Security Repro logo

Security Repro

OrganizationPopular
Leanmcp
security-repro

Build safe, local, defensive reproductions of ALREADY-PATCHED, publicly disclosed security vulnerabilities (CVEs, vendor advisories, AI-agent incidents) for whitepapers, control testing, and education. Use this skill whenever the user wants to reproduce, demonstrate, or write up a known vulnerability or security incident — phrases like "reproduce this CVE", "build a repro for", "demonstrate the EchoLeak/Cursor/postmark-mcp issue", "show how this exploit worked", "add a reproducibility guide", or "pull the NVD/CVE details for". It fetches advisory metadata from NVD and CVE.org, confirms the issue is fixed, and produces a vulnerable-vs-patched harness that is offline, loopback-only, and uses synthetic markers. It NEVER discovers, develops, or weaponizes new zero-days and refuses any unpatched or live-target request.

Overview

PublisherLeanmcp
Repositorygateway-skills
Skill namesecurity-repro
Stars
2.1K
Forks
2.1K
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Leanmcp on GitHub. Read the source before you install it.

Installation

Install the Security Repro AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Leanmcp/gateway-skills/tree/main/skills/security-repro
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Leanmcp/gateway-skills.git /tmp/gateway-skills
mkdir -p .claude/skills
cp -r /tmp/gateway-skills/skills/security-repro .claude/skills/security-repro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Repro in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Security Repro on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Security Repro is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

security-repro

Reproduce already-fixed, publicly disclosed vulnerabilities so a reader can see the failure mode and confirm the vendor's patch closes it. The audience is a whitepaper or an internal control test. The output is a contrast: the vulnerable behavior fires, the patched behavior refuses, and nothing real is touched.

The one hard line (read first)

This skill exists to reproduce the past, not to find the new. Everything below flows from that.

  • Only patched or withdrawn issues. If the vendor has not shipped a fix (or pulled the artifact), stop. Do not proceed on an open/unpatched issue, even a disclosed one.
  • Use the last known-vulnerable public version that is already on the internet — the release before the fix. You are demonstrating something that already exists publicly, not creating it.
  • Never develop new exploitation. No fuzzing for new bugs, no chaining, no variant discovery, no bypassing an unfixed control. If reproducing the disclosed mechanism faithfully would require inventing an undisclosed step (e.g. an escape whose mechanism was never published), leave that step out of scope and say so in writing.
  • Offline, loopback, synthetic. No third-party host, no real credential, no real target, no real recipient, no persistence, no exfiltration. A successful reproduction means a local marker or a policy decision is observed, not that code ran on someone's machine or data left the lab.

If a request pushes past this line — reproduce an unpatched bug, target real infrastructure, weaponize a payload, evade detection — decline that part in one sentence, explain why (it would be new zero-day / offensive work), and offer the safe version instead. This protects the user legally; it is the whole point.

Workflow

1. Establish that the issue is public AND patched

Before writing any code, pin down three facts and record them in the guide:

  1. Disclosure — a primary source: vendor advisory, MSRC/GitHub/GitLab advisory, or the researcher's write-up.
  2. Fix — the version or commit that patched it, or confirmation the artifact was withdrawn. This is the gate. No fix, no repro.
  3. Vulnerable version — the last public release before the fix.

Use the helper scripts to pull machine-readable metadata:

bash
# NVD record (CVSS, references, CWE, published/modified dates)
python3 scripts/fetch_nvd.py CVE-2025-32711

# CVE.org record (CNA description, affected versions, references)
python3 scripts/fetch_cve.py CVE-2025-32711

# Pretty one-line "is this patched?" summary across both sources
python3 scripts/check_patched.py CVE-2025-32711

These call only the public NVD and CVE.org REST APIs (read-only). They do not touch any victim system. If the CVE has no Fixed/patched reference and no withdrawal note, treat the patched status as unconfirmed and tell the user to verify against the vendor before you build anything.

2. Model the mechanism minimally, not the whole kill chain

Reduce the disclosure to the single control failure worth showing, and replace every dangerous verb with an inert marker:

Real elementReproduce it as
shell / exec / RCEa function that writes MARKER.txt with a fixed string
credential / secreta decoy file like DECOY_SECRET.txt = not-a-real-secret
exfiltration endpointan in-process or 127.0.0.1 loopback sink that just logs
email recipientrecipient@example.invalid
write outside sandboxa path calculation that is asserted, never performed
a real vulnerable dependencypin the real last-vulnerable version only if a
primary source confirms that exact version; otherwise model the disclosed
mechanism in two local loaders (vulnerable_* / patched_*) and say so

Cut the parts whose mechanism was never disclosed (mark them "explicitly out of scope"). Reconstructing an undisclosed step is new zero-day work — forbidden.

3. Build the vulnerable-vs-patched harness

Follow the house pattern already in Reproducibility/ in the whitepaper repo:

  • A single self-contained harness (stdlib-only Python preferred; Docker with network_mode: none when isolation must be enforced).
  • Two code paths: vulnerable (the marker fires) and patched (the same input is refused with a decision like block / review_required).
  • Deterministic output, a saved results/run.json (or transcript), and a non-zero exit if the contrast does not hold.
  • Log the gateway-relevant fields: source, data_class, outbound_channel, decision, policy_id, and a short reason.

See references/harness-pattern.md for the concrete file layout, the safety checklist, and a copyable harness skeleton.

4. Write the guide

Produce a short markdown guide next to the harness with these sections: Status and scope (name the CVE, the fix version, and what is explicitly out of scope), Lab design / Reproduction protocol, Defensive assertion (what control would have caught it), Safety limits, and Sources (primary links only). Match the tone and structure of the existing numbered guides.

5. Hand back run commands

Per this user's standing rule, do not execute reproduction harnesses yourself — write them, then give the exact command to run. State clearly what a pass looks like (which marker appears, which decision is logged).

Scripts

  • scripts/fetch_nvd.py CVE-ID — NVD 2.0 API record as JSON (CVSS, refs, CWE).
  • scripts/fetch_cve.py CVE-ID — CVE.org CVE Record (CNA data, affected versions).
  • scripts/check_patched.py CVE-ID — combines both and prints whether a fixed version / patch reference is present, so the "is it patched?" gate is fast.

All three are read-only, hit only public vulnerability databases, and take a single CVE ID argument.

References

  • references/harness-pattern.md — file layout, safety checklist, and a stdlib-only harness skeleton to copy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Security Repro AI skill do?

Build safe, local, defensive reproductions of ALREADY-PATCHED, publicly disclosed security vulnerabilities (CVEs, vendor advisories, AI-agent incidents) for whitepapers, control testing, and education. Use this skill whenever the user wants to reproduce, demonstrate, or write up a known vulnerability or security incident — phrases like "reproduce this CVE", "build a repro for", "demonstrate the EchoLeak/Cursor/postmark-mcp issue", "show how this exploit worked", "add a reproducibility guide", or "pull the NVD/CVE details for". It fetches advisory metadata from NVD and CVE.org, confirms the...

Why use Security Repro on TypingMind?

Because you install it once and use it with any model. Security Repro is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Security Repro in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Leanmcp/gateway-skills/tree/main/skills/security-repro. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Security Repro?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Security Repro?

As many as you like. As long as a model supports skills, you can use Security Repro with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Security Repro AI skill free?

Yes. It is published on GitHub by Leanmcp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