10-Stage Adaptive Engine

Before any attack payload is sent, the engine thoroughly fingerprints the target. This stage identifies the technology stack, server software, frameworks, WAF/CDN presence, TLS configuration, and application behavior patterns. Every subsequent stage uses this profile to make informed decisions.

The engine enumerates every attackable surface: endpoints, parameters, input vectors, authentication boundaries, and API contracts. Surfaces are prioritized by exploitability and business impact so the most critical paths are tested first.

The AI creates a prioritized attack plan, selecting vulnerability classes that match the detected technology stack and ranking tests by severity and exploitability. The plan is not static — it is continuously revised as new intelligence arrives from later stages.

The engine executes the attack plan autonomously, sending context-aware payloads at a rate-controlled pace. Every request-response pair is captured with full headers, body, and timing data. Session state and authentication tokens are maintained across tests.

Every response is analyzed for four possible outcomes: confirmed vulnerable (evidence captured), filter detected (WAF/input validation identified), partial signal (response differs from baseline in meaningful ways), or no signal (parameter tested, no indication of vulnerability). Partial signals are treated as leads and fed back into the loop.

This is the core of the "Never Give Up" engine. The AI evaluates every new finding and decides the optimal next action: deep-dive on the same surface, lateral pivot to similar surfaces, chain pivot to combine findings, escalation pivot to test for higher impact, or technology pivot when new tech is discovered. No human intervention required.

See full AI Pivot Logic documentation →

When a filter or WAF blocks a payload, the engine does not give up. It identifies the filter type, selects the appropriate bypass level, generates an adapted payload, applies encoding layers, and retries. Eight escalation levels ensure every known bypass technique is attempted before marking a parameter as exhausted.

See full Payload Adaptation documentation →

Individual findings are correlated and linked into multi-step attack chains. An SSRF finding combined with internal service discovery becomes a potential RCE path. An information disclosure combined with an authentication bypass becomes full account takeover. The AI validates each chain step to ensure the full path is executable.

See full Attack Graph documentation →

Every confirmed finding receives a complete evidence package: raw HTTP request, raw HTTP response, extracted data, timing information, SHA-256 hash, RFC 3161 timestamp, and a copy-paste reproducible PoC script. Evidence integrity is maintained through cryptographic hashing at capture time.

See full Evidence Chain documentation →

The AI writes professional penetration test reports: executive summaries for business audiences, technical findings with CVSS scores and evidence, attack chain narratives showing step-by-step exploitation, specific remediation guidance, compliance mapping, and a full evidence appendix. Reports are generated in PDF and HTML formats.