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AI-generated · Automatically published by LinkLoot. Vigil keeps offensive security engagements alive across restarts, verifies findings against captured evidence, and exports submission-ready reports. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it does Vigil is an AI harness for long-running offensive security work: web app pentests, bug bounty hunting, white-box code audits, or both against one target. A lean operator agent keeps the engagement on track while specialist agents recon, hunt, and exploit inside a full Kali runtime. Findings only count when a second agent confirms them against captured evidence, and attack chains link individual findings into end-to-end exploitation paths. Every command, request, probe, and finding lands in a durable database, so work survives context limits and restarts. The tooling stack includes ProjectDiscovery recon, fuzzers, an audited Playwright browser, MITM capture with multiple identities for cross-account testing, and out-of-band callbacks correlated to the probe that caused them. White-box audits use a sealed, content-addressed source snapshot with a tree-sitter structural index. Who it helps Security researchers running multi-day engagements who need state persistence beyond a single LLM context window. Bug bounty hunters submitting to Bugcrowd or HackerOne get hash-verified export formats. Code auditors working white-box get coverage ledgers showing what was tested and what was not. Getting started Requires Python 3.11+. The project supports Claude Code and Codex with full headless dispatch, session commands, and compaction hooks. goose, opencode, and Kimi lack some features; opencode and Kimi run specialists inline. Target-facing commands run through vigil assess kali, which records each command's argv, agent, phase, technique, intent, probe kind, exit status, and output. Limits and costs Authorized use only. Vigil autonomously drives offensive tooling against live systems. Use it only with explicit authorization and a reviewed scope. It is provided without warranty under Apache License 2.0; you are responsible for commands, credentials, infrastructure, and target effects. Hosting a Kali container and running long engagements incurs compute costs. No pricing is documented. Dispatches carry surface, scope, and rules of engagement but never pre-named bugs or checklists—specialists choose what to probe within the authorized surface. Source links Official repository Project documentation Apache License 2.0
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A self-hosted security research workbench that turns Codex or Claude Code agents into focused, de-duplicated vulnerability scans. open-kritt is a self-hosted security research platform for developers and bug-bounty teams that want AI agents to inspect code in smaller, repeatable workflows instead of asking one model to audit an entire repository at once. It lets you build prompt-based scan workflows, run them with Codex or Claude Code, validate findings with post-scripts, rank severity, and de-duplicate results before review. Why it is useful: It is aimed at practical code-security work: scoped scans, reusable workflows, findings, ranking, and proof-building rather than generic chat. It supports model access through Codex, OpenAI, Anthropic, or OpenRouter, so teams can choose the provider path that fits their setup. The project is open source under AGPL-3.0 and includes a local Docker Compose stack plus a repository-local ./kritt CLI. Use it carefully. The project documentation states that tool-enabled scan agents run as root inside disposable containers with writable repository copies and direct internet access. The backend is unauthenticated by default. That makes open-kritt best suited for a dedicated VM or isolated Docker host, with scoped credentials and network controls, especially when scanning untrusted or private repositories. A good first evaluation path is to read the threat model, clone the repository on an isolated machine, run ./kritt setup, configure one model-access option, and scan a small non-sensitive repository before connecting private code.
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