SecureClaw: Open-Source Dual-Stack Security for OpenClaw
â–Ľ Summary
– SecureClaw is an open-source security tool designed to add auditing and rule-based controls to OpenClaw AI agent environments, addressing fragmented security solutions.
– It uses a two-layer defense model: a code-level plugin for configuration hardening and a behavioral skill for real-time awareness, preventing override by prompt injection.
– The tool includes 55 automated audit checks and hardening modules to systematically secure installations against threats mapped to the OWASP Agentic Security Initiative.
– Its behavioral skill is optimized to approximately 1,150 tokens to maintain model focus, reduce costs, and ensure security directives are followed without hindering agent functionality.
– SecureClaw is positioned for enterprise adoption with mappings to MITRE ATLAS attack techniques and plans for infrastructure-level hardening and AI Red Teaming.
As AI agent frameworks become increasingly integral for automating complex tasks involving tools, files, and external services, significant security concerns emerge. These systems require robust oversight to manage what an agent can access, what changes it can make, and how teams can identify potentially dangerous actions. Addressing this critical need, SecureClaw is an open-source project that introduces comprehensive security auditing and rule-based controls specifically for OpenClaw agent environments. Developed by Adversa AI, this tool is engineered to function seamlessly with OpenClaw and compatible agents like Moltbot and Clawdbot.
According to Alex Polyakov, co-founder of Adversa AI, the existing landscape for OpenClaw security is notably fragmented. Many available tools address only isolated aspects of the problem, such as validating skills for supply chain risks, implementing data loss prevention, or hardening tool permissions. These point solutions often tackle individual threats in isolation, leaving broader vulnerabilities unaddressed.
SecureClaw adopts a unique two-component architecture: a plugin and a skill. The plugin integrates directly into OpenClaw’s plugin system, delivering automated security auditing and hardening functions. The skill component consists of a collection of rule definitions and scripts designed to operate in tandem with an agent during its execution. This dual-stack approach addresses both configuration-level assessments and operational controls that apply while the agent is active. Polyakov emphasizes that this structure tackles a fundamental weakness in alternative methods. Many competing tools are skill-only, meaning their security logic resides within the agent’s context window as natural language instructions. This creates a vulnerability where prompt injection attacks can override the security skill if an attacker manipulates the agent’s input.
To counter this, SecureClaw implements a layered defense model. A code-level plugin enforces hardening at the gateway and configuration level, while a behavioral skill provides the agent with real-time security awareness. Polyakov asserts that both layers are essential for effective protection.
The tool’s capabilities are extensive. It includes 55 distinct audit checks that evaluate an OpenClaw installation against various security conditions. Accompanying hardening modules can automatically apply corrective changes based on audit findings. The project repository provides command-line scripts to facilitate this audit-and-harden workflow. Polyakov notes that SecureClaw was built to systematically align with established security frameworks, being the first solution to comprehensively address the full attack surface as mapped to all ten categories of the OWASP Agentic Security Initiative (ASI) Top threats.
Beyond configuration auditing, SecureClaw incorporates 15 behavioral rules within its skill component. These rules are crafted to influence agent behavior during interactions with prompts, tools, and outputs. The skill is supported by nine scripts and four JSON pattern databases that enhance its detection logic. A key technical achievement, as highlighted by Polyakov, is context window optimization. The team meticulously designed the security skill prompt to use approximately 1,150 tokens. This careful optimization ensures the model reliably follows security instructions, minimizes latency and API costs per interaction, and preserves sufficient context for the agent to perform its primary functions effectively.
Looking toward enterprise adoption, Polyakov anticipates significant growth in OpenClaw usage following its acquisition by OpenAI. SecureClaw is being positioned to meet the rigorous demands of corporate environments. The latest update includes formal mappings to MITRE ATLAS agentic AI attack techniques (CoSAI) guidance and comprehensive threat modeling documentation, artifacts critical for enterprise security compliance and risk assessment. Future development will extend beyond skill-level guardrails, focusing on infrastructure-level hardening and rigorous red teaming exercises using Adversa AI’s specialized platform.
SecureClaw is freely available on GitHub for organizations and developers seeking to fortify their OpenClaw deployments.
(Source: HelpNet Security)
