Declawed: An Advanced Community-Driven Malware Scanner for ClawHub SKILL.md Files
Declawed is a security tool designed to scan SKILL.md files uploaded to ClawHub. It focuses on detecting malicious content including prompt injection, info stealers, and other threats within markdown files. The project emerged in response to a rise in malicious content being introduced into the ClawHub ecosystem.
Initially attempting to compete with OpenClaw's partnership with VirusTotal, Declawed showcases superior detection capabilities for advanced threat payloads. This is achieved through novel prompt injection detection and ascii smuggling techniques, which outperformed those of VirusTotal in certain tests.
Central to Declawed's effectiveness is its community-driven nature. It utilizes YAML files to allow users to build and expand the detection rulesets dynamically, catering to the continually evolving landscape of AI and cyber threats. Additionally, the platform supports agent-driven workflows alongside regular user interactions, with functionalities allowing agents and humans to comment and vote on scan results. A unique reverse-captcha system ensures proper registration distinguishing between human and agent registrants.
Additional features include the integration of STIX and TAXII standards to support threat intelligence feeds, offering companies a mechanism to integrate this intelligence with their Security Information Event Management (SIEM) and Extended Detection and Response (XDR) tools.
📖 Read the full source: r/openclaw
👀 See Also
Malicious PyTorch Lightning Package Steals Credentials and Worms npm Packages
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OpenAI's June 2026 Threat Report: AI Agents Used for Malicious Activities
OpenAI's latest threat report details how AI agents are being used for disinformation, phishing, and fraud, with specific incident data and mitigation strategies.
Multi-Message Prompt Injection: The "Fictional Creature" Attack Pattern Against Claude
An attack that builds a fictional rule over three messages, then summons a ghost to activate it — each message harmless in isolation. The pattern is converging independently among attackers.
Security Benchmark: 10 LLMs Tested Against 211 Adversarial Probes
A security researcher tested 10 LLMs against 211 adversarial attacks, finding that extraction resistance averages 85% while injection resistance averages only 46.2%. Every model failed completely on delimiter, distractor, and style injection attacks.