live · v0.9.1 · MIT
star on github pypi · ptai

Find it. Chain it. Prove it.

Autonomous AI pentesting that finds bugs, chains them, and proves them safely. Runs on your machine. Nothing leaves your box.

$ pip install ptai
copied to clipboard
191 security tools 11 specialist agents 32 MCP tools SARIF · CI/CD native
0 security tools 0 specialist agents 0 chain templates SARIF + CI/CD native MCP protocol
the difference

Scanners flag findings. pentest-ai weaponizes them.

Burp, Nessus, and Nuclei give you a flat list of issues. We connect them into multi-step attack paths, score every chain, and validate each step with a safe proof-of-concept.

scanner output · 47 findings flat
6.1 highest single CVSSnoise · manual triage required
MED/api/proxy?url= · external fetch5.4
MEDIMDSv1 metadata reachable6.1
LOWIAM node-role over-privileged4.3
HIGHaws-auth ConfigMap: cluster-admin7.8
MEDsecrets readable in prod ns5.9
triage queue: 47 tickets~3 days
no chain context · no PoC · no blast radius
pentest-ai chain · CRITICAL 9.8 chained
9.8 chain severitysignal · safe PoC per step
01SSRF: /api/proxy?url=entry✓ PoC
02→ IMDSv1 · role STS credspivot✓ PoC
03→ assume node role · eks-nodelateral✓ PoC
04→ aws-auth: cluster-adminescalate✓ PoC
05→ kubectl * · prod compromiseobjective✓ PoC
report: 1 chain, 5 validated steps~4 min
SARIF + Sigma + MITRE ATT&CK map shipped
signature moment

Every attack chain, drawn for you

Nodes, edges, proof-of-concept per step. No more copy-pasting between tools.

01
entry
SSRF via image proxy
02
pivot
IMDSv1 → STS creds
03
lateral
assume node role
04
escalate
aws-auth: cluster-admin
05
objective
prod EKS compromise
0%
drawn
live output

Watch a real engagement in motion

Recon, exploit, chain, validate, report. Fully auditable. Human approval at every risky step.

pentest-ai — staging
chain.yaml
recon.log
pentest-ai@ops:~/engagements/0x4f3b2a ssh · 00:00:00
LIVE ·findings 0 ·CRITICAL 0 ·SARIF [ctrl+c to stop]
capabilities

Everything runs locally.
You own every byte.

MCP server exposing 32 tools to any AI client. 11 specialist agents wrap 191 security tools, share recon context, and stamp every finding with a CVSS v3.1 score and a validated PoC.

tools_per_engagement
0

Security tooling, wrapped

nmap · nuclei · ffuf · sqlmap · trivy · kube-hunter · BloodHound · impacket · and 183 more, all via one MCP endpoint.

web
41
network
30
binary
27
osint
24
cloud
22
password
14
11_specialists

Agents with context sharing

Each agent streams findings to the shared engagement graph. No duplicate work, no lost signal.

recon-agent scanning subdomains
web-hunter testing 47 endpoints
cloud-agent enumerating IAM
ad-attacker mapping ACLs
exploit-chainer building paths
poc-validator confirming findings
chain_engine
0

Templates

Six battle-tested attack paths, rotating live:

webcloud
ADDA
K8slateral
supplyprod
CIprod
bountypwn
proof_of_concept

Every finding ships a safe PoC

Non-destructive reproducers, captured HAR, screenshot, request/response trace. False positives get filtered before your report.

[poc] blind SQLi confirmed · time-based · sleep(5) verified 3×
[poc] safe bucket listing · no mutation, 12 objects enumerated
[poc] XSS fires in sandbox · screenshot captured → evidence/f-014.png
ci_cd_native

Ships SARIF + JUnit + PDF

Drop pentest-ai into GitHub Actions. Breaks the build on severity gate. Posts findings as PR comments.

pentest-ai[bot] commented feat/payments-api · #482
critJWT alg=none accepted on /api/v2/admin
critIDOR on /api/v2/users/{id}/billing
poc2 findings validated, 0 false positives
severity gate: failed · build blocked · sarif.json uploaded
detection_output

Blue-team ready

Auto-generates Sigma, Splunk SPL, and KQL for every offensive technique used during the engagement.

Sigma
Splunk SPL
KQL

        
llm_red_team

OWASP LLM Top 10

Prompt injection, training-data leakage, insecure output, model DoS, covered as first-class assessment targets.

