Open-Source Security · xalgord

xalgorix

Xalgorix is a self-hosted AI-powered penetration testing platform that automates security scanning using large language models (LLMs) you control. It combines autonomous vulnerability discovery with independent verification of findings, producing exploit-verified reports with CVSS scores and proof-of-concept evidence.

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Source: GitHub — github.com/xalgord/xalgorix

693

GitHub stars

121

Forks

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	xalgord/xalgorix
Owner	xalgord
Primary language	Go
License	MIT — OSI-approved
Stars	693
Forks	121
Open issues	1
Latest release	v4.5.48 (2026-07-08)
Last updated	2026-07-08
Source	https://github.com/xalgord/xalgorix

What xalgorix is

Built in Go and TypeScript, Xalgorix implements a 22-phase testing methodology driven by an LLM agent (supporting OpenAI, Anthropic, DeepSeek, Gemini, Groq, Ollama, MiniMax), browser automation for runtime testing, terminal tooling integration, WebSocket telemetry, and independent finding verification before reporting. No scan data leaves the machine; all processing remains on-premises.

Quickstart

Get the xalgorix source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/xalgord/xalgorix.git2cd xalgorix3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Bug Bounty and Vulnerability Research

Point the agent at an in-scope target; it systematically enumerates the attack surface, tests business logic and authentication flows, and surfaces exploit-verified findings with CVSS scores and PoC evidence—reducing triage overhead versus template-based scanners.

Authorized Penetration Testing

Execute the full 22-phase methodology against systems you own or have explicit permission to test. The autonomous agent handles SSRF, IDOR, auth bypass, race conditions, and injection testing; independent verification ensures findings are proven before reporting.

Continuous Security Testing & Compliance Reporting

Deploy as a system service with scheduled scans; stream findings to Discord or Telegram, and auto-generate branded PDF reports with remediation guidance. Useful for ongoing security posture monitoring and audit-ready documentation.

Implementation considerations

Requires Go 1.24+ and Make; Linux platform officially stated. Windows/macOS support unclear. Plan for build and deployment on target infrastructure.
Bring-your-own-LLM model: choose provider (OpenAI, Anthropic, DeepSeek, etc.), model variant, and reasoning effort level; document and rotate API keys securely via environment variables.
Configure data residency carefully; while no data leaves the machine by default, proxy settings and LLM provider selection affect compliance scope. Review for GDPR, HIPAA, or other regulatory constraints.
The 22-phase methodology and autonomous agent behavior are opaque from outside the README; review published docs or run a test scan to understand scan duration, target load, and false-positive rates before production use.
Integrations mentioned (AgentMail, Discord, Telegram, Caido proxy) require additional setup; validate compatibility with your incident response and reporting workflows.

When to avoid it — and what to weigh

No LLM API Access or Budget for Inference — Xalgorix requires a working LLM provider (OpenAI, Anthropic, etc.) and associated API costs. If you lack an LLM provider account or cost control is critical, consider free signature-based alternatives (Nuclei, OWASP ZAP).
Targeting Systems Without Explicit Authorization — The README explicitly warns: use only on systems you own or have written permission to test. Unauthorized scanning violates laws in most jurisdictions and defeats the tool's design intent.
Need for Offline or Air-Gapped Scanning — The tool requires real-time calls to external LLM APIs. If your target environment is air-gapped or offline, you would need to run a local LLM (Ollama support mentioned) with sufficient local compute—adding significant overhead.
Legacy or Non-HTTP Protocols — Xalgorix is tailored for web application security testing. If your primary targets are firmware, network protocols, binary services, or non-HTTP APIs, this tool offers limited value.

License & commercial use

MIT License. Permissive OSI-approved license permitting commercial use, modification, and distribution provided the license and copyright notice are included.

MIT license permits commercial use without restriction. However, commercial deployment should account for: (1) LLM provider API costs, which scale with scan frequency and model choice; (2) potential liability for scan impact (DoS, data exposure) if misconfigured; (3) responsibility to use only on authorized targets. Recommend legal review before offering as a managed service or integrating into SaaS platforms.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Xalgorix runs penetration tests, which are inherently invasive. Operational security depends on: (1) restricting use to systems with explicit written authorization; (2) secure storage of LLM API keys and target credentials in environment variables; (2) network segmentation to prevent unintended scanning of production systems; (4) audit logging of scan results and findings (unclear if built-in or requires external SIEM); (5) review of proxy configuration (Caido integration) to prevent credential/finding exfiltration. Tool does not impose rate limiting or scan fingerprinting obfuscation. No mention of encryption for data at rest or in transit to the LLM provider.

Alternatives to consider

OWASP ZAP / Burp Suite Community

Mature, free, spider-based DAST scanners with passive and active rule engines. No LLM cost, strong community. Limited on business logic and false-positive load; requires manual payload tuning.

Nuclei (ProjectDiscovery)

Signature and template-driven vulnerability scanner. Fast, free, highly customizable. Excels at known vulnerability classes; lacks reasoning and exploit verification. No autonomous agent behavior.

Commercial DAST (Acunetix, Checkmarx, Rapid7)

Cloud or on-premises platforms with built-in heuristics, incident management, and compliance reporting. Higher cost; less control over LLM or methodologies. Suitable for enterprise compliance-heavy environments.

Software development agency

Build on xalgorix with DEV.co software developers

Deploy Xalgorix on your infrastructure for autonomous, AI-driven penetration testing with verified findings. Start with the free open-source version or explore the managed cloud option at www.xalgorix.com.

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xalgorix FAQ

Do I need an OpenAI account to use Xalgorix?

No. Xalgorix supports multiple LLM providers: OpenAI, Anthropic, DeepSeek, Gemini, Groq, Ollama, and MiniMax. You choose and configure your preferred provider via environment variables. Local LLM (Ollama) is an option to avoid external API calls.

Can I scan targets on the public internet, or only internal systems?

You can scan any target you own or have explicit written authorization to test. The README emphasizes only using Xalgorix on authorized systems. Unauthorized scanning violates computer fraud laws in most jurisdictions.

How long does a typical scan take?

Not clearly stated in the README. The tool runs a 22-phase methodology with LLM reasoning and browser automation, so scan duration likely depends on target complexity, LLM provider latency, and configured reasoning effort. Run a test scan to establish baseline timing.

Can I integrate Xalgorix findings into my SIEM or ticketing system?

Xalgorix exports branded PDF reports and can send findings to Discord or Telegram. Direct SIEM/ticketing integrations (Splunk, Jira, ServiceNow) are not mentioned in the README. Custom scripting or webhook forwarding may be required.

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