Unrestricted. Uncensored. Unlimited real-time AI coding environment, designed to streamline development of cyber attack weapons, zero-day exploits, spyware, malware, and offensive cyber capabilities.
CodePulse — The AI-Native Coding Environment Built for Offensive Cyber Operations. Accelerate zero-day research, exploit development, custom malware, and red team tooling with an unrestricted, real-time AI that understands the full offensive security stack.
Four autonomous AI stages working in sequence. Each stage feeds the next. One click runs the entire pipeline.
Vulnerability Scan
Multi-agent AST + semantic analysis across all files. CWE mapping, CVSS scoring, confidence thresholds.
Exploit Generation
Sandboxed PoC generation. gVisor + seccomp isolation. MITRE ATT&CK mapped. Never destructive.
Attack Chain
Chain individual vulns into multi-step kill paths. Probability scoring. Full MITRE kill-chain mapping.
Remediation Engine
AI-generated patches as unified diffs. OWASP + NIST compliance mapping. PR-ready output.
Every vulnerability travels a precise pipeline — ingested, analyzed, stress-tested, and weaponized into a fully-mapped exploit path.
Code Ingestion
Repository mapped & ranked
Source files are ingested, tokenized, and ranked by vulnerability likelihood. High-risk modules are flagged for deep analysis.
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Static Analysis
AI agents hunt for patterns
Multi-agent swarm scans each file for vulnerability patterns: injection sinks, unsafe memory ops, logic flaws, and taint flows.
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Dynamic Fuzzing
Inputs stress-tested at runtime
Schema-aware and grammar-based fuzzing generates thousands of mutation payloads, triggering crashes, memory corruption, and anomalous states.
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Exploit Path Generation
Attack chains confirmed
Confirmed findings are mapped to exploit paths and multi-step attack chains with MITRE ATT&CK tagging, PoC payloads, and exploitability scores.
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Built for
From vulnerability discovery to validated exploits — an autonomous engine that thinks like an attacker and remediates like a defender.
Parallel autonomous agents analyze files simultaneously, ranked by vulnerability likelihood.
Automatically builds full attack chains with PoC payloads and step-by-step exploitation guides.
Tracks untrusted data from source to sink across function boundaries and complex call graphs.
Interactive graphs map vulnerability chains, sanitization points, and exploitable paths.
Secondary AI agents cross-validate findings, dramatically reducing noise in reports.
Context-aware fix recommendations with secure code generation and compliance mapping.
Not just pattern matching — semantic understanding of code behavior, data flow, and exploitability across 13 languages.
Multi-Language Support
Transformer-based models (CodeBERT / GraphCodeBERT) fine-tuned for vulnerability detection with deep code understanding beyond pattern matching.
Abstract Syntax Tree parsing and Control Flow Graph construction to model every execution path through the codebase.
Full taint tracking from untrusted sources to dangerous sinks, across function boundaries and inter-procedural call graphs.
Pattern matching against CVE databases, OWASP Top 10, SANS Top 25, and the full CWE taxonomy with confidence scoring.
Annotated output with severity scores, exploitability metrics, and confidence levels for every identified vulnerability.
Real-time analysis via VS Code and IntelliJ plugins. Seamless integration into DevSecOps pipelines with GitHub/GitLab support.
Analysis Input Modes
Single File
Paste or upload any source file
Full Repository
GitHub / GitLab integration
Zip Upload
Upload entire codebase archives
IDE Plugin
VS Code & IntelliJ real-time
16 vulnerability classes — from classic injection to emerging AI/ML threats — detected, classified, and scored with CVSS v3.1/v4.0.
Secondary LLM agent re-analyzes each finding within full code context — eliminating false positives that pattern matchers produce.
Feedback loop from confirmed/rejected findings continuously retrains detection heuristics, improving precision over time.
Built-in triage board with human-in-the-loop review, accept/reject controls, and annotated audit trails for compliance.
16
Vulnerability Classes
97%+
Detection Confidence
<3%
False Positive Rate
CVSS v4.0
Latest Scoring Standard
Ethical Use & Authorization Required
All exploit generation capabilities are strictly for authorized security testing within scope-defined engagements only. Users must confirm written authorization for every target system before using any generated artifacts. AVIRE enforces an authorization verification workflow — generated exploits are watermarked, audit-logged, and scoped to approved targets. Misuse violates our Terms of Service and applicable law (CFAA, Computer Misuse Act, etc.).
