Vulnerability Validation logo

Vulnerability Validation

Organization
Factory-AI
vulnerability-validation

Validate security findings from commit-security-scan by assessing exploitability, filtering false positives, and generating proof-of-concept exploits. Use after running commit-security-scan to confirm vulnerabilities.

Overview

PublisherFactory-AI
Repositoryfactory-plugins
Skill namevulnerability-validation
Stars
111
Forks
15
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Factory-AI on GitHub. Read the source before you install it.

Installation

Install the Vulnerability Validation 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.
  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/Factory-AI/factory-plugins.git /tmp/factory-plugins
mkdir -p .claude/skills
cp -r /tmp/factory-plugins/plugins/security-engineer/skills/vulnerability-validation .claude/skills/vulnerability-validation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vulnerability Validation 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 Vulnerability Validation 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 Vulnerability Validation 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.

Vulnerability Validation

Validate security findings by assessing whether they are actually exploitable in the context of this codebase. This skill filters false positives, confirms real vulnerabilities, and generates proof-of-concept exploits.

When to Use This Skill

  • After commit-security-scan - Validate findings before creating issues or blocking PRs
  • HIGH/CRITICAL findings - Prioritize validation of severe findings
  • Before patching - Confirm vulnerability is real before investing in fixes
  • Security review - Deep-dive validation of specific findings

Prerequisites

  • .factory/threat-model.md must exist (from threat-model-generation skill)
  • security-findings.json must exist (from commit-security-scan skill)

Inputs

InputDescriptionRequiredDefault
Findings filePath to security-findings.jsonYessecurity-findings.json
Threat modelPath to threat modelNo.factory/threat-model.md
Finding IDsSpecific findings to validate (comma-separated)NoAll findings
Severity filterOnly validate findings at or above this severityNoAll severities

Instructions

Follow these steps for each finding to validate:

Step 1: Load Context

  1. Read security-findings.json from commit-security-scan
  2. Read .factory/threat-model.md for system context
  3. Identify which findings to validate based on inputs

Step 2: Reachability Analysis

For each finding, determine if the vulnerable code is reachable:

  1. Trace entry points

    • Can external users reach this code path?
    • What HTTP endpoints, CLI commands, or event handlers lead here?
    • Is authentication required to reach this code?
  2. Map the call chain

    • Starting from the entry point, trace the path to the vulnerable code
    • Document each function call in the chain
    • Note any branching conditions that must be satisfied
  3. Classify reachability

    • EXTERNAL - Reachable from unauthenticated external input
    • AUTHENTICATED - Requires valid user session
    • INTERNAL - Only reachable from internal services
    • UNREACHABLE - Dead code or blocked by conditions

Step 3: Control Flow Analysis

Determine if an attacker can control the vulnerable input:

  1. Identify the source

    • Where does the tainted data originate?
    • HTTP parameter, file upload, database query, environment variable?
  2. Trace data flow

    • Follow the data from source to sink (vulnerable function)
    • Document each transformation or validation step
    • Note any sanitization, encoding, or type conversion
  3. Assess attacker control

    • Can the attacker fully control the input?
    • Are there length limits, character restrictions, or format validation?
    • Does the data pass through any sanitization?

Step 4: Mitigation Assessment

Check if existing security controls prevent exploitation:

  1. Input validation

    • Is the input validated before reaching the vulnerable code?
    • What validation rules are applied?
  2. Framework protections

    • Does the framework provide automatic protection? (e.g., ORM parameterization, React XSS escaping)
    • Is the protection enabled and properly configured?
  3. Security middleware

    • Are there WAF rules, rate limiting, or other controls?
    • Do CSP headers or other browser protections apply?
  4. Reference threat model

    • Check the "Existing Mitigations" section for this threat type
    • Verify mitigations are actually in place

Step 5: Exploitability Assessment

Determine how difficult it is to exploit:

RatingCriteria
EASYNo special conditions, standard tools, publicly known technique
MEDIUMRequires specific conditions, timing, or chained vulnerabilities
HARDRequires insider knowledge, rare conditions, or advanced techniques
NOT_EXPLOITABLETheoretical vulnerability but not practically exploitable

Consider:

  • Attack complexity
  • Required privileges
  • User interaction needed
  • Scope of impact

Step 6: Generate Proof-of-Concept

For confirmed vulnerabilities, create a proof-of-concept:

  1. Craft exploit payload

    • Create a minimal payload that demonstrates the vulnerability
    • Use benign payloads (no actual damage)
  2. Document the request

    • HTTP method, URL, headers, body
    • Or CLI command, file input, etc.
  3. Describe expected vs actual behavior

    • What should happen (secure behavior)
    • What actually happens (vulnerable behavior)

Example PoC structure:

json
{
  "payload": "' OR '1'='1",
  "request": "GET /api/users?search=' OR '1'='1",
  "expected_behavior": "Returns users matching search term",
  "actual_behavior": "Returns all users due to SQL injection"
}

