Record summary
A quick snapshot of what this page covers.
Attack context
How this AI attack works in practice.
Adversaries may acquire publicly accessible AI agent configuration files to understand agent capabilities, gain unauthorized access to tools and data sources, or identify credentials for further attacks. Configuration files define what tools an agent can use, credentials for external services, system prompts, and behavioral settings, making valuable resources for adversaries targeting AI agent deployments.
Once configuration files are acquired, adversaries may perform Discover AI Agent Configuration to gain additional insights they can use in their operation or Credentials from AI Agent Configuration to harvest secrets.
AI agent configuration files come in multiple forms depending on the platform and agent framework. Agent configuration files adversaries may target include:
- System prompts: Files containing agent instructions, behavioral guidelines, and internal logic.
- Tool configuration: Files defining tools the agent can utilize, including Model Context Protocol (MCP) configs (e.g.,
mcp.json,claude_desktop_config.json), IDE-specific configs (e.g.,.claude/settings.json,.vscode/tasks.json), and framework-specific settings that define external tool and data source integrations. - Skills and workflows: Files defining agent capabilities, behaviors, or workflows. Often a combination of instructions, scripts, and resources.
- Environment and deployment configs: Files that control agent deployment and runtime behavior, often environment variables or framework-specific configs.
- ATLAS ID
- AML.T0002.002
- Priority score
- 30
Mitigations
Defenses that may help against this attack.
Case studies
Examples from public reports and exercises.
OpenClaw Command & Control via Prompt Injection
Researchers at HiddenLayer demonstrated how a webpage can embed an indirect prompt injection that causes OpenClaw to silently execute a malicious script. Once executed, the script plants persistent malicious instructions into future system prompts, allowing the attacker to issue new commands, turning OpenClaw into a command and control agent.
What makes this attack unique is that, through a simple indirect prompt injection attack into an agentic lifecycle, untrusted content can be used to spoof the model’s control scheme and induce unapproved tool invocation for execution. Through this single inject, an LLM can become a persistent, automated command & control implant.
Source
Where this page information comes from.
Original source
Original source links
Open the public records and source datasets used for this page.