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.

Applying Taint Analysis to Agent's Tool Flows

As developers increasingly rely on AI agents and the Model Context Protocol (MCP), the security paradigm shifts. We face a familiar challenge in a new form: ensuring agents behave as expected when chaining tools together. This problem mirrors the classic data flow challenges of static code analysis. Instead of tracing variables through functions, we must now trace data through tool calls mediated by an LLM.

From Code Flows to Agent Flows

In traditional static analysis, taint analysis is used to track how untrusted input or sensitive data propagates through a system. We can apply this same model to AI agents:

A tool that fetches private data is a source.
A tool that mutates state or exposes data is a sink.
The LLM, sitting between them, is an opaque transformation layer and a potential source of risk itself.

By categorizing tools, we can systematically reason about risk:

(P) = Private Data Source
(U) = Untrusted Data Source
(S) = State-Changing Sink

This allows us to define two primary risk templates:

Data Leak Risk (P → S): A confidentiality failure where a tool reading private data is followed by one that exposes it.
Tamper Risk (U → S): An integrity failure where a tool consuming untrusted input is followed by one that mutates state.

How We Identify Risky Tool Chains

Unlike in static code, data flow between tools is not explicit. We use a combination of signals to identify potentially hazardous connections:

Schema Overlap: Do two tools share input parameters (e.g., owner, repo)? A strong overlap suggests they operate on the same logical entity.
Output-to-Input Mapping: Can the output of a source tool directly satisfy the inputs of a sink tool? This is a strong indicator of a direct data flow.
Runtime Analysis: Observing real-world tool call sequences reveals which flows are common in practice, helping to validate static assumptions.
LLM-Powered Categorization: We use LLMs to interpret tool descriptions and classify them as sources (P, U) or sinks (S), providing a scalable foundation for our analysis.

By combining these signals, we can build a realistic model of how agents chain tools together and identify high-risk patterns.

Real-World Examples from the GitHub MCP

Here are three examples from the GitHub MCP that illustrate these risks:

1. get_pull_request_files (P) → add_comment_to_pending_review (S)

Risk: Data Leak.
Flow: The contents of private files (get_pull_request_files) could be fed into the body of a pull request comment (add_comment_to_pending_review), exposing sensitive code.

2. get_issue_comments (U) → create_issue (S)

Risk: Tamper.
Flow: An untrusted comment from a public issue (get_issue_comments) could contain a malicious prompt that instructs the agent to create a new issue with harmful content (create_issue).

3. get_pull_request_status (U) → merge_pull_request (S)

Risk: Tamper.
Flow: A manipulated or misunderstood pull request status (get_pull_request_status) could trick the agent into merging a branch that is not ready (merge_pull_request), compromising the repository's integrity.

Putting It All Together

Applying the principles of taint analysis to tool call flows provides a systematic framework for securing AI agents and knowing the risks. By categorizing tools as sources and sinks and analyzing the data flows between them, developers can identify and mitigate potential data leak and tampering risks before they are exploited. Rather than just filtering inputs and outputs, this lets us build security rules grounded in actual tool behaviour, mapping how data moves, spotting risky control paths, and enforcing protections intelligently.