Authorize
Turn signed rules of engagement into approved addresses, ports, actions and testing windows.
Run scoped pentests with local tools and reviewable evidence.
Anvil is the pentest program and toolbox for an authorized engagement. It puts approved local tools in one controlled workspace, scopes them to that engagement's targets, ports and testing window, and keeps the evidence each action collects attached to the resulting finding.
From permission to proof
Turn signed rules of engagement into approved addresses, ports, actions and testing windows.
Register a tester's device and issue a time-limited lease for one engagement.
Run qualified local tools. Every operation is checked against scope before it starts.
Managers approve findings, sign off exports, retention and client notice, then close.
Authorization preview
Target, technique, testing window and signed rules are checked before an adapter can run. Out-of-scope work is refused and retained as evidence.
AUTHORIZED TO PROCEED
app.northbridge.example:443
Port scan · window open · scope signed
Assessment traffic originates from the tester's machine, not from Cybrary infrastructure.
The engagement governs targets, ports, testing windows and permitted actions before execution.
Screen context, commands, original output, artifacts, network connections and known capture gaps remain traceable from action to finding.
Mode is always written on screen. Color is never the only signal.
LAB — SIMULATED
REAL ASSESSMENT — ADAPTER-SCOPED
REAL — EXPERT MODE
Leaving a lab always requires a fresh authorized engagement and scope review. Simulated targets and permissions never transfer into real testing.
Evidence preview
Reviewers can move from a finding to the exact action, output, artifact and connection that supports it. Missing or interrupted capture is shown as a gap, never silently hidden.
4 capture channels ship today. Other channels retain their literal Building or Planned status.
Walk through the evidenceFinding ANV-014
14:03 · Scope check passed
10.42.18.24:443 · authorized window open
14:04 · Tool output captured
Command, original output and produced artifact linked
14:06 · Finding drafted
Evidence citations retained for reviewer approval
SOW intake, structured scope, rules of engagement, approvals and assignments.
Qualified tool packs, real terminal sessions, run previews and emergency stop.
Recorded actions, artifacts, findings, remediation guidance and portable packages.
Auto-Recon and copilot proposals remain scope-checked, attributed and human-approved.
Engagement rooms with team chat, shared tasks and handoffs, live presence and a timeline that can't be edited.
Findings close only after a manager approves them, with notes. Authors can't approve their own work. Tester and reviewer portals on web and desktop.
The desktop checks assignment, role, stage and testing window with Forge before every command. Refusals are kept as evidence with the reason.
Real and practice work labeled apart, rules-of-engagement fingerprints on every command, capture-coverage flags and command-to-finding bookmarks.
PDF and Word reports with approval status, manager notes, cleanup and proof codes; a printable final report for auditors; a replay of the whole engagement in time order.
The computers that ran commands, an encrypted credential store, an access map drawn only from recorded commands, cleanup tracking with a client link, and a manager sign-off before closure.
Integration status stays literal
Reviewed pentest findings and client-facing reports.
Collaborative assessments with a shared issue library.
Reporting-first teams who want to control their own deployment.
Enterprise red-team operations logging and reporting.
Customer-managed pentest programs that own the engagement record.
Aggregated vulnerability management and engineering follow-up.
Start with the public workflow, then sign in for entitlement-aware downloads, device management and engagement controls.
Finding-to-exercise
When a manager approves an Anvil finding, Forge AI drafts a private practice lab from it. Your team reviews and publishes it, trains on it, and the real retest is recorded against the same finding, so you can see what was found, who practiced and whether the fix held.
1. Approved finding
Only manager-approved findings can become exercises.
2. Private draft
Forge AI writes an emulated practice lab from customer-safe details. Hosts, addresses, credentials and commands are stripped first.
3. Human review
A person with lab creator rights edits and publishes. It stays private to your company.
4. Team practice
People trained, pass rate and time to finish show on the finding.
5. Real retest
Fixed, partly fixed or not fixed is recorded with a readiness snapshot. Not fixed? Draft an updated exercise.
Practice results come from an emulated exercise and show readiness, not proof the real system is fixed. Training never authorizes a retest; that still needs its own window and signed rules of engagement.
See the loop in a demo