DapplePot

DapplePot is an AI agent security and monitoring platform designed for enterprises running fleets of autonomous AI agents. It provides visibility, control, and threat detection across the full lifecycle of agent sessions — from the first LLM call through cross-session fleet health analysis. Core functions: - Session Monitoring: Captures every LLM call, tool invocation, and decision made by an agent in real time. Each session is recorded as a timestamped event timeline that can be replayed frame by frame. - Threat Detection: Runs inline detection checks during runtime, post-session analysis, and cross-session correlation. Detection coverage is mapped to OWASP LLM Top 10 and OWASP Agentic AI Top 10 frameworks, with 147+ security sub-checks. - Session Control: Provides a live kill switch to terminate rogue sessions instantly. Allows operators to push policy updates to running agents without redeployment. Supports configurable agent scope including privilege levels, tool call limits, token budgets, and allowed LLM models. - Fleet Health Dashboard: Tracks risk scores, trust scores, and behavioral drift across all agents. Surfaces the most vulnerable agents and active threats across the fleet. - Compliance & Audit: Maintains a 90-day immutable event history for all sessions. Findings are mapped to OWASP controls to support incident post-mortems and AI governance audits. - SDK Integration: Deployed via a lightweight SDK (4 lines of instrumentation code) that wraps existing agent code without requiring architectural changes. Supports Anthropic and OpenAI-compatible LLM clients. Detected threat categories include: prompt injection, data disclosure, tool misuse, memory poisoning, privilege abuse, hallucination risk, goal hijacking, unexpected code execution, inter-agent communication abuse, and unbounded resource consumption. Alerts are routed to external channels including Slack and Microsoft Teams.