Real-time security for AI agents on Moltbook
Join 50,000+ AI enthusiasts for cutting-edge insights and tutorials
Moltbook is the world's largest social network for AI agents (770K+ agents). Research shows 2.6% of posts contain prompt injection attacks targeting vulnerable agents.
This toolkit protects your agent from hijacking, credential theft, and manipulation.
Real threats detected and blocked on Moltbook
| Attack Type | Risk |
|---|---|
| Jailbreak attempts | 🔴 High |
| Credential extraction | 🔴 High |
| Data exfiltration | 🔴 High |
| System prompt extraction | 🔴 High |
| Role hijacking | 🟡 Medium |
| Encoded payloads | 🟡 Medium |
When your agent runs, the security scanner protects it in real-time:
# Inside the agent runtime (tools/agent/runtime.py)
def _process_post(self, post):
is_safe, scan_result = self._scan_content(post.content) # Every post is scanned
if not is_safe:
return None # Malicious content never reaches your LLM
# ... process safe contentWithout this toolkit: Your agent processes malicious posts and risks leaking API keys or getting hijacked.
With this toolkit: Threats are detected and blocked before they ever reach your LLM.
git clone https://github.com/NirDiamant/moltbook-agent-toolkit.git
cd moltbook-agent-toolkit
pip install -r requirements.txt
# Setup (interactive wizard)
export MOLTBOOK_API_KEY="your_key"
export ANTHROPIC_API_KEY="your_key"
./moltbook init
# Deploy
./moltbook deploy --directDeploy your own dashboard to track threats in real-time.
Streamlit Cloud (Free, 2 min):
- Fork this repo
- Go to share.streamlit.io
- Set app path:
dashboard/streamlit_app.py - Add secret:
MOLTBOOK_API_KEY = "your_key" - Deploy
Local:
MOLTBOOK_API_KEY="your_key" streamlit run dashboard/streamlit_app.py./moltbook init # Setup wizard
./moltbook deploy # Deploy agent
./moltbook security # View security incidents
./moltbook security --scan # Scan for threats
./moltbook security --html report.html # Export report24 modules across 6 protection layers:
- Critical: Output scanner, error sanitizer, log redactor
- AI Firewall: Llama Guard + LLM Guard + pattern matching
- Platform: Memory sanitizer, egress firewall, credential monitor
- Social: Social engineering detection, reputation protection
- Data: Exfiltration prevention, financial safety
- Infrastructure: Docker isolation (cap_drop ALL, read-only fs)
from tools.security import SecurityManager
security = SecurityManager(level="standard")
result = security.scan_input(user_content)
if result.blocked:
print(f"Blocked: {result.reason}")- Agents Towards Production — Production-grade GenAI agent tutorials
- GenAI Agents — AI agent implementations from simple to complex
- RAG Techniques — Comprehensive RAG guide
- Prompt Engineering — Prompting strategies collection
Apache 2.0 — see LICENSE
This toolkit is built in good faith with a genuine desire to help developers secure their AI agents. However, security is an ongoing battle — every lock has someone trying to pick it.
We cannot guarantee this will stop all attacks. Attackers evolve, new techniques emerge, and no security solution is bulletproof. This toolkit raises the bar significantly, but determined adversaries may still find ways through.
Use this as one layer in your security strategy, not your only defense. Stay vigilant, keep your dependencies updated, and monitor your agents in production.
By using this software, you accept that the authors are not liable for any security incidents, damages, or losses that may occur.