The problem
Agents don’t just answer — they act. And the risk comes from three places.
- Checks once enforced by code become soft when replaced by model judgment.
- Unverified design assumptions — identity, user input, tool results — leave gaps.
- Models, MCP tools and Skills in the supply chain carry defects and malicious content.
All three failure modes share one trait: they only show up against this agent’s own business rules.
The JANUS platform
Security for AI agents, across their whole lifecycle.
All three run on one rule baseline — the business rules your agent must obey, derived from your code and docs, confirmed by your team. What we test before launch is exactly what we enforce at runtime.
Pre-launch
One rule, followed end to end
“Cumulative refunds per order must not exceed the amount paid.”
Derived from your code and docs — confirmed by your team.
REVIEW · supply-chain
Refund tool permissions checked.
PENTEST · pre-launch
Repeated requests, multi-turn manipulation, concurrency — bypass attempts fail.
RUNTIME MONITOR · in production
Limit verified in real time. Violating request: BLOCKED · linked to rule
One rule. Three enforcement points. One audit trail.
Proof, not adjectivesSee the full track record →
Findings mapped toOWASP LLM Top 10NIST AI RMFMITRE ATLAS
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