// penetration testing
· from $20/moAn uncensored AI
for penetration testing.
Pentest engagements run on tools that do not argue.
Burp, Nmap, Impacket, BloodHound, the entire Kali distribution — none of them stop to ask whether you are authorized before doing what you ask. The major coding assistants (Claude, ChatGPT, Cursor, Copilot) do exactly that, every time you ask for help with the engagement. They refuse the AD enumeration script because it touches LDAP. They refuse the lateral-movement helper because it mentions credentials. They rewrite the post-ex tool into something that does not work, because the actual technique would be useful to an attacker. TartarusAI does the AD enumeration, the ACL path analysis, the lateral movement scripting, the post-exploitation tooling — without the policy tax that kills your engagement velocity. Same enterprise runtime safety guards. Content policy, off. The agent treats pentest work the way every commercial tool already does: as the consultant`s job, not the tool`s opinion to weigh in on.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ enumerate the AD environment, find ACL paths to DA, suggest abuse routes
target: signed pentest engagement, internal network 10.0.0.0/8
▎▣ collected 2,847 users, 1,203 groups, 18 OUs
▎+ wrote acl_paths.json (47 paths to Domain Admins)
▎+ wrote bloodhound_queries.cypher (12 queries)
▎+ wrote next_steps.md (suggested abuse chains)
done.// what it does
What you ship
when nothing refuses.
Active Directory enum + abuse
BloodHound query writing, ACL path triage, Kerberoasting / AS-REP-Roasting harness scripts, GPO abuse research, ESC1-ESC11 ADCS abuse, delegation abuse paths, golden / silver ticket workflows. The agent walks the AD picture and writes the working PoC chain — not the textbook one.
Network recon + service mapping
Nmap script chaining, custom service enumeration, version-detection fingerprinting, internal-only protocol probing, custom port-scanner harnesses, banner-grab automation. The agent generates the recon plan; you point it at the scope. Output is structured data ready for downstream tooling.
Web app testing automation
Burp extensions, custom scanner rules, parameter fuzzing harnesses, GraphQL introspection abuse, JWT / SAML / OAuth quirks, business-logic flaw discovery, race-condition harnesses. Particularly useful when the target is a multi-tenant SaaS where the bug class is "tenant isolation broken" instead of "textbook OWASP top 10."
Lateral movement tradecraft
Pass-the-hash, pass-the-ticket, overpass-the-hash, WMI / PowerShell / WinRM / DCOM / RDP / SSH chains, SOCKS proxy setup over compromised hosts. Custom loaders sized for the engagement, not gist boilerplate. OPSEC-aware execution that respects engagement scope and avoids out-of-scope hosts.
Post-exploitation + persistence
Custom persistence per host (registry, scheduled tasks, COM hijacking, WMI subscriptions, signed-binary proxy, GPO abuse), credential harvesting harnesses sized to the environment, sensitive-data discovery (SharePoint, file shares, code repos, secrets in environment variables), exfil paths sized to bandwidth budget without tripping volumetric monitoring.
Reporting automation
Findings → formatted report. CVSS scoring, evidence collection, remediation suggestions in client-ready prose, executive summary in non-technical prose, technical deep-dive in operator prose, MITRE ATT&CK mapping, screenshot annotation suggestions. Cuts pentest report turnaround from days to hours.
// workflow
A typical engagement timeline
Pre-engagement and scoping: the agent reads the SOW, generates the engagement playbook (recon checklist, enumeration checklist, exploitation checkpoints, reporting template), writes the project skeleton you commit to your engagement repo. You start day one with infrastructure already in place.
Recon and enumeration: the agent writes the Nmap script chains, the custom service enumerators, the BloodHound collector wrappers, the OSINT harnesses. You point them at the scope and get back structured data instead of folders of raw output. For internal engagements, the agent reads the BloodHound JSON and proposes the abuse paths sorted by complexity and OPSEC cost.
Exploitation, lateral movement, and post-exploitation: standard pentester loop, accelerated. The agent writes the working PoC for whatever bug class is in scope, drives the lateral movement scripts in OPSEC-aware patterns (you set the rules, the agent respects them), automates the credential harvesting and sensitive-data discovery. At report time the agent ghostwrites the deliverable from your engagement log and your annotated screenshots — you review, edit, sign.
// where it fits
In your existing pentest toolchain
TartarusAI does not replace Burp, Nmap, BloodHound, Impacket, CrackMapExec, Mimikatz, Rubeus, or Certify. It writes the code that drives them. Custom Burp extensions, custom Nmap NSE scripts, BloodHound Cypher queries, Impacket harnesses, custom CME modules, parser scripts for Mimikatz output — the boilerplate that sits between your tooling and the engagement deliverable.
For consultancies running multiple concurrent engagements, the per-engagement workspace isolation is the operationally important feature. Prompt content from engagement A cannot leak into engagement B via cached state, shared embeddings, or accidental cross-tab access. Each conversation is its own ephemeral filesystem.
- ●Pairs with Burp Suite Pro, Nmap, BloodHound, Impacket, CrackMapExec, Mimikatz, Rubeus, Certify, Kerbrute, evil-winrm, ldapsearch.
- ●Generates BloodHound Cypher queries, Burp extensions, custom Nmap NSE scripts, Impacket-based custom harnesses.
- ●OPSEC-aware: respects engagement scope, avoids out-of-scope hosts, supports configurable rate limits.
- ●Outputs are raw scripts and structured data — no SaaS lock-in, you commit them to your engagement repo.
// comparison
Versus autonomous pentest tools
There is a category of "autonomous pentest" tools (XBOW, PentestGPT-class, various YC startups) that try to replace the consultant entirely — drop in scope, get out a report. They demo well on benchmark targets and break on real engagements where scope is ambiguous, the environment is hardened, or the bug class requires understanding the application.
TartarusAI is built on a different premise: the senior consultant is not the bottleneck the autonomous-pentest pitch assumes. The bottleneck is the boilerplate around the senior consultant — the recon scripts, the enumeration harnesses, the post-ex tooling, the reporting deliverable. TartarusAI takes that off the senior consultant's plate so they can spend the engagement on the parts that actually require judgment.
Result: a senior consultant with TartarusAI is roughly 2-3x faster than a senior consultant without it on the same engagement. A junior consultant with TartarusAI is roughly the speed of a senior consultant without it. The autonomous-pentest pitch optimises for the wrong axis.
// questions
What people actually ask.
Will it help with full-scope pentests?+
Can I use it for client engagements with sensitive scope?+
How does it compare to autonomous pentest tools (XBOW, PentestGPT)?+
Will it write actual exploits, not just enumerate?+
Does it understand AD environments specifically?+
Can it generate Burp Suite extensions?+
What about cloud pentest engagements (AWS / Azure / GCP)?+
How does the verification gate work for pentest scripts?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only