Step 7: Calculate CVSS Score

Assign a CVSS 3.1 score based on:

MetricOptions
Attack Vector (AV)Network (N), Adjacent (A), Local (L), Physical (P)
Attack Complexity (AC)Low (L), High (H)
Privileges Required (PR)None (N), Low (L), High (H)
User Interaction (UI)None (N), Required (R)
Scope (S)Unchanged (U), Changed (C)
Confidentiality (C)None (N), Low (L), High (H)
Integrity (I)None (N), Low (L), High (H)
Availability (A)None (N), Low (L), High (H)

Example: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N = 9.1 (Critical)

Step 8: Classify Finding

Based on analysis, classify each finding:

StatusMeaning
CONFIRMEDVulnerability is real and exploitable
LIKELYProbably exploitable but couldn't fully verify
FALSE_POSITIVENot actually a vulnerability (document why)
NEEDS_MANUAL_REVIEWRequires human security expert review

Step 9: Generate Output

Create validated-findings.json:

json
{
  "validation_id": "val-<timestamp>",
  "validation_date": "<ISO timestamp>",
  "scan_id": "<from security-findings.json>",
  "threat_model_version": "<from threat-model.md>",
  "validated_findings": [
    {
      "id": "VULN-001",
      "status": "CONFIRMED",
      "original_severity": "HIGH",
      "validated_severity": "HIGH",
      "exploitability": "EASY",
      "reachability": "EXTERNAL",
      "existing_mitigations": [],
      "exploitation_path": [
        "User submits search query via GET /api/users?search=<payload>",
        "Express router passes query to searchUsers() handler",
        "Handler passes unsanitized input to SQL template literal",
        "PostgreSQL executes malicious SQL"
      ],
      "proof_of_concept": {
        "payload": "' OR '1'='1",
        "request": "GET /api/users?search=' OR '1'='1",
        "expected_behavior": "Returns users matching search term",
        "actual_behavior": "Returns all users due to SQL injection"
      },
      "cvss_vector": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N",
      "cvss_score": 9.1,
      "validation_notes": "Confirmed via code tracing. No input validation or parameterization."
    }
  ],
  "false_positives": [
    {
      "id": "VULN-003",
      "original_severity": "MEDIUM",
      "reason": "Input is validated by Joi schema in middleware before reaching this code. Schema enforces UUID format which prevents injection.",
      "evidence": "See src/middleware/validation.js:45 - Joi.string().uuid()"
    }
  ],
  "needs_manual_review": [
    {
      "id": "VULN-005",
      "original_severity": "HIGH",
      "reason": "Complex data flow through message queue. Unable to fully trace if sanitization occurs in consumer service."
    }
  ],
  "summary": {
    "total_analyzed": 10,
    "confirmed": 5,
    "likely": 2,
    "false_positives": 2,
    "needs_manual_review": 1,
    "by_severity": {
      "CRITICAL": 1,
      "HIGH": 3,
      "MEDIUM": 1,
      "LOW": 0
    }
  }
}

Success Criteria

The skill is complete when:

  • All specified findings have been analyzed
  • Each finding has a status (CONFIRMED, LIKELY, FALSE_POSITIVE, NEEDS_MANUAL_REVIEW)
  • Confirmed findings have exploitation paths documented
  • Confirmed findings have proof-of-concept exploits
  • False positives have clear reasoning
  • validated-findings.json is valid JSON
  • CVSS scores are calculated for confirmed findings

Verification

Run these checks before completing:

bash
# Verify output exists and is valid JSON
cat validated-findings.json | jq . > /dev/null && echo "✓ Valid JSON"

# Check all findings have status
jq '.validated_findings | all(.status)' validated-findings.json

# Check confirmed findings have PoC
jq '.validated_findings | map(select(.status == "CONFIRMED")) | all(.proof_of_concept)' validated-findings.json

# Check false positives have reasoning
jq '.false_positives | all(.reason)' validated-findings.json

Example Invocations

Validate all findings:

Validate the security findings from the last scan.

Validate specific findings:

Validate findings VULN-001 and VULN-002 from security-findings.json.

Validate only HIGH/CRITICAL:

Validate all HIGH and CRITICAL severity findings from the security scan.

Validate with specific files:

Validate findings in security-findings.json using threat model at .factory/threat-model.md.

References

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 Vulnerability Validation AI skill do?

Validate security findings from commit-security-scan by assessing exploitability, filtering false positives, and generating proof-of-concept exploits. Use after running commit-security-scan to confirm vulnerabilities.

Why use Vulnerability Validation on TypingMind?

Because you install it once and use it with any model. Vulnerability Validation 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 Vulnerability Validation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Factory-AI/factory-plugins/tree/master/plugins/security-engineer/skills/vulnerability-validation. 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 Vulnerability Validation?

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 Vulnerability Validation?

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

Is the Vulnerability Validation AI skill free?

It is published on GitHub by Factory-AI. Check the repository for licensing terms. 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 👇