What is the enterprise risk of using agentic AI workflows like GitHub Copilot?

Agentic AI workflows, while powerful, introduce new attack vectors. The primary enterprise risks include: 1) Data Exfiltration: Malicious actors can use prompt injection to trick AI agents into accessing and leaking sensitive corporate data. 2) Code Injection & Sabotage: Agents can be manipulated into writing insecure code, introducing vulnerabilities, or sabotaging the codebase. 3) Tool Poisoning: If an AI agent uses external tools (APIs, databases), those tools can be compromised, leading to a supply chain attack. CodeIntegrity provides real-time guardrails to mitigate these specific risks.

How does CodeIntegrity address the 'lethal trifecta' of AI security threats?

The 'lethal trifecta' consists of control-flow hijacking, data-flow corruption, and tool poisoning. CodeIntegrity uses a multi-layered defense: 1) For Control-Flow, we use semantic analysis to detect and block malicious instructions in prompts. 2) For Data-Flow, we sanitize and validate data retrieved by agents to prevent context poisoning. 3) For Tool Poisoning, we secure the Model Context Protocol (MCP) by validating tool calls and responses, ensuring the integrity of the agent's operational environment.

Can CodeIntegrity be deployed on-premise for maximum data privacy?

Yes. CodeIntegrity is designed for enterprise security and can be deployed entirely on-premise or in your private cloud (VPC). This ensures that your source code, prompts, and sensitive data never leave your controlled environment, meeting strict compliance requirements like SOC 2, GDPR, and CCPA.

How do your specialized Small Language Models (SLMs) improve security?

Instead of relying on a single large language model, we use a collection of fine-tuned SLMs, each specialized for a specific security task (e.g., prompt injection detection, PII redaction, hallucination detection). This approach provides higher accuracy, significantly lower latency (sub-millisecond), and a smaller attack surface compared to monolithic models. It allows for a more robust and efficient defense-in-depth security posture.

What is the Model Context Protocol (MCP) and how do you secure it?

The Model Context Protocol (MCP) is a specification for how AI models interact with external tools and data sources. It's a critical component of agentic AI. We secure MCP by implementing guardrails directly at the protocol level, inspecting every tool call and response for anomalies, unauthorized actions, and malicious payloads. This prevents attackers from exploiting tool interactions, a common vector for advanced attacks.

Does CodeIntegrity help with AI governance and compliance?

Absolutely. Our platform provides comprehensive logging, auditing, and policy enforcement capabilities for all AI interactions. This gives CISOs and compliance officers full visibility into how agentic AI is being used, helping to enforce security policies and generate auditable logs for compliance frameworks like NIST AI RMF and ISO/IEC 27001.

Heroku Exploit: App Ownership Takeover

A user has deployed a Node.js application on Heroku, which they manage through Heroku's remote MCP server in their IDE. This setup, while convenient, opens the door to a clever exploit that can result in a complete loss of application ownership.

The Attack: A Three-Step Takeover

The attack is a three-step process that begins with a malicious request and ends with the attacker gaining full control of the user's Heroku app.

Step 1: Injecting the Malicious Prompt

The attacker identifies the URL of the user's backend service and embeds a malicious prompt in a GET request. This prompt is designed to trigger a 404 error, ensuring it gets logged by the Heroku service.

The malicious GET request: https://scanner-mcpwned-backend-fd6e92d62400.herokuapp.com/

The prompt instructs the Heroku MCP to transfer ownership of the app to the attacker's email address.

Step 2: Triggering the Exploit

The user, while triaging bugs, asks their IDE to fetch the latest logs from the Heroku service. This action inadvertently triggers the exploit.

Step 3: Hijacking the MCP

When the IDE retrieves the logs, it is prompt-injected by the malicious message. The IDE's MCP then begins the tool call chain to transfer ownership of the app to the attacker.

Attack Summary

Why This Is Dangerous

This exploit is a form of Indirect Prompt Injection that critically compromises the MCP:

Control Flow Hijack: The user's intended read-only operation (viewing logs) is escalated into a highly privileged write operation. The malicious prompt, hidden in the logs, forces the MCP to execute the transfer_app tool, an action the user never authorized.
Data Flow Corruption: The data required for the malicious action—the attacker's email address—is supplied within the prompt. This corrupts the data flow by feeding attacker-controlled data into a sensitive tool, leading to a complete application takeover.

This specific attack vector is a variant of LLM01: Prompt Injection, the number one vulnerability in the OWASP Top 10 for Large Language Model Applications.

One-hop Account Takeover: The attacker never needs to compromise the victim's Heroku credentials. A single crafted GET request plants the takeover payload in the application logs, and the MCP executes it when a developer reviews those logs. This turns a harmless 404 probe into a full application takeover.
Low-Skill, High-Impact: Crafting this exploit requires no special headers, authentication, or advanced payload encoding—just a URL and the victim's hostname. Because the vulnerable workflow is common, any public Heroku app becomes a potential target.