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Adversaries may compromise macOS-based Generative AI (GenAI) tools through prompt injection, malicious Model Context Protocol (MCP) servers, or poisoned plugins to establish Command and Control (C2) channels or exfiltrate sensitive data by causing them to connect to unusual domains.

Tracking brief for the Shai-Hulud campaign; individual sightings are folded in as reported.

AI coding agents such as Claude Code, Cursor, Codex, and GStack are increasingly exhibiting behaviors on Windows endpoints that mimic adversarial tradecraft, including credential access, LOLBin usage for ingress, command-line obfuscation, and persistence mechanisms, thereby triggering existing security detection rules designed for malicious activity and posing significant false positive challenges for detection engineers.

A high-severity vulnerability (CVE-2026-12957) in the Amazon Q Developer Extension for Visual Studio Code allowed attackers to achieve arbitrary code execution and cloud credential theft by automatically loading and executing malicious Model Context Protocol (MCP) server configurations from a `.amazonq/mcp.json` file in a repository without user consent, providing full access to a developer's environment and cloud credentials.

Attackers are leveraging indirect prompt injection against AI agents with access to private data, untrusted content, and external communication channels to steal sensitive information by embedding malicious instructions in content processed by the agent.

This rule detects Elastic Defend alerts originating from or directly related to GenAI coding utilities, indicating potential prompt injection, malicious skills, or supply-chain compromise.

This rule detects GenAI tools on macOS connecting to unusual domains, potentially indicating command and control activity, data exfiltration, or malicious payload retrieval following compromise via prompt injection, malicious MCP servers, or poisoned plugins.

Unsecured Model Context Protocol (MCP) servers, used to connect AI agents to enterprise tools, lack authentication and audit trails, leading to data exfiltration, private repo leaks, cross-tenant exposure, and remote code execution due to AI agents using valid user credentials to make API calls based on potentially poisoned context.

This rule detects unusual modifications to GenAI tool configuration files, potentially indicating an attacker injecting malicious MCP server configurations to hijack AI agents for persistence, command and control, or data exfiltration.