Intelligence Core Online

Evidence-Driven
Security.
Zero Guesswork.

Limma analyzes real attack surfaces with verified evidence, eliminating false positives and surfacing only what truly matters across your entire ecosystem.

Bypassing WAFs
0-Day Mapping
SCANNING INFRA
TARGET LOCKED
0.0%
Overall Accuracy
Measured across 62 real-world security scenarios
0.0
Test Scenarios
Real-world vulnerability patterns and configurations
0%
False Positive Rate
All findings are evidence-verified before reporting
~0.0ms
Latency Per Page
Average processing time per page under real conditions
Data aggregated from Limma Live Benchmarks (v2.1)

Security Teams Waste Time
Chasing Synthetic Noise

Legacy security tools haven't kept pace with modern web complexity. Limma bridges the gap between raw scanning and verified intelligence.

Excessive False Positives

Traditional scanners lack runtime context, burying teams under thousands of meaningless alerts.

Learn more

Manual Verification Sink

Pentesters waste up to 40% of their time manually verifying if a finding is actually exploitable.

Learn more

Critical Blind Spots

Complex evasion techniques and modern web architectures are missed by static signature matching.

Learn more

Fragmented Tooling

Security engineers string together dozens of disjointed tools, losing context across the attack path.

Learn more

Misleading CVSS Scores

Risk assessment is based on theoretical severity rather than actual real-world exploitability.

Learn more

PoC Overhead

Generating Proof-of-Concepts to convince developers requires extensive custom script development.

Learn more
Engine Capabilities

Intelligence in Action

We don't just report vulnerabilities; we execute precision sweeps and provide instant technical proof with concrete evidence.

HttpInvestigatorv1.2

Derinlemesine altyapı istihbaratı ve WAF/CDN parmak izi çıkarma...

ep_engine@auth-sess-9214A
Epistemic Logic Verified
Rust-Powered Performance
Zero-Noise Guarantee
Context-Aware Scoring
Competitive Analysis

Built Different.
Proven Better.

Core Capability
Limma
NessusBurp SuiteNucleiOWASP ZAPAcunetix
Epistemic Accuracy
4-level certainty scoring per finding
False Positive Filtering
ML-powered noise elimination
Real-time SSE Streaming
Live scan result delivery
Autonomous PoC Verification
Zero-manual proof generation
Dynamic Rule Engine
YAML/JSON hot-reload without recompilation
Reputation Engine
Domain trust scoring system
Full Automation
Partial / Manual
Not Supported
Performance Metrics

Unmatched Execution
Built in Rust

Our engine completely redefines scanner performance, merging memory safety with microsecond-level precision.

Overall Accuracy
0.00%
Industry Avg: ~65%
Measured across 62 real-world security scenarios
False Positive Rate
0.00%
Industry Avg: 45%
All findings are evidence-verified before reporting
False Negative Rate
0.00%
Industry Avg: ~30%
Tradeoff of zero-noise model prioritizing certainty over aggressive scanning
Scan Duration (62 Tests)
0.00s
Industry Avg: Minutes+
Full multi-layer analysis including runtime verification and correlation

Verify Our Claims

Inspect the test scenarios, expected outcomes, and validation methodology. Run it yourself and compare results against Limma.

Sample Finding Output
{
"target": "https://app.example.com",
"rule": "DRE-HDR-001: Missing CSP Header",
"severity": "Medium",
"certainty": "Certain",
"evidence": "Header 'Content-Security-Policy' absent in response"
}
Tested against OWASP Benchmark v1.2 / 2026 Engine Architecture

Limma follows an evidence-driven detection model that eliminates false positives by requiring runtime validation, which may introduce controlled false negatives.

Rust-powered · Tokio async runtime

Bu proje şu anda yapım aşamasındadır ve geliştirme süreci devam etmektedir.

Limma's Rust-based concurrency model and performant string parsing deliver enterprise-grade vulnerability scanning at unprecedented speed.