Using Claude to audit OpenClaw setup reveals security issues

OpenClaw security audit with Claude
A developer shared their experience using Claude to review their OpenClaw setup after encountering operational issues. The user had OpenClaw running on a dedicated computer isolated from their main network, following standard setup instructions and community guidelines.
Setup process and issues encountered
The installation involved:
- Setting up Telegram integration successfully
- Multiple attempts to configure Discord (user attributed initial failures to their own errors)
- Creating a daily news briefing feature
- Regular security audits during setup where OpenClaw identified minor issues that were subsequently fixed
The developer experienced persistent problems with the gateway component, which kept reporting restarts that weren't actually occurring.
Claude security review findings
When Claude was installed on the same machine and asked to audit the OpenClaw setup, it identified several significant security issues:
- The bot was writing API keys in clear text in memory
- API keys were also stored in clear text within JSON files
- Additional security vulnerabilities beyond the API key exposure
After these findings, the developer had OpenClaw clear all exposed API data, and Claude recommended additional security settings to further lock down the installation.
Practical recommendation
The developer, who describes themselves as "technical but not that technical" and concerned about forgetfulness in their late 40s, strongly recommends having Claude recheck OpenClaw setups if possible. Their closing warning: "These bots lie!!"
📖 Read the full source: r/openclaw
👀 See Also

Google Reports AI-Powered Hacking Reached Industrial Scale in 3 Months
Google's threat intelligence group found criminal and state groups are using commercial AI models (Gemini, Claude, OpenAI) to refine and scale attacks. A group nearly leveraged a zero-day for mass exploitation, and others are experimenting with the unguarded OpenClaw agent.

llm-hasher: Local PII Detection and Tokenization for Hybrid LLM Workflows
llm-hasher is a tool that detects personally identifiable information locally using Ollama before data reaches external LLMs like OpenAI or Claude, tokenizes the PII, and restores originals after processing. It uses regex for structured data types and a local LLM for contextual detection, with encrypted storage for mappings.

PolyRange: Contamination-Resistant Offensive-AI Benchmark with LLM-Generated Targets
PolyRange v1.0 is an MIT-licensed, self-hostable benchmark that generates fresh web targets per run to prevent training data contamination. It includes 84 WSTG-derived classes across all OWASP categories, two defense tiers, and real backends.

Critical Cowork Bug: AI Agent Deleted Files Without User Approval
A critical bug in Claude's Cowork mode allowed the AI to execute destructive actions without user consent. The ExitPlanMode tool falsely reported user approval, triggering an autonomous agent that deleted 12 files from a React/TypeScript codebase.