The AttackForge Alternative
With AI Built In.
The self-hosted offensive security platform with AI built in. The model and the inference both run on your hardware. Nothing to wire up, nothing to leave the network.
Neuron vs AttackForge
Side by side. The differences that change a buying decision.
| Neuron | AttackForge | |
|---|---|---|
| Deployment model | Self-hosted, single binary plus PostgreSQL | Self-hosted, Docker or Podman (Enterprise tier) |
| AI inference | Included (Neuron AI module) | Not included; bring your own AI client via MCP |
| Where AI prompts go | Your hardware | Wherever your AI client routes them (typically a third-party cloud LLM) |
| Air-gapped operation | Yes, including AI | Yes, but AI requires a separate self-hosted LLM stack |
| Burp Suite integration | Right-click send from Burp | XML file import |
| AD attack path graphing | Per-engagement, collaborative (Directory module) | Not native |
| Knowledge libraries | Findings, commands, snippets, checklists, scan templates | Findings only |
| Multi-language reports and briefs | Yes, AI-drafted briefs separate from full reports | Reports only, no native multi-language |
| Engagement scaffolding templates | Full scaffold: assessments, team, QA pools, phases | Per-vuln review notes |
| Retest workflow | Round-based with peer cosign by severity, round-lock, per-finding QA log | Round-based with per-finding states and evidence upload |
| Engagement scheduling | Gantt with drag-drop, five perspectives, capacity conflict detection, Health Dashboard | Calendar with availability checker |
| Findings library | Yes (core platform) | Yes |
| Multi-scanner import | Yes (core platform) | Yes |
| Real-time collaboration | Yes (core platform) | Yes |
| Jira / ServiceNow push | Workflow Integrations module | Native |
Deployment model
AI inference
Where AI prompts go
Air-gapped operation
Burp Suite integration
AD attack path graphing
Knowledge libraries
Multi-language reports and briefs
Engagement scaffolding templates
Retest workflow
Engagement scheduling
Findings library
Multi-scanner import
Real-time collaboration
Jira / ServiceNow push
Three reasons to choose Neuron over AttackForge.
AttackForge needs an AI client. Neuron ships the AI itself.
AttackForge connects to your AI client over the Model Context Protocol. You bring Claude Desktop, ChatGPT, or Microsoft Copilot Studio. The prompts and the data the client pulls flow to wherever it runs inference. In an air-gapped environment, you also stand up a separate self-hosted LLM stack. Neuron's AI module ships the model and the inference together. Nothing to wire up, and nothing leaves the network.
AttackForge imports Burp by file. Neuron sends right from Burp.
Neuron's Burp extension sends request and response pairs into the engagement with a simple click. AttackForge has no equivalent. For testers in Burp all day, that is the difference between integrated and bolted-on.
No AD graphing in AttackForge. Neuron makes it collaborative.
AttackForge has no equivalent. Neuron's Directory module graphs AD attack paths inside the engagement. Multiple testers collaborate on the same graph in real time, each engagement keeps its own isolated data, and findings tie directly to the path. No clearing the database between projects, no single-player limitation.
AttackForge wires AI. Neuron ships it.
AttackForge's AI is implemented as a Model Context Protocol server. You bring the AI client.
AttackForge's AI MCP module connects to external AI clients including Claude Desktop, ChatGPT, Microsoft Copilot Studio, and LM Studio.
The AI you use is the AI you bring. Prompts and the data the client pulls flow to wherever it runs inference, which for most teams is a third-party cloud LLM. For air-gapped teams, that means standing up a self-hosted LLM stack like LM Studio and keeping it running alongside AttackForge.
Neuron's AI runs on your hardware. The whole model. Prompts, context, and output all stay inside your network.
Neuron's AI drafting a finding. Nothing about it leaves the network.
The same install that runs your engagements runs the AI. No separate AI vendor, no LLM infrastructure to stand up, no AI client decision pushed onto each tester.
One install. One vendor. One thing to support.
Years of approved language, plus the AI to use it.
Both platforms have a findings library. Neuron has the AI to use it. The AI module reads directly from your library. Approved Risk Statements and Remediation Guidance go in verbatim, every time. The AI generates only what is engagement-specific: finding details, reproduction steps, retest verification.
That changes the review burden. Instead of editing every paragraph the AI produces, your testers do light QA on the engagement-specific sections and trust the boilerplate they already approved.
