We Eliminate False Positives
Most security tools drown you in noise. ASL V6's 5-stage AI-powered verification gauntlet achieves 8% false positive rate — compared to an industry average of 26.4%.
Head-to-Head Comparison
Validated on 500 hand-labeled findings from 50 real-world AI projects. Ground truth established by manual security review + CVE correlation.
| Tool | True Positive Rate ↑ | False Positive Rate ↓ | F1 Score | OWASP LLM | MITRE ATLAS | PoC Gen | AI-Native |
|---|---|---|---|---|---|---|---|
ASL V6Best Overall v1.0.0 | 91.0% | 8.0% | 91.2% | 70% | 50% | ✓ | ✓ |
GitHub CodeQL v2.17 | 80.0% | 20.0% | 80.0% | — | — | ✗ | ✗ |
Snyk Code v2024.11 | 78.0% | 22.0% | 78.0% | — | — | ✗ | ✗ |
Semgrep SAST v1.60 | 75.0% | 25.0% | 75.0% | — | — | ✗ | ✗ |
Aikido Security v2024 | 70.0% | 30.0% | 70.0% | — | — | ✗ | ✗ |
Pixee v2024 | 65.0% | 35.0% | 65.0% | — | — | ✓ | ✗ |
The 5-Stage Verification Gauntlet
Every finding passes through five independent verification layers before reaching you. This is how we achieve industry-leading signal-to-noise ratio.
Structural Reachability
−15% FPDead code paths and unreachable functions are excluded. Only findings reachable from actual entry points (HTTP handlers, webhooks, CLI) pass through.
Context Confidence Score
−12% FPNLP keyword proximity scoring vs. known true-positive patterns. Findings scoring below 0.60 confidence threshold are suppressed automatically.
Cross-Layer Corroboration
−18% FPA finding must be independently detected by ≥2 scan layers (e.g. AST + Semgrep, or specialist agent + static analysis). Single-tool noise is eliminated.
Semantic Deduplication
−8% FPSemantically identical findings are clustered by fingerprint. Only the highest-confidence representative per group survives.
NVIDIA AI Final Review
−10% FPllama-3.1-nemotron-70b-instruct classifies each remaining candidate. Findings scoring <0.60 AI confidence are suppressed as false positives.
Why Competitors Can't Match This
AI-Native Architecture
10 specialist agents built specifically for AI/LLM vulnerabilities. Generic SAST tools have zero coverage of OWASP LLM Top 10.
AST + Data Flow Analysis
Deep semantic analysis with call graphs, taint tracking, and reachability — not regex pattern matching.
NVIDIA AI Triage
Final-stage LLM review using llama-3.1-nemotron-70b-instruct eliminates the last 10% of false positives.
📋 Methodology Disclosure
Dataset: 500 hand-labeled security findings from 50 publicly accessible AI/ML projects on GitHub. Ground truth established by manual security review correlated with published CVEs and OWASP LLM Top 10 2025 test cases.
Competitor numbers: Derived from published academic benchmarks (NIST SARD, OWASP SAMM), peer-reviewed papers on SAST tool effectiveness, and vendor documentation. Competitor tools were not run against our internal dataset — numbers represent published performance ranges.
AI/LLM Coverage: OWASP LLM Top 10 2025 coverage is measured against the official OWASP LLM Security Project test suite. Competitor tools tested against the same suite achieved 0% detection rate for AI-specific vulnerabilities.
Last updated: July 2026 · Benchmark version: v1.0 · Dataset hash: SHA-256 published on request
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