HoneyTrap Enterprise AI Agent Security

Problem

When a local AI Agent is reachable by external tools, crawlers, or other Agents, sensitive files, credentials, API configs, and conversation context can be exposed. Traditional endpoint security relies on signatures, behavior monitoring, and automated response; Agent systems also need defenses for prompt injection, memory poisoning, tool abuse, and impersonation. HoneyTrap treats these interactions as security events rather than ordinary tool calls.

How It Works

The skill is organized around detection, response, and forensics:
- Attack fingerprinting: identifies patterns such as curl, scrapy, external Agent markers, and prompt-injection structures, then assigns risk levels.
- Counter-response: selects honeypot payloads, fake endpoints, fake keys, rejection, or delay traps from 100ms to 5000ms.
- AV defense engine: borrows AV and EDR/XDR ideas, using fingerprint libraries, behavior monitoring, heuristic rules, automated response playbooks, threat intel, and forensic reports.
- Self-protection and IP control: uses HMAC-SHA256 integrity checks, an Owner Key permission model, IP allow/deny lists, rate limiting, and owner confirmation to reduce unauthorized access and tampering.

Boundaries

It is better suited to protecting a local or private Agent’s sensitive context. It is not for deceiving real users or malicious activity. Honeypot content, webhook alerts, auto-bans, and audit logs must be configured for the deployment; losing the developer key can block unlocking and updates.