Yesterday, OpenAI hired the developer behind OpenClaw, the autonomous AI agent platform that’s been gaining traction in developer communities. This validates the idea that agentic AI is quickly moving from experimental side projects to mainstream adoption. The security industry needs a ready-made blueprint to secure such high-agency systems and make them production-ready. We’ve created both the solution and the blueprint — please welcome SecureClaw.
We’ve seen this pattern before with the Model Context Protocol. What starts as an experimental solution quickly becomes embedded in production enterprise infrastructure. OpenClaw and OpenClaw-inspired agents are following the same trajectory. Organizations will adopt these tools for their productivity gains, often before security teams have a chance to assess the risks.
The problem is that autonomous agents introduce a fundamentally different set of threats, as shown in our OpenClaw security deep-dive. An agent with tool access can read your files, manage your credentials, and make API calls. Without proper safeguards, that agent becomes a perfect vehicle for credential theft, data manipulation, and exfiltration — all through actions that look completely benign from a network perspective.
We refuse to let security become an afterthought.
To systematically address agentic AI security issues, Adversa AI built SecureClaw, an open source security solution designed specifically to safeguard OpenClaw AI agents. We’re releasing this as both a practical tool and a template for how AI agent security should work.
SecureClaw takes a comprehensive approach. It runs over 51 automated audit checks scanning for misconfigurations, exposed gateway ports, and weak file permissions. It includes five hardening modules that instantly secure the agent environment, like binding gateways to localhost and locking down sensitive directories. The plugin loads 15 lightweight behavioral rules directly into the agent’s context, teaching it to reject suspicious instructions and require approval for high-risk actions. It even scans for supply chain threats like ClawHavoc malware signatures in third-party skills. It also supports a kill switch preventing OpenClaw from running if protection is disabled for any reason.
But what makes SecureClaw different is how it thinks about compliance and frameworks.
SecureClaw is the first OpenClaw security tool to formally map its controls to five major agentic security frameworks. This isn’t just feature bragging. It’s essential for any organization that needs to demonstrate compliance and run agents in regulated environments.
The coverage is comprehensive. SecureClaw addresses all 10 categories in the OWASP ASI Top 10. It covers 10 out of 14 techniques in MITRE ATLAS Agentic TTPs (the remaining four are either industry-unsolved problems like prompt injection or out-of-scope issues like model poisoning). It handles all four case studies and 14 of 17 techniques from MITRE ATLAS’s OpenClaw investigation. It meets 13 of 18 CoSAI Secure-by-Design sub-requirements and eight of 11 control areas from the CSA Singapore Agentic AI Addendum.
This formal mapping matters because enterprises need auditable, framework-aligned security that fits into existing compliance programs. We built SecureClaw to speak the language that security teams and auditors already understand.
We’ve been developing SecureClaw rapidly and recently released version 2.1, addressing gaps identified in our framework mappings. These updates represent fundamental advances in how we think about agent security.
The update added memory trust levels with injection detection in cognitive files, addressing concerns about untrusted content from MITRE ATLAS and CoSAI frameworks. We implemented a proper kill switch mechanism with CLI commands and file-based checks for emergency shutdowns. Behavioral baseline monitoring now tracks tool call patterns and detects anomalous behavior.
We also tackled more subtle issues. Rule 15 now requires agents to state their plan before multi-step operations, providing reasoning telemetry. We added audit checks for default control tokens, a specific defense against the control token attacks documented in MITRE’s framework. And we introduced per-workload risk profiles (strict, standard, permissive) so security posture can match the actual risk level of different deployment scenarios.
You can read the complete details on our GitHub.
Here’s our prediction: Securing autonomous agents will be significantly harder than securing traditional applications — especially agents running locally with access to all local tools and making benign-looking API requests.
Traditional security controls struggle with agents. Network monitoring sees legitimate API calls. Endpoint protection sees legitimate file access. The malicious behavior is hidden in the semantic intent of the agent’s actions, not in the technical execution.
This is why we believe the future of agent security requires strong host-level controls that can audit what an agent does and prevent dangerous actions using both formal methods (permissions, sandboxing, resource limits) and semantic understanding (intent screening, context preservation, behavioral analysis). SecureClaw is our first step in building that capability.
The agentic AI era is here. OpenAI’s hire confirms what we’ve been seeing: These systems are moving to production faster than security solutions can keep up. We built SecureClaw to change that dynamic, making enterprise-grade agent security available from day one as an open source framework.
Explore SecureClaw on GitHub, deploy it in your OpenClaw environments, and learn more about our approach to AI agent security at Adversa AI. If you want to test your agents beyond OpenClaw, take a look at our continuous AI red teaming platform.
SecureClaw is the first comprehensive open-source security solution tailored for OpenClaw agents, with 50 audit checks, automated hardening modules, and lightweight behavioral rules.
