MILLENNIUMS.AI turns an autonomous attacker loose on your chatbots and agents — exactly the way a real adversary would — and proves every hole with a working exploit. Minutes, not weeks. Zero false alarms. And a one-click fix for what it finds.
Traditional penetration tests are slow (weeks), expensive ($10,000–$50,000), and rare (annual). Meanwhile your agents gain new tools, prompts, and data access every sprint.
And the attack surface that actually matters for AI — prompt injection, tool abuse, cross-tenant data exfiltration, runaway spend — isn't what a generic web scanner even looks for. So teams ship AI features effectively blind.
Give it your staging URL and connect your repo. No agents to install, no rules to write. It fetches your API surface and finds the AI features on its own.
A multi-agent adversary maps the surface, then probes every AI-specific weakness and proves each one with a real, re-runnable exploit — inside a throwaway sandbox that never touches your production data.
A ranked report, a working proof-of-concept per finding, and one-click draft pull requests that patch the code and upgrade the vulnerable dependencies. You review; nothing merges on its own.
A full scan runs in about an hour, a scoped one in minutes — so you test on every pull request, not once a year. Security keeps pace with shipping.
From $149/month, versus $10,000+ for a single manual engagement. Continuous coverage for a fraction of one annual test.
Every finding carries a working, re-runnable exploit. If the engine can't prove it, it doesn't report it — so your team never chases ghosts.
One click opens a draft pull request: an LLM-written code fix, or a dependency upgrade to the patched version. Don't like the fix? Tell it what to change and it retries.
Maps every finding to the OWASP LLM Top 10 and MITRE ATLAS — prompt injection, tool and agent abuse, sensitive-data disclosure, RAG poisoning, unbounded cost.
Runs only inside a throwaway sandbox, is never used to train any model, and is deleted when the scan ends — with a full chain-of-custody data trail for auditors.
Up to 95% of dependency vulnerabilities are never exploitable in your app. We prove which 5% are — with three signals the industry now treats as table stakes.
Real numbers from one scan. We layer exploit-probability (EPSS), known-exploited status (CISA KEV), and reachability — is the vulnerable code even called in your app — then let you record a VEX determination that suppresses the noise on every future scan and exports as a standards-grade audit trail. Your engineers fix what's real, and can prove they were right to skip the rest.
Generate fix PR repairs your code · Update dependencies patches vulnerable libraries · Guided regenerate lets a reviewer steer the fix. Every result is a draft pull request — a human always reviews before anything merges.
Every AI-specific finding maps to the industry-standard risk framework auditors, engineers, and insurers already recognize, and to the real technique an attacker would use (MITRE ATLAS).
Start free — no card. Point it at a staging app, get a proven vulnerability with a working exploit, and see the fix drafted for you. Then scan on every release.