// Product
Vuln-AID
Vulnerability AI Discovery
Find what scanners miss. Validate what matters.
Eliminate the noise.
// The Problem
Traditional Scanners Are Broken. You Know It.
You run a scan. You get 2,000 findings. Maybe 50 are real. You spend three days triaging, only to discover the critical vulnerability was in a category your scanner doesn't even cover. Meanwhile, your team treats scan results like spam — ignored until an auditor asks.
Most security tools operate in a single dimension — pattern matching or signature lookup. They don't understand code flow, can't reason about intent, and have no way to verify whether a finding is actually exploitable. The result? Alert fatigue, missed vulnerabilities, and a false sense of security.
// The Solution
Multi-Tier Analysis That Thinks Like a Researcher
Vuln-AID doesn't just scan — it investigates. Multiple analysis tiers work together, each adding depth. Static analysis catches the obvious. AI reasoning evaluates context and intent. Dynamic validation confirms exploitability. Every finding is scored, calibrated, and ranked — so you fix what matters first.
It supports 13 programming languages out of the box and runs 581 detection rules at every tier — covering all 838 Base+Variant CWEs, which are the weakness types with concrete, detectable code patterns. (MITRE's full catalog is larger, but the rest are abstract Categories, Pillars, and Views — groupings, not detectable bugs.) No configuration per language. No separate tools for different stacks. One system, complete coverage.
Scanners give you alerts. Vuln-AID gives you evidence. Every finding is validated, calibrated, and prioritized — because a vulnerability report full of noise is worse than no report at all.
Signal over noise.
Evidence over assumptions. Depth over breadth.
Proven Against Real-World Targets
Benchmarked against the NIST Juliet suite (20,000+ cases), the OWASP Benchmark, OpenSSL, and 70+ real-world open-source projects and library versions — from Kubernetes and Tomcat to axios, lodash, and Log4Shell — across 13 languages. Not synthetic-only scores — measured results from real codebases.
// Capabilities
What Vuln-AID Brings to Your Security Program
581 Detection Rules Across 3 Levels
Pattern matching, deep code analysis, and data-flow tracking — every rule operates at multiple depths. What one level misses, the next catches.
Multi-Model AI Analysis
Multiple AI models analyze each finding independently, then vote. Disagreements trigger deeper investigation. No single model's blind spots become yours.
13 Languages, Zero Configuration
C, C++, Python, Java, JavaScript, TypeScript, Go, Kotlin, Swift, PHP, Ruby, C#, Rust — and binary analysis via Ghidra. Auto-detected. Nothing to configure.
Dynamic Validation
Findings don't stop at "possible." Instrumented test harnesses, symbolic execution, and fuzzing confirm whether a vulnerability is actually exploitable. Proof, not probability.
Calibrated Severity Scoring
Probability-calibrated confidence scores with severity-adjusted thresholds. Every finding includes a calibrated probability — not just "high/medium/low" guesswork.
Threat Intelligence Integration
Findings are enriched with real-time data from EPSS exploit prediction, CISA Known Exploited Vulnerabilities, and threat intelligence feeds. Know which vulnerabilities are being actively exploited in the wild.
Organizational Memory
Suppression rules, pattern tracking, and historical context persist across scans. Vuln-AID learns your codebase — previously triaged findings stay triaged. No re-work.
SARIF & CI/CD Native
Standard SARIF 2.1.0 output uploads straight into GitHub Advanced Security as code-scanning alerts — inline on every pull request — and feeds VS Code or any SARIF-aware tool. A built-in PR mode diffs against a baseline so only newly introduced vulnerabilities surface, and an exit-code gate can fail the build on new findings. Works in GitHub Actions out of the box, or any CI (GitLab, Jenkins, CircleCI) via SARIF and exit codes.
// Why Vuln-AID
What Makes It Different
Research-Grade Analysis, Production-Ready Speed
Techniques borrowed from academic vulnerability research — adapted for real-world codebases. Full campaign on a medium project runs in minutes, not days.
Complete CWE Coverage
838 out of 838 MITRE Base+Variant CWEs — that's 100% of the weakness types with detectable code patterns, not a slice of them. MITRE's catalog also lists abstract Categories, Pillars, and Views, but those are thematic groupings rather than detectable bugs. No blind spots in your coverage matrix.
Novel Detection Engines
Three proprietary analysis engines detect vulnerability classes that traditional tools structurally cannot — implicit dependency assumptions, security gate bypasses, and specification gaps.
Adaptive Re-Analysis
When AI models disagree or confidence is borderline, Vuln-AID automatically re-analyzes with additional context. Ambiguous findings get resolved, not ignored.
Git History Awareness
Analyzes commit history to detect patch regressions, variant vulnerabilities, and recently introduced weaknesses. Knows what changed and when — not just what exists now.
CVSS 4.0 Scoring
Automatic CVSS 4.0 vector generation with CWE-to-metric mapping. Findings come with industry-standard severity scores — ready for compliance reporting.
Responsible Disclosure Workflow
Built-in 9-state disclosure lifecycle tracking, vendor advisory generation, and evidence archiving with cryptographic verification. From discovery to disclosure, managed in one system.
Budget-Controlled Campaigns
Set a dollar budget for AI analysis. Vuln-AID optimizes model routing to maximize coverage within your spend. Full transparency on costs — no surprises.
Zero-Config Scanning
Point at a repository and go. Automatic language detection, dependency resolution, and analysis tuning — no YAML files to write, no rules to configure.
Real-World Benchmark Results
Measured against real codebases — not synthetic-only benchmarks. On an independent, held-out external-CVE corpus, Vuln-AID recalls roughly 87% of known vulnerabilities. On the NIST Juliet C/C++ suite it holds 95% precision, and it has been exercised across 70+ real-world projects and library versions — plus the OWASP Benchmark and OpenSSL — spanning 13 languages. Clean-corpus false-positive rate stays under 5%.
Calibrated confidence on every finding. Evidence over assumptions — across 13 languages.
// How It Works
From Source Code to Validated Findings
Vuln-AID runs a multi-tier campaign against your codebase. Each tier adds depth — pattern detection, AI-powered reasoning, data-flow analysis, and dynamic validation. Findings accumulate evidence at every stage. The result is a calibrated, prioritized report you can actually trust.
1. Point It at Your Code
Tell Vuln-AID where your source lives. It auto-detects languages, sets up analysis infrastructure, and begins. No configuration files. No per-language setup.
2. Multi-Tier Analysis Runs Automatically
Four analysis tiers execute in sequence — each adding depth and confidence. Pattern detection, AI-powered reasoning, data-flow analysis, and dynamic validation. Findings accumulate evidence at every stage.
3. Fix What Matters
Calibrated, prioritized results in standard SARIF format. Every finding includes evidence, confidence score, CWE classification, and CVSS vector. Integrate into your existing security workflow or CI pipeline.
Built for Scale
From single-file audits to enterprise monorepos. Vuln-AID scales with your codebase and your budget.
Ready to Find What Others Miss?
Vuln-AID is currently in beta testing. Request early access to test it against your codebase — and help shape the future of vulnerability discovery.