What is AI security testing?
AI security testing uses an autonomous agent to do the work a human penetration tester would: map an application, decide what to probe next based on what it sees, and try to break in. Where a traditional scanner emits a long list of possible issues, an AI agent can drive the running app, chain steps together, and confirm whether a weakness is actually exploitable.
Sintropyc runs this loop as automated penetration testing with a hard scope gate: localhost and private targets run automatically, public targets only after you confirm ownership. Nothing is guessed by the model where it matters — scope is deterministic.
Proof of exploit, not a list of maybes
The difference that matters is evidence. Sintropyc does not hand you a CVSS score and walk away — every finding ships with a recorded live exploit that demonstrates real impact in the running app. That is what turns a backlog ticket into a fix you can justify shipping today.
The agent currently proves these vulnerability classes with real effect, not reflection:
- Reflected XSS — payload injected and JavaScript execution confirmed in a real browser session.
- SQL injection — raw string builds proven against a live database response.
- Command injection — shell commands run in the sandbox with output captured as evidence.
- SSRF — server-side requests tracked from source to destination.
- Path traversal — out-of-scope file reads demonstrated against the running app.
- Leaked live API keys — found secrets validated against the live provider, confirming real exposure.
Underneath, a silent second layer runs static analysis (SAST), software-composition analysis (SCA) and dependency auditing in the same sandbox run — so dynamic application security testing (DAST) and code-level review land together.
Built for AI-generated code
Software in 2026 is increasingly written by AI assistants like Cursor, Lovable and Claude Code. That speed creates a new problem: new endpoints, new queries and new secrets land every single day, faster than any human can review. A vulnerability scanner for AI-generated apps has to keep pace with that flow.
Sintropyc is designed for exactly this. It reviews each change the way an attacker would, before it reaches production, and proves what is actually exploitable instead of drowning you in noise. For teams shipping AI-generated code, it is the security review step that finally moves at the speed of the code.
Local-first — your code never leaves your machine
Each run executes inside an isolated container on your own infrastructure and is destroyed afterwards. Your source code is never stored by us and is never used to train any model. Detection and scanning can even run offline.
Prove, fix, prove again
Finding a bug is half the job. Sintropyc closes the loop: it writes a root-cause fix, applies it, then replays the original exploit to confirm it no longer fires — a before-and-after you can keep as proof the patch held.