AttackForge surfaces the library to your AI client through MCP, but exposure is not enforcement. Whether the AI actually uses your approved Risk Statement verbatim, or paraphrases it into something close but not quite, is up to the model on every prompt. The library is reference material the LLM may or may not reach for. Neuron inserts the approved language mechanically. The boilerplate that passed QA last quarter ships verbatim in every report after, every time.
Per-engagement AD graphs, collaborative in real time.
Active Directory is where most internal engagements actually live, and the standard tools for graphing attack paths were built for one tester at a time, against one database at a time. Switching engagements means clearing data and re-importing.
Neuron's Directory module runs the graph inside the engagement. Multiple testers collaborate on the same graph in real time. Each engagement keeps its own isolated data, so there is no clearing between projects and no risk of a query pulling from the wrong directory. Findings tie directly to the path.
AttackForge has no equivalent.
The schedule the team runs from. The retest the auditor trusts.
AttackForge's schedule is a calendar with an availability checker. You filter projects by month or week, scroll through availability per user, export to CSV. It tells you what is scheduled. It does not run the team's day.
Neuron's schedule is a Gantt the team runs from. Drag bars to reschedule, drop on a tester to reassign, switch between me, users, teams, by-client, and by-engagement perspectives without leaving the view. Capacity overlap surfaces as overload day counts, peak concurrent counts, and a next-free window calculation. The Health Dashboard ranks twelve categories of risk before they hit kickoff.
On retests, both platforms run rounds with per-finding outcomes. The differences live in what an auditor can verify.
Neuron adds peer-cosign gated by severity policy, so critical findings can require a second tester to verify before the round closes. Once a round moves to ready for approval, per-finding mutations freeze. The QA log captures every state change with actor, timestamp, and prior state. Retests show up as first-class allocatable scope on the Gantt alongside phases and assessments, so the planned dates land in the same view the team already runs from.
Frequently asked questions
The questions buyers ask us most when evaluating Neuron against AttackForge.
Can I migrate my data from AttackForge to Neuron?
Yes. We work directly with customers to migrate their data from AttackForge. Reach out and we will walk through your specific export and the migration path during a demo.
AttackForge runs on-prem too. What makes Neuron different?
Neuron's AI module ships the model and the inference together, both running on the same install that runs your engagements. AttackForge's AI is the Model Context Protocol, which routes prompts through whatever AI client you connect, like Claude Desktop, ChatGPT, or Copilot Studio. The data goes wherever that client runs. In an air-gapped environment you also stand up your own self-hosted LLM stack and keep it running. Outside of AI, both platforms run inside your network.
Does Neuron support the same scanner imports?
Neuron imports from Nmap, Nessus, Nexpose, Masscan, Shodan, and more. Burp Suite has a dedicated extension that sends request and response pairs from Burp directly into Neuron with a right-click. If you rely on a specific scanner format, ask during a demo and we will confirm coverage.
What about reusable team knowledge like checklists, commands, and scan templates?
Neuron's Library is your team's working knowledge base, not just a findings catalog. Approved findings live there, alongside reusable Commands, Snippets, Service Checklists, Service Notes, Bookmarks, and preconfigured Scan Templates for tools like Nmap and Masscan. Everything is shared across the org and versioned. Where AttackForge keeps a findings library, Neuron keeps the operational knowledge testers actually reach for during an engagement.
Is Neuron's on-prem deployment hard to set up?
No. Neuron ships as a single binary. Run it, and a guided init wizard walks you through license activation (online or fully air-gapped), database setup, admin user creation, and server config in one session. PostgreSQL is the only external dependency, and the wizard prints the exact commands to set it up.
AttackForge has retests too. What's actually different?
Both platforms run rounds with per-finding outcomes. Neuron adds a peer-cosign workflow gated by severity policy, so critical findings can require a second tester to verify before the round closes. Once a round moves to ready for approval, per-finding mutations freeze, and a completed round cannot be quietly edited later. Every state change writes a QA log entry with actor, timestamp, and prior state. Retests are first-class allocatable scope on the Gantt the same as phases, so planned dates show alongside the rest of the team's work.
What about scheduling pentest engagements?
AttackForge's schedule is a calendar with an availability checker. Neuron's is a Gantt the team runs from. Drag bars to reschedule, drop on a tester to reassign, switch between me, users, teams, by-client, and by-engagement perspectives without leaving the view. Capacity overlap surfaces as overload day counts and a next-free window calculation. The Health Dashboard ranks twelve categories of risk like overload, conflicts, unassigned upcoming work, deliverable readiness, and QA backlog before they hit kickoff.
See Neuron in action.
Walk through the platform, the on-prem AI, and how it deploys in your environment.