Autonomous PoC generation, multi-hop attack chain construction, and MITRE ATT&CK mapping — all within a sandboxed ethical framework.
Kill Chain Stage Identification
Lockheed Martin Cyber Kill Chain® · MITRE ATT&CK® technique auto-mapping
Sandbox Testing Environment
Containerized · Docker-based · Network-isolated
Difficulty Rating & Time-to-Exploit
Per-vulnerability attacker effort estimate
Graph-based attack modeling from entry point to crown jewels — with probability scoring, alternative path suggestions, and MITRE ATT&CK mapping.
Node
SSRF
VulnDescription
SSRF via unvalidated redirect. Reaches AWS metadata service.
MITRE ATT&CK
T1552.005 · Cloud Instance Metadata API
Exploit probability: 91%
Internet → SSRF → AWS Creds → DB → RCE → Crown Jewels
Internet → IDOR → SQLi → DB → Crown Jewels
Graph Node Types
Vulnerabilities
Exploitable weaknesses with CVSS scores
Assets & Hosts
Servers, services, cloud resources
Credentials
Keys, tokens, password hashes
Network Paths
Lateral movement & routing edges
Crown Jewels
High-value targets and objectives
Export Formats
MITRE ATT&CK Navigator
Executive PDF Report
Mermaid Diagram
From detection to merged PR — automated fix generation, compliance mapping, patch validation, and effort estimation in one pipeline.
// ❌ VULNERABLE const query = "SELECT * FROM users WHERE id = " + req.params.id; db.execute(query);
PR #847 · Ready for review
fix(security): parameterize SQL query in /api/search — resolves CVE-2024-XXXX
Remediation Steps
Regression Tests
Compliance Controls
Logical and physical access controls
Bespoke software secure development
System acquisition, dev & maintenance
Data Security protection controls
Input Validation First
Validate & sanitize all inputs at the boundary before processing.
Defence in Depth
Layer authentication, authorization, and monitoring at each tier.
Least Privilege Principle
Grant only the minimum permissions required for each component.
Fail Secure
Errors and exceptions must default to a denied / locked-down state.
NVD · CISA KEV · ExploitDB feeds fused with MITRE ATT&CK actor profiles, geolocation heatmaps, and industry-specific threat modeling.
PAN-OS GlobalProtect command injection — actively exploited
Ivanti Connect Secure RCE — nation-state actors observed
ConnectWise ScreenConnect auth bypass — mass exploitation
JetBrains TeamCity auth bypass — PoC public
Ivanti ICS authentication bypass — chained with CVE-2024-21887
Whether you need a 10-second snippet scan or a 90-day red team engagement — AVIRE has a dedicated workflow for every security use case.
Instant vulnerability detection on any code snippet or uploaded file — with natural-language AI explanations and one-click PoC generation.
< 8s
Avg time to first finding
20+
Languages supported
✓
PoC auth gate
< 8s
Avg time to first finding
20+
Languages supported
✓
PoC auth gate
Six integrated capabilities — from AI multi-agent analysis to downloadable board reports — forming a complete offensive and defensive intelligence engine.
Multi-agent LLM orchestration
Multi-agent LLM pipelines simultaneously analyze ASTs, control-flow graphs, data-flow traces, and runtime behavior — producing findings no single-pass scanner can match.
Four isolated layers — frontend, backend, AI engine, and sandbox — engineered for scale, security, and full auditability.
App Router-based Next.js 14 frontend with Tailwind CSS, Radix UI primitives, and shadcn/ui components. React Query handles server state; Zustand manages client state. Dark mode only.
Zero-trust security
JWT + RBAC + audit logs on every action
Observability
OpenTelemetry traces · Prometheus metrics · structured logs
pgvector VulnDB
Local CVE/NVD/CWE with vector similarity search on code
Sandbox isolation
gVisor + seccomp + network-deny-all per container
Each AI agent is bound to a typed system prompt with explicit output schemas, safety invariants, and zero tolerance for freeform prose.
JSON-only output
No prose ever leaves an agent
sandbox_required: true
All PoCs flagged for isolation
No destructive payloads
Hard-blocked at prompt level
Real CWE / MITRE IDs only
Hallucinated IDs cause rejection
System Prompt
Enforced JSON Output