hol-guard
Prevents AI agents from executing unauthorized tools, accessing secrets, or running malicious code—critical for enterprises deploying autonomous agents in pr…
Open-source antivirus for AI agents: block risky tools, secret access, prompt injection, malicious packages, MCP servers, plugins, and skills at runtime.
- Block unauthorized tool access when AI agents attempt to use secrets or credentials
- Detect and prevent prompt injection attacks targeting deployed AI agent systems
- Stop installation of malicious packages and untrusted plugins before runtime execution
Prevents AI agents from executing unauthorized tools, accessing secrets, or running malicious code—critical for enterprises deploying autonomous agents in production without sandbox constraints.
Platform teams and security engineers securing AI agent deployments against tool misuse and injection attacks.
https://github.com/hashgraph-online/hol-guard
By hashgraph-online
How to Get It
claude plugins install hashgraph-online/hol-guard
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me block unauthorized tool access when AI agents attempt to use secrets or credentials
Trust Signals Auto-scanned
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 868 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 868 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.