AI-Driven Security Hardening for Any Codebase
Lagune helps your AI agent harden any codebase: it detects what your system actually does, then drives the defensive work that fits as you build.
翻譯準備中
Agentic AI security scanner that hunts exploitable vulnerabilities like an adversary, proves them with executable PoCs, and fixes them test-first. A maintained fork of Capital One's VulnHunter, rebuil
翻譯準備中
安全Python · ShellGitHub @nealbridges自行安裝營運者未確認
試用過了嗎? 點一下就好,不需要登入。
尚未偵測到更新。GitHub 發布版本後會顯示在這裡。
Agentic AI security scanner that hunts exploitable vulnerabilities like an adversary, proves them with executable PoCs, and fixes them test-first. A maintained fork of Capital One's VulnHunter, rebuilt for any agent harness.
翻譯準備中
正在載入完整 README。
檢視完整 README ↗[!NOTE] A maintained fork of Capital One's VulnHunter (Apache-2.0) — built to run on any agent harness, not just Claude Code. This fork's focus: harness portability, sandboxed (containerized) exploit validation, and measured-impact PoCs. See Why the changes · What this fork changes · The numbers.
From pattern-matching to provability.
VulnHunter is an open-source, agentic AI security tool that applies proactive, attacker-first analysis directly to source code.
Unlike traditional, passive SAST scanners that flag suspicious patterns and often cause false positives, VulnHunter reasons like an adversary. It identifies which defects are actually exploitable, maps prospective attack paths, and proposes targeted, evidence-backed fixes.
Modern software supply chains are deeply interconnected. A single vulnerability in a widely-used open-source component can ripple across thousands of enterprises simultaneously.
VulnHunter was developed internally at Capital One and open-sourced for the community. This fork carries that work forward — same methodology, reworked to run on any agent harness, with sandboxed (containerized) exploit validation and measured-impact PoCs as the roadmap. See What this fork changes.
Dual-use caution VulnHunter performs dual-use cybersecurity work (vulnerability discovery and exploitation). Expect guardrails: most commercially available models apply dual-use cyber safeguards, and aggressive exploitation behavior can trip rate limits or usage flags. VulnHunter's development and testing ran on open-weight, community-provided models — de-risked, abliterated, and uncensored — which are the models likely to matter for organizational use going forward. Audit only code you own or are otherwise authorized to audit.
[!IMPORTANT] Prerequisites & Model Requirements VulnHunter's methodology is built to run on open-weight, community-provided models — the de-risked, abliterated, uncensored ones organizations can actually deploy. A capable reasoning model is required; the strongest model your harness offers gives the best results, but the methodology does not depend on a specific vendor's frontier model. You supply your own model access.
| Capability | Upstream (Capital One) | This fork | Status |
|---|---|---|---|
| Harness portability | Skills invoke Claude Code specifically; installer targets ~/.claude/skills; model gates hardcode Opus; harness pins claude-opus-4-8 | Skills are harness-portable prompt files (any harness with a skills directory + subagents); VULNHUNT_SKILLS_DIR / VULNHUNT_AGENTS_DIR / VULNHUNT_BIN_DIR / VULNHUNT_HOST_CMD / VULNHUNT_MODEL environment contract; model gates rephrased to "your harness's most capable reasoning model" | Shipped |
| No-guess installer | install.sh assumes ~/.claude/skills | Explicit directories, honors GROK_HOME semantics, writes the vh launcher to VULNHUNT_BIN_DIR/~/.local/bin, installs the vulnhunter-run skill + agent definition; Windows .cmd equivalents updated | Shipped |
vulnhunter-run operator skill | — (absent) | Unattended operator: clone → hunt → find-results → write/validate the scan manifest, with explicit stop rules and no improvisation | Shipped |
| Benchmark/judge hardening | Fixed model + basic retry | Model via environment, retry/backoff configuration, analyze_misses pipeline loss-point tracing, per-finding history tracking | Shipped |
| Harness-neutral report language | Claude-specific prose throughout the skills | Harness-neutral tool language (Agent → subagent, Claude CLI → harness session) | Shipped |
| Sandbox-first exploit validation | Exploit tests may be static traces; runtime choice ad hoc |
VulnHunter's methodology is host-agnostic by nature: it is prompt procedure, not tool binding. The upstream project grew up inside Claude Code — a coherent choice, and the right first home. But the agent-harness landscape has broadened, and a security methodology that installs into only one of them stops being an audit capability and starts being a vendor feature. This fork makes four changes, each with a reason.
Every skill here is a portable prompt file with an explicit environment contract (VULNHUNT_SKILLS_DIR, VULNHUNT_AGENTS_DIR, VULNHUNT_MODEL, VULNHUNT_HOST_CMD), and the model gates now ask for your harness's most capable reasoning model instead of a specific product. Better means: the same methodology installs into whatever harness your team already runs — and becomes comparable across harnesses in benchmark runs, which is how this fork is developed.
The upstream installer copied skills into ~/.claude/skills unconditionally. On a machine running two harnesses — or a harness with a relocated home — that guess installs into the wrong place, silently. The fork's installer asks, or takes environment variables, and fails loudly with the exact instruction when the answer is missing. Better means: safe on multi-harness machines, correct under relocated homes, loud instead of silent when misconfigured.
以上為 README 的開頭部分。其餘內容請閱讀原文。 檢視完整 README ↗
| Docker-first runtime provisioning; the runtime recorded per finding; Medium+ severity must execute |
| In progress |
| Measured-impact PoCs | PoCs are documents; impact asserted | Executable PoC + impact number in the finding (rows exposed, requests amplified, key-hours stranded) | In progress |