Less noise.More signal.
An autonomous AI agent validates findings, eliminates false positives, chains exploits into real attack paths, and delivers evidence-backed triage — not scanner noise.
Intelligent Noise Reduction
Heuristic False Positive Filtering
Multi-layer rule engine classifies findings across 8 validity levels — confirmed, candidate, needs validation, informational, likely false positive, parser artifact, out-of-scope, and false positive. Non-reportable noise is separated, not counted as vulnerabilities.
Evidence Quality Rating
Every finding receives a 6-level evidence quality score: exploited, reproducible, strong, basic, weak, or none. Know exactly how trustworthy each result is.
Catch-All Server Detection
Automatically identifies default HTTP servers that return 200 for any path — a major source of scanner false positives — and suppresses meaningless findings.
Parser Artifact Filtering
Known scanner parser artifacts (duplicate entries, encoding glitches, tool-specific noise) are identified and removed before findings reach your report.
Vuln Lab Detection
Flags intentionally vulnerable training applications and lab-only targets to prevent inflated severity reporting.
External URL Filtering
Removes non-relevant external URLs from reports, reducing noise and keeping deliverables focused on in-scope assets.
Autonomous Verification
Agent-Based Attack Planning
An autonomous AI agent builds an attack plan, prioritizes findings into proven, candidate, and chainable categories, then dispatches targeted verification payloads.
Safe Exploit Verification
7 exploit classes (SQL injection, remote code execution, path traversal, authentication bypass, Redis exposure, XXE, and SSRF) verified with safe proof-of-concept payloads producing structured exploit evidence.
Confidence-Threshold Decisions
Three-tier verification thresholds: 0.3 (suspect), 0.5 (likely), 0.8 (confirmed). Each finding is automatically promoted or demoted based on verification results.
Attack Chain Construction
Individual findings are linked into multi-step exploitation paths — LFI-to-RCE, SQLi-to-auth-bypass, SSRF-to-internal-services — revealing real-world attack scenarios scanners miss.
Context & Prioritization
Severity Context with Business Impact
AI-generated analysis adds exploitability assessment, business impact, and remediation priority per finding — so you focus on what actually matters, not just CVSS.
Next-Step Recommendations
For every finding, get actionable guidance: validate, exploit, or dismiss. Structured exploit playbooks provide step-by-step instructions per vulnerability category.
Root-Cause Deduplication
Smart deduplication groups related findings by root cause — not just title matching. Confidence scoring consolidates duplicate entries from multiple scanners.
Multi-Source Corroboration
Same finding from multiple scanners is merged into a single entry with combined sources and the best available evidence — stronger confidence, cleaner reports.
Report Integrity
Schema Validation
Every report is validated against a strict JSON schema — checking for duplicate IDs, broken references, empty findings, and structural consistency before delivery.
Secret Scanning & Redaction
Reports are scanned for accidentally exposed secrets (API keys, tokens, passwords) and automatically redacted — preventing credential leaks in deliverables.
Phase Self-Verification
The autonomous agent validates completion of recon, validation, discovery, and exploitation phases — flagging gaps so nothing falls through the cracks.