Browse Rules

GenAI Process Connection to Unusual Domain

Detects GenAI tools connecting to unusual domains on macOS. Adversaries may compromise GenAI tools through prompt injection, malicious MCP servers, or poisoned plugins to establish C2 channels or exfiltrate sensitive data to attacker-controlled infrastructure. AI agents with network access can be manipulated to beacon to external servers, download malicious payloads, or transmit harvested credentials and documents.

GenAI Process Connection to Suspicious Top Level Domain

Detects when GenAI tools connect to domains using suspicious TLDs commonly abused for malware C2 infrastructure. TLDs like .top, .xyz, .ml, .cf, .onion are frequently used in phishing and malware campaigns. Legitimate GenAI services use well-established domains (.com, .ai, .io), so connections to suspicious TLDs may indicate compromised tools, malicious plugins, or AI-generated code connecting to attacker infrastructure.

GenAI Process Performing Encoding/Chunking Prior to Network Activity

Detects when GenAI processes perform encoding or chunking (base64, gzip, tar, zip) followed by outbound network activity. This sequence indicates data preparation for exfiltration. Attackers encode or compress sensitive data before transmission to obfuscate contents and evade detection. Legitimate GenAI workflows rarely encode data before network communications.

Unusual Process Modifying GenAI Configuration File

Detects unusual modification of GenAI tool configuration files. Adversaries may inject malicious MCP server configurations to hijack AI agents for persistence, C2, or data exfiltration. Attack vectors include malware or scripts directly poisoning config files, supply chain attacks via compromised dependencies, and prompt injection attacks that abuse the GenAI tool itself to modify its own configuration. Unauthorized MCP servers added to these configs execute arbitrary commands when the AI tool i

GenAI Process Accessing Sensitive Files

Detects when GenAI tools access sensitive files such as cloud credentials, SSH keys, browser password databases, or shell configurations. Attackers leverage GenAI agents to systematically locate and exfiltrate credentials, API keys, and tokens. Access to credential stores (.aws/credentials, .ssh/id_*) suggests harvesting, while writes to shell configs (.bashrc, .zshrc) indicate persistence attempts. Note: On linux only creation events are available. Access events are not yet implemented.

GenAI CLI Started with Unsafe Permission Bypass

Identifies GenAI agent CLIs started with permission-bypass or auto-approval flags that disable human-in-the-loop guardrails. These modes are intended for isolated sandboxes but are frequently misused on internet-connected developer workstations, allowing prompt injection, compromised dependencies, or malicious skills to execute commands, modify files, or reach sensitive paths without confirmation.

Elastic Defend Alert from GenAI Utility or Descendant

Detects Elastic Defend alerts (behavior, malicious file, memory signature, shellcode) where the alerted process or its direct parent is a GenAI coding or assistant utility (e.g. Cursor, Claude, Windsurf, Cody, Continue, Aider, OpenClaw, Moltbot, Clawdbot, Codeium, Tabnine, GitHub Copilot). Activity from these tools can indicate prompt injection, malicious skills, or supply-chain abuse; this Higher-Order rule helps prioritize such alerts for triage.

GenAI or MCP Server Child Process Execution

Detects child process execution from GenAI tools or MCP (Model Context Protocol) servers. Adversaries exploit AI agents to execute system commands, exfiltrate data, or establish persistence. MCP servers provide LLMs direct access to execute shell commands, read files, and interact with external services. This building block provides visibility into AI-initiated process execution for correlation with other suspicious activity.

GenAI Process Compiling or Generating Executables

Detects when GenAI tools spawn compilers or packaging tools to generate executables. Attackers leverage local LLMs to autonomously generate and compile malware, droppers, or implants. Python packaging tools (pyinstaller, nuitka, pyarmor) are particularly high-risk as they create standalone executables that can be deployed without dependencies. This rule focuses on compilation activity that produces output binaries, filtering out inspection-only operations.

AWS Bedrock Guardrail Deleted or Weakened

Detects deletion, weakening, or version management of AWS Bedrock guardrails via the DeleteGuardrail, UpdateGuardrail, DeleteEnforcedGuardrailConfiguration, or PutEnforcedGuardrailConfiguration APIs. Bedrock guardrails enforce content, topic, word, and sensitive-information policies on model invocations. Deleting a guardrail, loosening its policies, removing or overwriting the organization-enforced guardrail configuration, or creating a new version to enforce a weakened configuration allows an a