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.

Linear Exploit: Bypassing Team ACLs

A critical vulnerability in Linear's MCP integration allows attackers to bypass team-level access controls and exfiltrate confidential data. This exploit, discovered by CodeIntegrity researchers, uses malicious prompts to hijack the MCP, turning a seemingly harmless query into a significant data breach.

The Setup: Segregating Confidential Data

The scenario involves two Linear teams: confidential_team, with restricted access, and public_team, with broader access. A user manages these teams through the Linear MCP in their IDE.

The Attack: A Four-Step Exploitation

The attack unfolds in a sequence of steps, beginning with a malicious prompt and ending in data exfiltration.

Step 1: Injecting the Malicious Prompt

An attacker, without access to confidential_team, injects a malicious prompt into a task in public_team. This is the entry point for the exploit.

Step 2: Compromising the MCP

A legitimate user with access to confidential_team queries their IDE about recent updates in public_team. When the IDE's chat feature retrieves this information, it is compromised by the embedded malicious prompt.

Step 3: Exfiltrating Confidential Data

The compromised MCP is hijacked, causing it to update the public_team issue with confidential data from an issue in private_team.

Step 4: Capturing the Leaked Information

The attacker can then access the private data that has been exfiltrated to the public team.

The Anatomy of the Attack

This diagram provides a high-level overview of the attack flow, from prompt injection to data exfiltration.

Why This Attack Is So Dangerous

This exploit is a classic example of an Indirect Prompt Injection attack. The malicious prompt, stored in a public ticket, is later retrieved by a privileged user, hijacking the MCP in two critical ways:

Control Flow Hijack: The prompt dictates a malicious sequence of tool calls (list_comments → get_issue → update_issue), forcing the MCP to perform actions in an order that leads to data exfiltration.
Data Flow Corruption: The data retrieved from a confidential issue is rerouted. Instead of flowing back to the user, it is injected into the body parameter of the update_issue tool call, effectively redirecting sensitive information to a public ticket.

This attack vector is a variant of LLM01: Prompt Injection, the number one vulnerability in the OWASP Top 10 for Large Language Model Applications. It has two particularly dangerous characteristics:

Invisible Privilege Escalation: A comment in a public Linear team can trick the MCP agent into acting with a privileged user's token, silently bypassing Linear's team-level ACLs.
"Drive-by" Exfiltration: No credentials, phishing, or OAuth consent are needed—just a single update in a ticket the attacker already has access to. One well-placed prompt can leak the contents of every linked confidential issue.

How to Mitigate the Risk with CodeIntegrity

Protecting against this exploit is straightforward with CodeIntegrity's security policies. By implementing targeted guardrails, we can block unauthorized data access without disrupting workflows.

Identifying the Attack Signature

To develop an effective security policy, we first need to identify the exploit's signature pattern. The attack requires a specific sequence of tool calls to succeed:

First, the attacker must read data from a confidential issue.
Then, they must exfiltrate that data to a public issue.

All successful exploits must use the update_issue tool call to exfiltrate the stolen data. However, simply blocking update_issue is too restrictive, as it would prevent legitimate updates and severely limit Linear MCP functionality.

Implementing a Targeted Policy

A closer analysis reveals that the attack follows a specific tool call sequence. The malicious pattern always includes:

Reading comments: list_comments
Followed by reading issue data: list_issues, get_issue, or list_my_issues
Followed by exfiltration: update_issue