration mechanisms
Conduct thorough security reviews of MCP server implementations before deployment
Follow MCP security best practices for transport layer security

The vulnerability specifically requires that oatpp-mcp executes with HTTP SSE transport and that attackers have network access to the relevant HTTP server, which helps limit the potential attack surface. However, organizations using the affected oatpp-mcp implementation should immediately review their session management practices.

The Future of AI Protocol Security

This vulnerability underscores the growing importance of security in AI integration protocols. As detailed in JFrog’s comprehensive analysis, the intersection of traditional web security vulnerabilities with emerging AI technologies creates unique challenges that require specialized security expertise., as detailed analysis

The discovery of CVE-2025-6515 serves as a critical reminder that securing AI systems extends beyond protecting the models themselves to ensuring the entire ecosystem – including communication protocols, session management, and integration frameworks – maintains robust security standards. As AI continues to permeate business operations, comprehensive security assessments of AI infrastructure components will become increasingly essential for organizational security.