LLM01 · Prompt injection
LLM02 · Insecure output
LLM03 · Training data leak
LLM04 · Model DoS
LLM05 · Supply chain
LLM06 · Sensitive info
LLM07 · Insecure plugins
LLM08 · Excessive agency
LLM09 · Overreliance
LLM10 · Model theft
data_sovereignty

Local-first, zero telemetry

Your engagement never leaves your machine. MIT licensed. Self-hosted. Deterministic.

outbound0 B
telemetrydisabled
storagelocal only
licenseMIT
pricing

Three ways to use pentest-ai

Free OSS for individuals. Enterprise dashboard for teams. Managed Assessment delivered.

no per-seat no per-scan no hidden fees cancel anytime
open_source
for builders & CI
The CLI
Free · forever
Full CLI + MCP server, no auth, no limits
  • 191 security tools
  • 11 specialist agents
  • Autonomous exploit chaining
  • PoC validation
  • CVSS v3.1 + MITRE ATT&CK mapping
  • SARIF + JUnit + PDF reports
  • CI/CD pipeline mode
  • Checkpoint + resume
  • MIT license
view on github view on pypi
managed · limited
one-time engagements
Full assessment
$9,500 · one-time
typical human pentest: $15k–$30k
Full pentest engagement, delivered for you
capacity5 / quarter
by application · ~3-week turnaround
includes 3 months Enterprise ($1,497 value)
  • Complete autonomous pentest
  • Pre-engagement scoping
  • Exploit chain validation + PoCs
  • Executive + technical reports
  • Compliance framework mapping
  • Remediation priorities
  • 30-min findings walkthrough
  • 90-day retest window
  • Dedicated Slack channel
book assessment or book a 15-min scoping call
faq 12 questions

Common questions

Yes. All 191 tools, 11 agents, 32 MCP tools, exploit chaining, PoC validation, CVSS, SARIF + JUnit + PDF export, CI/CD mode, compliance mapping, Sigma/SPL/KQL generation, checkpoint-resume. Free under MIT. Enterprise ($499/mo) is a separate hosted dashboard for teams that want SSO, shared workspaces, and scheduling.
We take isolated low-severity findings and connect them into multi-step attack paths. Info disclosure + weak permission + credential reuse = Domain Admin. Each step requires your approval. Six templates cover web, AD, cloud, containers, supply chain, and API attacks.
Scanners output a flat list of isolated findings. pentest-ai connects low-severity items into multi-step chains (SSRF → IMDS → cluster-admin), scores the chain end-to-end, and validates each step with a safe PoC. You get one real attack path instead of 47 tickets to triage.
Yes, within the program's scope and rules. Use --scope to constrain targets. The bug-bounty preset auto-prunes out-of-scope hosts, disables destructive modules, and formats findings to match most program templates.
No. Every risky command runs in human-in-the-loop mode. You see the full command and approve or deny before execution. Set --auto at your own risk for sandboxed targets.
The CLI runs on your machine. Scans, findings, evidence, and reports stay local unless you upload them yourself. The only exception is the LLM call: prompts go to whatever model backend you choose. Use a local model via Ollama to keep everything offline. Enterprise dashboard sync is opt-in per workspace.
You need a model backend. Options: Claude, OpenAI, or a local model via Ollama (Llama 3, Mistral, Qwen). The tools themselves are free. Running with Ollama means zero cloud calls and no API key required. Set the backend with PTAI_MODEL=ollama/llama3 or similar.
Every finding ships with the exact request and response, a screenshot or captured payload where applicable, CWE + CVSS scoring, and for chains, the PoC script that validated each step. Evidence is hashed and stored alongside the finding so you can replay it or attach it to a report.
A full pentest engagement delivered at $9,500 one-time. Scoping, autonomous pentest, exploit-chain validation with PoCs, executive + technical reports, 30-minute findings walkthrough, 90-day retest, 3 months of Enterprise. Capacity is 5 engagements per quarter, by application.
The OSS CLI is the trial. Same engine, same tools, same chaining, runs locally for free. Enterprise adds shared workspaces, SSO, scheduling, API access, and the dashboard. If you want a 30-min walkthrough before committing, email sales and we'll set one up.
Linux and macOS are first-class. Windows works via WSL2. Install with pip install ptai, pipx install ptai, or uvx ptai. Python 3.10 or newer.
GitHub Issues for bugs and feature requests. Email [email protected] for Enterprise and Managed customers. Enterprise includes a dedicated Slack channel and a 24h response SLA during business hours.
built by pentesters, for pentesters

Start finding what scanners miss.

Open source. Run it locally. Own your data.

1install
$ pip install ptai
copied
python 3.10+ · macOS, linux, WSL2
2run
$ ptai start your-target.com
pick scope · pick agent · approve each step
3ship
↗ SARIF + JUnit + PDF
fail the build · attach to jira · brief the exec
MIT licensed SARIF 2.1.0 CVSS v3.1 MITRE ATT&CK No telemetry