Run the real attack, then re-run it after the fix
BASzy™ AI is the attack-validation engine inside CVEasy Red. It fires 158,271 real payloads, chains multi-step attacks the way an adversary would, maps every technique to MITRE ATT&CK, and re-tests each fix to prove the door is actually closed. All on your hardware.
- Payloads
- 158,271, fired as deterministic code
- Engine
- cveasy-ai-v1, reasoning on your hardware
- Framework
- Every technique mapped to MITRE ATT&CK
- Footprint
- Agentless · macOS & Linux · zero telemetry
BASzy AI · A list is a hypothesis · 0:42
A scanner says maybe, BASzy says reachable
BASzy runs the multi-step chain end to end. When it reaches the objective, that is proof the exposure is exploitable in your environment, not a theoretical CVSS number. Fix it, and BASzy re-runs the same chain to prove the door is closed.
Every run is scope-enforced and audit-logged. See how validation feeds back into scoring on threat actor simulation.
158,271 payloads covering every surface
From web injection to cloud privilege escalation, each module is AI-orchestrated, scope-enforced, and MITRE ATT&CK tagged. The local cveasy-ai-v1 engine adapts the plan when a defense blocks it.
Web application
Injection, logic flaws, authentication weaknesses, API security, session attacks, and protocol-level vulnerabilities across every major web surface.
Network & infrastructure
Service discovery, lateral movement simulation, protocol attacks, and infrastructure enumeration against your real network topology.
Authentication & auth bypass
Token forgery, session hijacking, OAuth misconfiguration, and credential-based attack paths. The ones most scanners will not touch.
Cloud security
Privilege escalation paths, misconfigured storage, and IAM enumeration across AWS, Azure, and GCP environments.
Post-exploitation
Persistence techniques, privilege escalation, data exfiltration paths, and C2 simulation: what happens after the initial breach.
Advanced & emerging
Adversarial ML attacks, LLM injection, supply chain simulation, mobile surfaces, and evasion techniques. The full attack surface, including the corners scanners skip.
Built for authorized red team operations. Scope boundaries and target authorization are enforced before any module executes. Every action is audit-logged with timestamp, operator, and output. BASzy is a tool for testing your own infrastructure, not someone else’s.
Finds what has no signature yet.
AutoFuzz is BASzy’s proprietary fuzzing engine. It generates intelligent payloads from target behavior, mutates inputs across protocols, and surfaces exploitable conditions traditional scanning misses entirely.
AI-adaptive payload generation that adapts to target responses. Not random fuzzing: structured, protocol-aware mutation guided by the local cveasy-ai-v1 model.
Traditional scanners match known CVEs. AutoFuzz finds what they cannot: logic flaws, auth bypasses, and injection paths unique to your application.
Every payload generated and executed locally. No cloud dependency. No telemetry. Your zero-day findings stay on your machine.
158,271 payloads for the price of electricity.
BASzy fires its 158,271-payload, 108-CVE library as deterministic code, zero AI tokens. The local cveasy-ai-v1 model reasons only where it has to. A cloud-LLM pentest pipeline pays input and output tokens for every single validation. $100,000 of a frontier LLM buys 56,000 deep validations. BASzy runs a billion.
| Per-validation workload | Frontier LLM ($100K) | BASzy ($100K) | Advantage |
|---|---|---|---|
| Simple · 10K tokens | 1.1M | ~1.0B | 900× |
| Agentic pentest · 50K tokens | 222K | ~1.0B | 4,000× |
| Deep multi-agent · 200K tokens | 56K | ~1.0B | 18,000× |
Frontier LLM priced at $5/M input, $25/M output (80/20 input-weighted agentic blend = $9/M). BASzy library fires deterministically at zero token cost; local-model reasoning estimated at electricity. Validation counts scale with a $100,000 budget.
Four commands run the full engagement lifecycle
From the command line to a board-ready report. Every result is logged with timestamp, technique ID, and detection outcome.
Recon
Discover services, endpoints, and technologies. Results inform the AI attack plan.
Plan
Local cveasy-ai-v1 generates a phased attack plan. MITRE ATT&CK techniques selected per module and target profile.
Execute
Runs the full module suite within scope. Each result logged with timestamp, technique ID, and detection outcome.
Report
HTML report with executive summary, technical findings, detection gaps, and remediation priorities ranked by risk.
Web GUI included: baszy gui
Not a CLI person? Launch the web dashboard on port 8443. Full engagement management, live module output, report viewer, and model management, in the browser.
Two tools, one closed loop
CVEasy AI and BASzy™ AI share one local engine. The output of one feeds directly into the other, and detection gaps land back on the remediation queue.
- ✓Ingests and scores your full CVE inventory
- ✓TRIS™ score: real priority per asset
- ✓Triage queue assigns ACT / ATTEND / TRACK / MONITOR bands
- ✓Asset inventory exports to BASzy™ AI
- →Receives asset inventory from CVEasy AI
- →AI builds attack plans targeting your CVEs
- →Runs 158,271 payloads, MITRE ATT&CK tagged
- →Detection gaps feed back as ACT triage items
Most vulnerability programs stop at the patch list. CVEasy + BASzy closes the full loop, from discovery and risk scoring to adversary validation and detection-gap evidence. One platform, the same local AI engine, and zero data leaving your network.
Stop guessing and run the attack
BASzy™ AI ships as the attack-validation half of CVEasy Red. See it validate, chain, and re-test against your own estate.
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