Detecting AI agents like OpenClaw with automated tooling

Links to article series:

• Part 1: OpenClaw: Open for work?
• Part 2: Detecting AI agents like OpenClaw with automated tooling
• Part 3: Mitigation assets and detection patterns for AI agents like OpenClaw

AI-powered coding assistants, autonomous security agents, and extension-based development tools are transforming how engineering teams operate. The innovations are real, but they introduce a new category of shadow IT with significant security and compliance implications.

For CTOs, CIOs, and CISOs, understanding and controlling this risk must become a priority.

The challenge: multi-vector AI tool usage

AI agents and extensions operate across multiple vectors:

Static artifacts — Skills, plugins, configurations installed across user directories and repositories

Runtime processes — Active AI tools executing code during development and workflows

Extension registries — VSCode and IDE extensions that persist and execute automatically

Network communication — Agents and tools communicating with external APIs, cloud services, and command and control infrastructure

Traditional SIEM alerts rarely catch these activities because individual signals appear normal during development workflows.

Why it matters for enterprise

Security risks

• Data exfiltration: Code, credentials, and proprietary IP sent to external AI providers
• Malicious code injection: Skills and plugins with unauthorized execution capabilities
• Supply chain compromise: Extension vulnerabilities and repo-manipulation attacks
• Autonomous threat actors: Agents capable of autonomous code execution and persistence

Compliance and governance

• Policy violations: Unauthorized AI tool usage against enterprise security requirements
• Audit trail blind spots: Difficulty tracking AI-assisted development and code reviews
• Data classification gaps: Uncontrolled sharing of sensitive information via AI interfaces
• Regulatory non-compliance: Potential violations of data handling and privacy regulations

A defense-in-depth detection framework

An effective detection strategy must combine multiple detection vectors:

1. Filesystem and artifact detection

AI tools often install persistent artifacts across user home directories and project repositories. Path-based queries scan for:

• Configuration directories: ~/.claude/skills, ~/.openclaw/skills, ~/.gemini/skills
• Plugin registries: ~/.claude/.plugins, .vscode/extensions
• Repository-local skills: Project-specific .claude/ directories

This captures dormant threats that may not appear in process lists.

2. Runtime process detection

Active AI tools manifest as running processes with recognizable signatures:

• Desktop applications: Claude Desktop, Cursor, Windsurf, LM Studio
• CLI tools: GitHub Copilot CLI, Aider, Continue.dev
• Autonomous agents: OpenClaw, Clawdbot, Moltbot, ollama

Process monitoring combined with user context provides real-time visibility into tool usage.

3. Extension and plugin detection

IDE extensions and browser plugins can execute code even when parent applications are inactive:

• VSCode extensions: GitHub Copilot, Claude, Cody, Continue.dev
• Code completion providers: Windsurf, OpenClaw agents
• Browser extensions for AI integration

Extension catalogs provide version metadata for vulnerability correlation and attack surface analysis.

4. Network and communication monitoring

AI agents communicate with cloud services, API endpoints, and local infrastructure:

• External API calls to LLM providers (Anthropic, OpenAI, Google)
• Local service bindings on non-standard ports
• Command and communication infrastructure

Network telemetry reveals who's talking to what, and when.

Strategic implementation for c-level governance

Detection architecture

Effective threat hunting requires a layered approach:

• Static Analysis: Periodic filesystem scans for installed artifacts
• Dynamic Monitoring: Continuous process and network telemetry
• Compliance Integration: Correlation with user authorization and data classification policies
• Vulnerability Tracking: Version awareness for CVE correlation and patch management

Data and alerting strategy

CISOs should define clear alerting thresholds:

• High-severity: Unauthorized autonomous agents on non-compliant systems, exposed listening ports, credential access attempts
• Medium-severity: Unknown AI tool usage, unexpected process starts, suspicious command-line arguments
• Low-severity: Novel AI tool installations, unexpected file changes in AI configuration directories

Rely on contextual correlation rather than single-sign alerts. An AI tool run by an authenticated developer performing authorized development work may generate multiple signals. Alerting should prioritize unusual patterns or violations of access controls.

Response and governance

When AI tool violations are detected:

• Immediate: Disable unauthorized tools, isolate infected systems, preserve forensic evidence
• Investigative: Trace user sessions, correlate with network logs, document attack paths
• Corrective: Update policy, revoke access, patch vulnerabilities
• Preventive: Harden configurations, implement agent whitelisting, improve monitoring

This sequence should be supported by clear evidence trails for audit and compliance purposes.

Measurement and reporting

Board-level visibility requires focused metrics:

• Asset coverage: Percentage of endpoints with AI tool detection enabled
• Alert volume: High/medium/low severity alerts prioritized by business risk
• Incident response time: Average time to detect and address AI-related security events
• Policy compliance rate: Percentage of endpoints in compliance with AI tool usage policy
• Vulnerability exposure: Number of extension and tool versions with known CVEs

These metrics should be reviewed quarterly with the board and technical teams.

Moving forward

AI and autonomous tooling is no longer optional for modern engineering teams. The question isn't whether to adopt these tools — it's how to govern them effectively.

Organizations that implement detection frameworks now will be better positioned to:

• Maintain security posture in an evolving threat landscape
• Enable compliance with data protection and privacy regulations
• Protect intellectual property and sensitive information
• Make informed decisions about AI tool adoption and governance

The time to establish control is now, before AI tools become entrenched. With a comprehensive detection framework in place, leadership can enable innovation while maintaining visibility and control.

For more on this topic, check out Threat Hunting AI Agents and Autonomous Tooling: What Your Board Needs to Know from our very own VP of Security Solutions, Dhruv Majumdar.

In our next article (part 3 of this series) Fleet's Adam Baali will go beyond the overview given here and provide mitigations that security teams can use today.