An autonomous attacker probes your AI app, proves every vulnerability with a working exploit, and drafts the fix — in minutes, not weeks. The pentest coverage you need for SOC 2, ISO 27001, PCI DSS, and HIPAA.
Scope a client target, let the engine run the engagement, and deliver a white-label report under your own brand.
Explore the pentester tool →Scan every pull request, block net-new vulnerabilities in CI, and take a one-click fix PR your engineers review.
Explore AI development →Continuously discover shadow AI, test it on a schedule, and prove what's exploitable with audit-ready evidence.
Explore Enterprise →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.
Four AI-native attacks a generic scanner will never test for — and we prove on every scan:
An attacker hides instructions in content your AI reads and walks out with data you processed for someone else. We prove whether yours can be turned against your own users — before they find out for you.
Every external LLM API, vector store, and library adds attack surface. We scan them all — then prove which vulnerabilities are actually reachable in your app, not just listed in a CVE feed.
One malicious upload can smuggle hidden instructions that make your AI leak data or take actions it never should. We test the exact RAG and tool-call paths that let it happen.
A logic flaw can let User A read or act with User B's context. We probe every tenant boundary and hand you a working exploit — not a "maybe."
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.
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.
One click opens a draft pull request — an LLM-written code fix or a dependency upgrade to the patched version. Don't like it? Tell it what to change and it retries. Nothing merges on its own.
Every finding maps to the OWASP LLM Top 10 and MITRE ATLAS — prompt injection, tool and agent abuse, 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 trail for auditors.
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.