Built for AI Agents,
Not Chatbots.
Tool chains, multi-step attacks, memory poisoning, privilege escalation. Agent-specific security probes designed for how agents actually work — not generic prompt tests.
Why agents need different security
Chatbots respond to prompts. Agents take actions — calling tools, writing files, executing code, delegating to sub-agents. Each capability is a new attack surface that traditional LLM security testing misses.
Agents can call tools. Attackers chain them — read memory, write files, post data. Our probes detect unauthorized tool transitions.
Agents maintain state across steps. Attackers exploit this with multi-turn jailbreaks that an isolated prompt test would miss.
Agents remember past interactions. Poison once, exploit repeatedly. We test memory corruption across conversation boundaries.
Agents delegate to sub-agents. A vulnerability anywhere in the chain compromises the entire system. We trace the full delegation tree.
Agent-specific security capabilities
Privilege Escalation Simulator
Tool chain risk analysis across 14 tool nodes and 13 risky edges. Detects dangerous cross-tool transitions like browser.fetch → filesystem.write → exec.run in a single session.
MCP Audit Log
Every MCP tool evaluation logged — policy decisions, risk scores, findings. Full audit trail for Model Context Protocol communications.
Multi-Agent Probes
Tests for cross-agent contamination, memory poisoning between sessions, and supply chain risks across delegated agent hierarchies.
Tool Chain Risk Graph
Graph-based risk scoring that maps every tool transition. Weighted by node risk and edge risk, capped at 1.0 for auditor-grade severity assessment.
Memory Poisoning Detection
Injects malicious content designed to corrupt agent memory and context. Tests if harmful instructions persist across conversations and influence future responses.
Supply Chain Testing
Validates agent autonomy tiers, delegation parent tracking, and AiBOM (AI Bill of Materials) integrity across the agent supply chain.
Most AI security tools test chatbots.
Orithos tests agents — tool chains, memory, multi-step attack chains, and delegation hierarchies.